At the age of 12, he clutched 20 Hong Kong dollars for lunch, only able to choose the cheapest 12-dollar boxed meal, saving the remaining 8 dollars, not even daring to dream of the toys he liked. Watching his classmates choose freely, this poor student hurriedly sought shortcuts to make money—studying over 100 Hong Kong Mark Six lottery numbers, trying to win an 8 million jackpot with just 5 dollars, but only ending up with repeated failures.

This "path of trading alchemy" was fraught with thorns. At 14, he borrowed his brother's account to enter the market with red envelope money, and at 16, due to a love for trading with 10x and 20x leverage, he lost over 40,000 in principal overnight; at 19, he made a comeback, saving 150,000 through tutoring, only to face another total loss due to aggressive trading. After being severely punished by the market twice, he finally realized: the emotional and fervent nature of manual trading ultimately cannot withstand the greed and biases of human nature.

He gave up the fantasy of "getting rich quickly" and instead embraced the power of data, viewing trading as a rigorous scientific experiment. He validated each strategy with historical data, reinvesting effectively and iterating on ineffective ones. Thus, quantitative trading may waste time, but it will not waste money. Ultimately, he achieved an annual income exceeding 100 million through quantitative trading, profiting in both bull and bear markets.

At this point, making money had become just a number. "Traders who make money love trading itself," he began to share and educate, encouraging more people to do what they truly want to do, rather than doing what they don't want to do just for money.

He said, "There are no born trading geniuses, only those who learn and persist." Maintaining rationality without being swayed by emotions, promptly correcting cognitive biases without getting stuck in dead ends, and always keeping a humble mindset to learn—this is the core secret to navigating bull and bear cycles and establishing an independent trading system.

In this episode of OKX's "Dialogue with Traders," OKX Mia (@mia_okx) engages in a deep conversation with quantitative trading champion Calvin Tsai, exploring how he built his quantitative kingdom from the ruins of two total losses, and the pure love and philosophy of trading behind the cold data.

Below is the full dialogue (organized)

1. The Path to Glory | One and a Half Years, from a Million Principal to Over a Hundred Million Annual Income

01 From Traditional Finance to the Crypto Industry: Earning 20 Times in Less than 2 Years

Mia: Hello everyone, I am Mia. Welcome to the seventh episode of OKX's "Dialogue with Traders" series. Here, we will interview well-known traders in the industry to discuss their heartbeats from the first trade, trading strategy logic, and the ups and downs of market cycles. Every trader has their unique story and methodology—some face total losses overnight, while others turn the tide; some insist on manual trading, while others stick to quantitative operations. But their commonality is: amidst market fluctuations, they can still find their own way to victory. Today, we have invited quantitative trading champion Calvin Tsai. He once achieved the highest 3% trading volume of OKX Solana on our OKX exchange and manages a quantitative fund of 160 million. It is a great honor to invite Teacher Tsai; please introduce yourself.

Calvin Tsai: I am very honored to accept this interview and share my trading journey. I used to work in traditional finance, entering a hedge fund after graduation. At that time, we also used quantitative trading methods, mainly trading different stocks, such as A-shares, Hong Kong stocks, US stocks, and some commodities like gold, silver, copper, and crude oil. Around 2020, I discovered that I could transfer traditional strategies to cryptocurrency, such as Bitcoin. I found it easier to make money, so I gradually shifted my time, energy, and funds here. Now, I have been trading cryptocurrencies for about four and a half to five years.

Mia: Calvin is one of our OKX "very VVIP" clients, a true billionaire. When did you first join the crypto industry, and what was your initial capital?

Calvin Tsai: I first bought Bitcoin during the summer of 2017. I remember clearly that a college classmate invited me to dinner and casually asked if I had heard of Bitcoin. At that time, I had never heard of it, so I Googled it when I got home and found it quite interesting. The more I read different articles and discussion forums, the more curious I became, so I decided to buy one Bitcoin first. I transferred money from the bank to a trading platform—there weren't many options back then. My first Bitcoin was purchased at a price of 3,000 dollars. After buying it, I just left it there, and not long after, I saw it rise from 3,000 dollars to 20,000 dollars—that was in December 2017. I really felt like I had "struck it rich overnight." Although I didn't buy much, about several hundred thousand Hong Kong dollars, I thought, "This asset has too much explosive potential; I need to keep observing." Unexpectedly, in 2018, it returned to 3,000 dollars, and I felt like I had just had a dream—rising six times only to return to the starting point. Later, I didn't pay much attention to it and continued my work at the hedge fund. 2018 was a bear market, and 2019 didn't see much increase either; it could be described as a slow bull process. By 2020, I began to notice news reporting on Bitcoin halving—May 2020 was just the halving period. At that time, I started to study seriously, noticing that trading volume was gradually increasing and the market size was expanding. I began to transfer traditional strategies one by one to the crypto market, testing them with historical data.

Mia: So, from the initial small capital to later earning over a hundred million, what was that process like? What was your initial small capital?

Calvin Tsai: The initial small capital was actually not much, around 1 to 2 million Hong Kong dollars. That was from 2020 to 2021. 2021 was arguably the best year for making money. Starting from May or June 2021, I transferred my quantitative strategies to the crypto market, opened a new account for testing and running strategies. Then, from May 2021 to January 2023, my account grew from several million Hong Kong dollars to one hundred million Hong Kong dollars—in about a year and a half, the return was roughly 20 times.

Mia: Earning a hundred million in a year and a half, what about later? Since you entered the circle in 2017, it has been five or six years now; how was your average return in the following years?

Calvin Tsai: The following years were actually similar, with an average profit of about one hundred million each year. Our quantitative strategy has an average annual return rate of over 100%.

02 The Secret to Outperforming Bull and Bear Markets: Trading is More Profitable than Holding Coins

Mia: Okay, but when we first encounter quantitative trading, we might think of it as a low drawdown, stable return method. But you went from a small capital directly to a hundred million; was it all through quantitative trading?

Calvin Tsai: It was all through quantitative trading. Many people think they made money just by holding Bitcoin, but in reality, holding Bitcoin didn't yield such high returns. For example, in 2021, Bitcoin peaked at 60,000 dollars, and now it's at 120,000, which is only a doubling, not a huge increase. Even if you bought at the low of 2021, say over 20,000 or 30,000 dollars, it has only quadrupled. So, simply holding Bitcoin, even with leverage, doesn't earn as much as quantitative trading or trading itself. The key to trading is that—amidst the overall market fluctuations and drawdowns, you can still make money. It's normal to make money holding Bitcoin in a bull market. In a bull market, those who earn more are the ones with higher leverage. But in a bear market, how you avoid drawdowns and prevent being knocked back to square one is crucial. In a bear market or during a correction, do you have a "shorting" strategy that tells you: "At this point, I want to short; I don't want to hold Bitcoin; I want to sell the Bitcoin I have." Trading or quantitative trading allows you to make money in any market condition. For example, we made money in 2022, with a return of about 240%. In a bear market, we can also short and profit during declines. So I think this point is very important—trading definitely earns more than just holding Bitcoin.

2. Trading Philosophy | The Underlying Logic of "Code Printing Money"

01 Core Methodology: Medium to High-Frequency CTA and Risk Management

Mia: No matter the bull or bear market, you are all "printing money"?

Calvin Tsai: We try our best. There are indeed times when we lose money, and strategies can become ineffective. For example, some strategies may not make money for three months or six months in a row. That is the most painful time for us to develop strategies—we have to think, is this strategy ineffective? Should we remove it? Or should we continue running it? Will it one day reach a new high? We need to think and judge whether this strategy can still make money. This is a very, very important point.

Mia: Have you experienced significant drawdowns during this process?

Calvin Tsai: Yes, it’s not that we are always making money. For example, in quantitative trading, a major pain point is judging whether a strategy can continue to make money. Sometimes a strategy may not make money for three months or six months. That is the most critical time for us in quantitative trading, to determine whether the strategy has become ineffective. If it has become ineffective, you need to remove it quickly from the portfolio. But if it can still make money, then you should keep it. So, a very important judgment point in quantitative trading is to think about the logic of a strategy and whether it can still keep you "alive" in this market.

Mia: Do you have a specific case to share with us about your largest drawdown experience?

Calvin Tsai: The direction we take is essentially a CTA strategy. CTA is trend trading, or directional trading. Unlike other funds, some funds do high-frequency trading, some do arbitrage, and others are low-frequency, judging large trends over six months or a year. We do medium to high-frequency CTA, using hourly levels to judge whether the market is rising or falling. If we think it will rise, we go long; if we think it will fall, we go short; we profit directly from the direction. We do not operate both long and short simultaneously; we do not "long one batch of coins while shorting another batch." That kind of strategy is what we call "long-short strategy," but we are purely CTA. The biggest difference between pure CTA and other strategies is that we can have relatively large drawdowns. Our drawdown ratio is the largest among different types of funds. For example, high-frequency drawdowns usually do not exceed 1%; arbitrage drawdowns do not exceed 3 to 5%; but for us doing CTA, it can exceed 10% or even 20%, and such situations do occur. We almost encounter drawdowns exceeding 20% every year. At that point, we feel psychological pressure. Investors will ask us: "Is this strategy still viable? Can the fund still make money? Do you still have confidence? Have you changed the strategy? Has the weight in the portfolio been reallocated?" They will ask various questions. At that time, we need to judge from the data and from different angles whether this strategy can continue to run. This is a very important point.

So, we actually encounter varying degrees of large and small drawdowns every year. For example, we have experienced drawdowns of over ten points or twenty points. In fact, drawdowns of over ten points or twenty points are already considered quite rare.

Mia: Really? Are you comparing it with manual trading?

Calvin Tsai: Yes, after all, we are not purely proprietary trading, because many manual trades are self-funded. I believe proprietary funds can withstand drawdowns of, say, fifty points or more. But if you have investors or clients, the situation is different. For example, if there’s a drawdown of thirty points, they will definitely call to ask, and they might even call in the middle of the night. So, to make investors feel a bit better, I think a drawdown of twenty to thirty points is already a limit.

Mia: When these investors call you to inquire, how do you explain it to them?

Calvin Tsai: When there’s a significant drawdown, investors also need a process to adapt. For example, some investors might enter at the peak of our curve and immediately encounter a downturn, possibly losing twenty points right away. They will feel uncomfortable, asking, "Why did I lose twenty points as soon as I entered? Is this a scam?" They will definitely ask this question. But my experience is that if they stay for a longer time, say over a year or two, they will see situations like: first losing twenty points, then thirty points, and then reaching a new high, earning a hundred points. So they will gradually get used to it and understand what CTA is, rather than just thinking of it as purely high-frequency or purely arbitrage. Many people think that eight out of ten funds in the market are arbitrage or high-frequency, and it’s common to lose two or three points. So I need to educate them on what CTA is, what we do, and the logic and fundamentals we use to make judgments. I think this is an educational process that takes time for them to gradually adapt.

Mia: What are the important indicators in your CTA process?

Calvin Tsai: Important indicators can be divided into two types. The first type is what we use to make judgments, such as generating signals—looking at which data to determine market direction. For this issue, we actually don’t have a particularly heavily weighted factor or a single dominant factor. For example, if you hear about a fund or institution that has a very important indicator, I would start to wonder: if this indicator fails, will it have a significant impact on the fund? So I prefer to keep the weights of each indicator and factor as balanced as possible. This way, if one factor fails, it won’t have a huge impact on the entire portfolio. So we don’t let any single factor have too much weight. I’ve tried in the past where one factor was very profitable, and I gradually increased its weight until it accounted for more than half of the entire portfolio. For example, if you have a hundred dollars, more than fifty dollars might be on this factor. It might be very profitable the next month or week, making a large profit for the entire portfolio, but if it fails the following month or quarter, it could lead to significant volatility for the entire portfolio. So we prefer to average the weights of each factor.

The second type is what indicators we use to determine if a factor can make money. We try to look at its risk-reward ratio, such as the Sharpe ratio, which is a number we value highly; the higher the Sharpe ratio, the better. We also look at the Calmar ratio, which is the annual return divided by the maximum drawdown. We examine these different numbers to determine whether this factor is a good indicator.

Mia: In this process, if you can achieve over 200% returns even in a bear market, do you think there are unique aspects to your methodology and strategy? How do you control risk?

Calvin Tsai: This question is very difficult to answer. Simply put, it’s about how to create a very successful quantitative model. Right? I think you need to do each step relatively well. For example, you need more data, look at the data more accurately, and use different models to test when creating a quantitative model to see which factors are more useful. At the same time, you need a very rigorous methodology to determine whether a factor is genuinely useful. What is falsely useful? Falsely useful means you see it making a lot of money in the database over the past three to five years, but when you run it in real trading, it turns out to be losing money. In other words, you thought it was useful during testing, but it’s not useful in actual operation. We often encountered this situation in the past, so it’s essential to have a strict method for screening. After confirming that it is genuinely useful, I gradually add it to real trading, increasing it slowly by one or two dollars. Another point is to judge from the underlying logic whether a factor can genuinely make money, which is very difficult. Many people find that factors that look good do not make money when used, or even lose money. So every step must be done carefully, rigorously, and tightly.

Mia: Is the strategy development done by you personally?

Calvin Tsai: Yes, our team consists of a few people, and I mainly handle strategy development. Other colleagues, for example, someone is responsible for system development, and someone else is responsible for machine learning research.

02 Strategy Iteration: From Inspiration to Implementation, a Rigorous "Three-Step Method"

Mia: For example, if you have an idea for a strategy, what is the entire iteration process from strategy development to execution? Can you share?

Calvin Tsai: First, you need to have a rule, just like manual trading. In manual trading, there are also some judgment points. For example, some people look at charts, some look at prices, some look at trading volume, some look at news, and some look at KOLs or information provided by others; everyone has different judgment points. Initially, you need to think clearly about which factors and indicators to consider for your entry strategy. Using the simplest price example, some people look at moving averages, such as the 20-day moving average. If the price breaks above the 20-day moving average, I will buy. So the first step is to determine which factors to use.

The second step, which is crucial in quantitative trading, is to validate whether this strategy can make money using historical data. So we look for price data from the past three to five years, whether online or provided by exchanges. Then we determine the trading frequency, whether to look at minute, hourly, or daily prices, and input this data into the computer to start programming. For example, establishing the logic for the 20-day moving average: if the price is greater than the 20-day moving average, buy; the selling method might be if the price falls back below the 20-day moving average. Then we let the computer run the strategy, which will tell you how much it earned on average each year over the past five years, which months had losses, the profit-loss ratio, Sharpe ratio, Calmar ratio, etc., to determine if the strategy passes. You can set standards, such as earning over 50% annually and having a drawdown of less than 20% to pass. If it passes, you move to the next step; if it doesn’t, you adjust the parameters. For example, you can change the 20-day moving average to a 10-day, 30-day, or even test up to a 100-day moving average to find the optimal parameter. In the end, you might find that the 50-day moving average is the best. Then you take the 50-day moving average strategy to simulate trading for a week or a month to see if the system can run stably, whether signals can be sent to your computer in real-time, telling you how much Bitcoin to buy or how many contracts to sell. After completing the simulated trading, you enter the real trading phase, gradually increasing your position, starting with $100, then $1,000, and gradually increasing to the target position.

The final step is risk management, checking whether the strategy has caused significant losses in the portfolio or if there are any failure situations. If everything is normal, let the strategy gradually make money.

Mia: How often do you iterate your strategies?

Calvin Tsai: It depends on the frequency of the strategy. If it’s high-frequency, such as at the second or minute level, we adjust quite frequently, possibly once a week or every few days. But if it’s a slower strategy, like at the hourly level, we might only adjust once a month or every few months.

Mia: In the current situation where so many quantitative teams are developing strategies, how do you maintain your industry-leading position while also keeping your returns very high?

Calvin Tsai: I think it’s related to the point I just mentioned—doing every step as meticulously as possible. Also, it relies on data. The foundation of quantitative trading is that the amount of data needs to be sufficient, and the data you look at should be different from others. If you want to earn money that others can’t, you need to look at things that others haven’t looked at or have overlooked. Some teams I’ve noticed didn’t look at on-chain data before, so I started looking at on-chain data. I found that some teams didn’t pay attention to sentiment in discussion forums, so I began to focus on sentiment. Many teams are looking at charts and prices, but I don’t look at charts or prices; I try to do things that others don’t do. By doing what others don’t do, you can earn money that others haven’t earned.

Mia: For example, when encountering extreme market conditions, like the LUNA crash, how do you adjust your strategy or hedge to ensure you don’t incur significant losses?

Calvin Tsai: LUNA crashed in May 2022. At that time, our quantitative trading portfolio did not include LUNA; we mainly traded major coins like BTC and ETH. LUNA was traded in my manual account, but most of the funds were in the quantitative portfolio. At the beginning of 2022, I bought a little LUNA. By April, I noticed it was offering an annual interest rate of about 20%, and I thought it was hard to sustain. Long-term, such a high interest rate is difficult to support through the protocol. I looked at its reserves and found that the funds were insufficient to sustain several months of high-interest payouts. So, I shorted one of the interest-paying protocols called Anchor. At that time, I was long on LUNA while shorting Anchor, with a ratio of about 1:1. As a result, when LUNA plummeted in May, my manual account lost money while the quantitative account made money, ultimately resulting in a break-even situation. In the quantitative portfolio, we continued to engage in trend trading. When LUNA crashed, the market was very volatile; we might have initially gotten the direction wrong, but if the market moved in a certain direction, we could still make money.

03 The AI Wave: An Opportunity and a Threat

Mia: I remember in your previous interview, you mentioned the AI trading crisis. Do you think that under the current AI wave, you will use AI? Could AI pose a threat to quantitative trading?

Calvin Tsai: AI does help our quantitative system. We have about two or three layers of strategies that generate signals using AI. We input different factors and data, using some time series models in machine learning for training, and then it generates signals, such as whether to go long or short now. In real trading, these strategies are also profitable, so we have two or three layers of strategy combinations that rely on AI to generate signals. Additionally, AI is also very helpful in programming. Writing a piece of code used to take ten hours, but now with tools like ChatGPT and DeepSeek, you can complete it in five or ten minutes. The same functionality has greatly improved efficiency and saved a lot of time. Of course, it sounds like a significant advantage, but other institutions or teams can also use the same AI tools and machine learning models for quantitative trading to improve their efficiency. So AI is both an opportunity and a danger. It can help you, but it can also make your competitors stronger. In the next five to ten years, the key is how to make good use of AI tools. Everyone uses AI differently; some people may not produce useful signals using the same machine learning model, but you might be able to generate effective signals. Every detail is crucial. If you can use AI more meticulously and efficiently, this should be a focus for the next five to ten years.

Mia: You just mentioned that you can use AI to generate some useful signals. How long did you spend on this training process?

Calvin Tsai: Initially, we started testing in 2021. At that time, we didn’t find anything particularly useful, so the early results were not significant. By 2022, we revisited whether machine learning could be profitable, but that year also didn’t yield substantial profits. In the first couple of years, we didn’t really incorporate machine learning into our strategies. By 2023, with the rise of the AI wave and many people globally discussing AI tools, we tested again and found that it started to become profitable. I think there’s an effect we call “self-fulfilling.” It means that as more and more people use this tool, it becomes increasingly effective. From 2023 onwards, into 2024 and 2025, the results each year have been better than the previous year. More and more people are using it, so it gradually transformed from “useless” to “useful.”

Mia: I find you to be a very perceptive trader, for example, you can identify things that others haven’t done, thus optimizing your strategies. You started working with machine learning and AI back in 2021. How did you cultivate your perceptiveness during this process?

Calvin Tsai: I think part of the reason is my previous experience working in traditional hedge funds. At that time, a company might have six teams, and each group was independently competing. We would see the performance of different groups each month; for example, one group might make money for several consecutive months while others didn’t earn anything in the market. During that time, I would seek out the traders from that group to have meals, learn, and exchange ideas. I think this is very different from trading at home. When trading at home, the environment is relatively closed; no one communicates with you, and when you lose money, no one comforts you, and when you make money, no one shares your joy. In an institution, the benefit of being in a team is that you can communicate and share. I also learned how to trade other asset classes from different groups. For example, I wasn’t trading forex at that time, but other groups were doing it, and they were willing to share their experiences. Although we traded different instruments, I could learn the logic and methodology of strategies from their forex or other asset trading. So, I think communicating with different traders is very helpful for enhancing your perceptiveness.

Three, Two Zeroes | The Journey of a Trader

01 Youth and Trading: Starting Research on the Lottery at 12 Years Old

Mia: Calvin just mentioned some of his past experiences in traditional finance, so I think we might as well start from when you first began learning to trade and discuss how you became such a legendary trader. I remember you mentioned that you started learning to trade with your red envelope money at the age of 12? At 12, I didn’t even know what I was doing; was I playing with mud? (laughs)

Calvin Tsai: At that time, I was probably in middle school, in the first year. My family didn’t have much money; I was quite poor. I still remember during lunch, I only had about 20 Hong Kong dollars in my hand; I really only had 20 Hong Kong dollars.

Mia: At 12 years old, 20 Hong Kong dollars was still okay, right?

Calvin Tsai: I remember that some of the more expensive lunches at school were about 25 dollars, which I couldn’t afford; some were cheaper, around 15 dollars. I could only choose the cheaper meals every day. I also remember there was a small shop near the middle school with very cheap meals, only 12 dollars. I would run to that shop every day, buy a 12-dollar meal with my 20 dollars, and have 8 dollars left over. I was really poor at that time. I wanted to buy toys but had no money, and I couldn’t participate in extracurricular activities at school because they cost money. I saw other classmates and envied them, wondering why their families were so wealthy and why they could freely choose what they liked. At that time, I was thinking, what ways could I make money?

The first thing that came to mind, since I didn’t have any capital, was to buy the lottery, Hong Kong’s Mark Six. A lottery ticket only costs 5 dollars, and if you hit the jackpot, it’s about 8 million Hong Kong dollars, which is the method with the highest leverage—turning 5 dollars into the possibility of millions. After seeing this possibility, during the summer of my first year in middle school, I started researching how to predict the next winning numbers. The local lottery requires guessing 6 numbers plus a special number, predicting which 7 out of over 40 numbers will be drawn. I took historical data from over 100 past draws to backtest and see if I could predict, for example, how many times number 1 had been drawn consecutively, how many times number 2 had been drawn consecutively, and then predict which numbers would be drawn that night. I spent about two months on this, but I couldn’t successfully predict; every time was a failure. Later, I gave up on this method, thinking it was completely random.

The second thing I thought of was trading. At 12, I couldn’t legally work; you had to be 16 or 18, and working didn’t have leverage, earning 40, 50, or 60 dollars an hour, which couldn’t lead to quick wealth. So I thought about trading stocks. However, for a 12-year-old me, stock trading was still very distant. Without a parent to guide me, it was hard to achieve. At that time, my parents didn’t trade stocks, but I had an older brother who was 8 years older than me. He had just entered university and opened a stock account to look at stocks. I saw him looking at moving averages and charts, which piqued my interest. I thought, if I could successfully predict whether a stock would go up or down tomorrow, I could make money.

Mia: Did that brother make money from stocks at that time?

Calvin Tsai: Sometimes he made money, sometimes he lost. Anyway, later I started reading books and researching on my own. By the time I was about 14, I had really studied for a whole year. I used the textbooks from middle school to write down my predictions of whether a stock would go up or down the next day. Then the next day, I would run to the computer to see if it really went up or down, checking the answers—OK, this one was right, this one was wrong, this one was right again.

Mia: Like a simulated trading account?

Calvin Tsai: Yes. For a whole year, my “win rate” was quite high. So I decided to open a real trading account. At that time, I wasn’t yet 18, so I asked my brother to help me open a securities account at the bank downstairs using his ID. I told him—this is my red envelope money, I’ll set the password myself, and you shouldn’t touch the money inside.

Mia: You had to guard against him too?

Calvin Tsai: Yes. I borrowed your identity to open it, but the money is mine, the password is mine, and you shouldn’t care about which stocks I buy. He said OK, no problem. I remember the first year I made money, earning about thirty percent. By the time I was 16, I thought—since I’m making money and my vision is good, why not try high leverage? So I started using derivatives. At that time, Hong Kong stocks didn’t have leverage, but there were bull and bear certificates and warrants, which could achieve 10x or 20x leverage. I transferred my money from stocks to bull and bear certificates and warrants to try higher-risk trades.

02 The Revelation of Liquidation: Two Zeroes at 16 and 19 Years Old

Mia: You started dealing with high leverage?

Calvin Tsai: Yes, I started dealing with high leverage. I remember one day I opened the computer and saw my capital drop from over 40,000 Hong Kong dollars to only a couple of hundred, or even just over a hundred dollars. I thought I had logged into the wrong account—why is there so little left?

Mia: Did you think it was stolen?

Calvin Tsai: I thought it was stolen.

Mia: Did you think your brother took it?

Calvin Tsai: Yes. Later I found out it wasn’t stolen by someone; it was “stolen” by the market. At that time, I had no idea how to face it. Seeing only a few hundred dollars left in the account, I didn’t dare to tell my family or my brother; I had no idea what to do. I remember that night, I took my best friend from middle school to the park to walk and chat. I asked him, since his dad traded stocks, “If it were you, how would you feel if you lost money trading stocks?” At that time, I didn’t understand money at all and didn’t know how to face losses. For me, winning and losing felt like playing a game. The first time I saw a loss, I had no idea what attitude or emotion to use to face it. He thought for a moment and said, “I wouldn’t feel anything even if I lost everything.” I said, “Are you silly? Why wouldn’t you feel anything after losing 100%?” Then he told me, “Think about it, tomorrow you can still eat, you’ll still go to school as usual, you can sleep in your own bed tonight, and everything in life will go on as usual. You don’t have kids to raise, no family to take care of, so whether there are thirty or forty thousand or three or four hundred dollars in your bank account, it doesn’t make much difference to you.” I thought about it seriously and really felt he was right. That moment had a huge impact on me. I thought—trading young is really a good opportunity. If you lose a lot of money at thirty or forty, with a family and kids, that would be terrible. So at that time, I decided—while I’m young, I should learn more about trading and use a bit more leverage. Leverage is something you can only play with when you’re young; that was my biggest feeling and thought at that time. After my first liquidation, I continued to work hard to learn how to trade. By the time I got to university, around nineteen years old, I faced my second liquidation—losing even more. At that time, I was in my first year of college and was also tutoring middle school students, quickly accumulating some funds to trade again. That year at nineteen, I once again encountered leverage, using options and futures, and lost everything again, going from about 150,000 Hong Kong dollars back to zero.

Mia: Between the ages of fourteen and nineteen, you didn’t trade much, right? You were mostly learning?

Calvin Tsai: I was learning. Sometimes I would read books, investment books, and look at different—like fundamental analysis, technical analysis, and charting books.

Mia: You have a lot of patience; from the very beginning of your trading journey, from twelve to fourteen, you first learned, then traded, and after the liquidation, you continued to learn for five years.

Calvin Tsai: Yes, that’s right.

Mia: You kept waiting, and then at nineteen, you struck again.

Calvin Tsai: Yes. Because at eighteen—I just mentioned tutoring and accumulating some money, so I had funds to go back into battle.

So I entered the market, still trading Hong Kong stocks at that time. I also encountered options, futures, and different high-leverage products. By nineteen, I lost everything again, going from 150,000 back to zero.

03 The Epiphany of Quantitative Trading: Abandoning Manual Trading, Embracing Data

Mia: Yes, that feeling is really not good. Because you encountered this situation for the second time, and you had already worked very hard.

Calvin Tsai: At that time, I thought, I’ve already read the news, and I’ve looked at different charts—second-level, minute, hourly, and daily charts. I’ve also looked at different technical indicators and listened to various people talk about fundamentals, whether the company behind the stock is good, and whether it has future prospects. I felt I had analyzed everything thoroughly, so why was I still losing money? I couldn’t figure out why this was happening, so I seriously thought—was there something wrong with my methods? I felt that my existing approach was not quite right, which led to these two major losses. Then I wondered if there were other trading methods that could allow me to make stable profits in the market. It definitely wasn’t my current methods. So I searched online and read different books, and I came across the term “quantitative trading.” I realized that I had been doing something while overlooking one key point—I hadn’t used historical data to verify whether my methods could make money. I had always been listening to others. If someone said a 20-day moving average could make money, I believed it; if someone said this stock was good, I thought it was good; if someone said this industry had great growth potential, I believed it should be great. Theoretically, you should verify your methods. For example, was what this person said true in the past? Were the KOLs accurate? If you had used that 20-day moving average to buy and sell ten years ago or five years ago, could you really have made money? I found that I had overlooked a significant point in my manual trading: I hadn’t used historical data to verify whether my methods were effective. Quantitative trading is about using historical data to see if your methods can make money. You only start putting in money when you test and find that it can make money. So, in quantitative trading, the potential waste is time, but not money.

Mia: You experienced the shock of liquidation at a very young age. Did it affect your character and subsequent risk management?

Calvin Tsai: I think it gradually made me emotionally—unaffected by profits and losses. In the past, I might have been very happy to see profits and very unhappy to see losses, with my emotions completely tied to the market. When the market went up, my emotions went up; when the market fell, my emotions would be very low. Later, I gradually trained myself to—regardless of whether my account was losing or making money, I didn’t feel much. I think this made me more rational.

04 Choosing Pharmacy: Leaving a Backup Plan, But Not Giving Up on Passion

Mia: I understand. When you were in university, I remember you studied pharmacy. Normally, after studying trading for so many years, you would choose finance or related subjects. Why did you choose pharmacy at that time?

Calvin Tsai: At that time, when I was choosing courses in middle school, I was thinking about what subjects to choose in university. I also asked for opinions from different people, and they said, “Just take some subjects that require a license to work in that industry.” For example, doctors, lawyers, and pharmacists must study those subjects in university to obtain a license to work in the industry. But for finance, stocks, and trading, you don’t need any license. So many people suggested—“In university, take those subjects that can lead to a license.”

Mia: To leave yourself a backup plan?

Calvin Tsai: Yes. At that time, for example, in Hong Kong, I also heard that the medical industry had relatively stable salaries. So I thought: OK, I want to find a relatively stable job to support my trading. Even if trading loses money, I still have a stable job to balance it out, with cash flow to support my assets and finances. That was my thinking at the time. Later, in my third year of university, I participated in several trading competitions and was fortunate enough to win awards. At that time, a friend told me—“Have you heard of proprietary trading firms?” I asked, “What is a proprietary trading firm?” My friend said, “They will give you a sum of money and won’t interfere with what strategies you use; you decide for yourself. If you make money, you share the profits.” I thought this model was quite good. After all, I was trading myself, and my capital wasn’t large. If I joined a firm, they would give me a larger sum of money, and I could use other people’s money to make profits, and I could share the profits. So I became very interested in this and searched online, finding a proprietary firm to interview. During the interview, I showed them the awards I had received in the competitions.

Mia: What awards did you win at that time?

Calvin Tsai: I participated in some manual trading competitions and also in quantitative trading competitions. The quantitative trading competition is quite different from manual trading competitions—quantitative trading requires you to program your own strategies, and after writing them, they will test whether the strategy is profitable using historical data. If it is profitable, you become the champion.

05 Joining a Hedge Fund: Looking at Data More Closely and Learning Client Expectation Management

Mia: So at that time, did you also self-learn programming for quantitative trading?

Calvin Tsai: Yes, I self-studied in university. Because there were no programming courses in my major. Then during the interview, I showed them my monthly statements, which showed that I had been making money for several consecutive months. They asked me, “How did you make these trades? Why did you make this trade?” I explained clearly: OK, I looked at this data, did backtesting, and saw that this strategy could continue to make money, so I used it. And just like that, I successfully entered the industry and got my first job in quantitative trading.

Mia: How much did they give you to operate at that time?

Calvin Tsai: At that time, it was still simulated trading at first. They would observe for a few months to see if I could make money in the simulated account. After proving I could make money, they would gradually understand the essence of my strategy and risk management, then decide how much money to give me. It might start with a few million Hong Kong dollars, and if I performed well, it could gradually increase to tens of millions.

Mia: OK, how long did you spend on the simulated account before you started making profits?

Calvin Tsai: At that company? It took about three to six months at first to prove—OK, I have this strategy, and it can continue to make money.

Mia: I see, so after your liquidations at fourteen and nineteen, you started getting into quantitative trading, participated in various competitions, and then joined this company. Did you stay at this company afterward, or were there changes?

Calvin Tsai: There were changes. In my third year, around twenty years old, I worked as an intern at this company for a year and a half. After graduating, I moved to another hedge fund, where I worked for about five years.

Mia: At that hedge fund, did you learn some trading concepts that could also be applied to crypto?

Calvin Tsai: Yes. In fact, most strategies can be replicated across different asset classes. For example, the simplest trend trading, like moving average strategies—buy when the price is above a certain level, sell when it’s below a certain level—these logics can be applied to different instruments. However, some strategies differ between traditional markets and crypto markets. Traditional markets have opening and closing times, and there are after-hours gaps—upward gaps and downward gaps. Cryptocurrencies trade 24/7, with no concept of opening and closing, so certain strategies based on opening and closing cannot be directly transferred. Of course, the crypto market also has unique strategies that traditional markets do not have. For example, on-chain data allows us to analyze various information on the blockchain to determine market movements. In traditional markets, there isn’t such transparent data for quantitative analysis. So both markets have common strategies and their own unique strategies.

Mia: What are the most important points you learned at that traditional fund that have impacted your subsequent career?

Calvin Tsai: First, you need to look at data more closely and more extensively. Second, if you trade at home, you won’t learn how to manage clients and do expectation management. For example, if you make money or lose money, how to communicate with clients and help them understand the overall situation. I think this is one of the biggest gains from my five years there. For example, if you can earn 50% in real trading every year, and a client asks you, “If I invest with you, how much can I earn in a year?” If you directly say 50%, you haven’t managed expectations well. Usually, I would give a lower estimate, saying, “You might expect to earn around 20% to 30%.” By the end of the year, if you only made 40%, which is less than the previous year, because you initially told them 20%, they would feel good and happy. They would be willing to keep their money with you and even add more capital. So, I think doing good “expectation management” is a very important lesson I learned in those five years.

The exciting conversation is not over; more content will be presented in the next part…

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