Nine years is enough to completely change the appearance of a quantitative team.

Written by: Joe Zhou, Foresight News

"Out of ten quantitative teams, eight are losing, and one or two have even gone bankrupt and disappeared."

This is a rough impression left by Lao K after investing in over ten cryptocurrency quantitative teams during the last cycle.

Of course, this is not an industry statistic. It is merely the result observed by someone who has been deeply involved in the crypto industry for a long time, having invested real money in over a dozen teams.

Lao K is a partner of a Hong Kong quantitative trading team whom I recently met in Bali. In 2016, he began to engage with cryptocurrencies and officially entered the industry in 2017. Over the past cycle, he has invested not only in several cryptocurrency quantitative teams but also in crypto VCs such as Waterdrop Capital and Hack VC.

Over the past nine years, he has experienced nearly all of the crazy moments in the crypto space. He bought Antshares when it was only 1 yuan, later reaching a peak of nearly 1400 yuan; when BNB was just 1 yuan, he and his team bought 50,000 coins, and today the price has reached about 800 dollars.

But compared to these stories of explosive growth, he wanted to tell me something else: In the crypto space, making money in quantitative trading may not be the hardest part; surviving is.

To gain a deeper understanding of quantitative trading, during the last cycle, he invested real money in over ten quantitative teams to see how others were making money.

The results surprised him: roughly eight out of ten are losing money, and some have even disappeared.

Some were obsessed with contracts and ended up going bankrupt, some had their on-chain funds stolen, others faced exchange closures, and some suffered heavy losses during altcoin crashes.

With real money, Lao K peeled back a little-known corner of quantitative trading and revealed a fact often obscured by terms like "quantitative," "arbitrage," and "low risk": within this industry, the truly resilient teams that can survive through cycles may be far fewer than imagined.

Below is Lao K's own account.

First Bucket of Gold: From Thousandfold Myth to Manual "Arbitrage"

In 2016, when Lao K first came across cryptocurrencies, the industry was still in a very early and primitive stage.

In the second half of 2016, he participated in an early domestic public chain project's ICO—Antshares, which later became NEO.

The participation method at that time was very primitive: the project team would announce the address on the website, and investors would send Bitcoin, after which they would receive project tokens. He remembers the project raised 20 million yuan in the second round, only accepting Bitcoin, and the projected issue price was about 1 yuan.

The following year, the market went completely crazy.

In 2017, ICOs began to explode on a large scale, and the crypto market entered a bull market. Particularly after the "94 incident," Lao K's Antshares experienced an astonishing surge—from about 1 yuan, it peaked between 1200 to 1400 yuan.

The nearly thousandfold market made him truly feel the explosive power of this market for the first time.

However, what truly allowed Lao K's team to earn real money and realize that there was a "business opportunity" was not the thousands of times rising coins but a simpler phenomenon: The price of the same coin can be different on different trading platforms.

At that time, the market was extremely early, and information, liquidity, and prices between exchanges were not fully synchronized. The same coin could sell for 100 yuan on platform A while it was already 105 yuan on platform B.

After the bull market began in 2017, Lao K and his team quickly discovered the price differences between various platforms, having already held some coins. They researched on their own and manually operated the cross-platform transactions: buying where it was cheap and selling where it was expensive.

There were no complex quantitative systems, nor mature trading infrastructure. Monitoring the markets, calculating price differentials, transferring coins, and placing orders were all done manually.

"In just one month, we made back the principal of 100,000 yuan."

This experience made Lao K realize for the first time that what he was truly interested in was perhaps not how much a certain coin could go up, but whether it was possible to continuously make money from the price differences in the market itself.

As trading volumes grew, manual arbitrage quickly became unsustainable.

They began purchasing other trading software. Later, the monthly software usage fees could even run into tens of thousands of yuan.

Lao K calculated: since they were spending so much money every month, several tens of thousands of yuan could be enough to hire a few people to develop their own software, so why not write their own system?

Thus, the team, originally consisting of only seven people, began developing their own quantitative trading platform.

Initially, they wanted to open the platform to external users, but later due to regulatory reasons, it turned into an internal system. This system has been iterated upon ever since.

Besides Antshares and early arbitrage, Lao K has vivid memories of BNB.

In July 2017, Binance conducted its ICO with a subscription price of about 1 yuan, settled in ETH. The participation threshold was very low; registration required only an email address. Lao K's team used three accounts, each subscribing for over 10,000 BNB.

Later, this batch of BNB was sold in batches starting from 1 yuan, all the way up to 80 yuan, averaging a profit of 20 to 30 times. At that time, this was already a pretty considerable return.

But looking back on this trade today, there is still a bit of regret: BNB later peaked at over 800 dollars. To maintain Binance's VIP level, Lao K eventually bought back some at around 100 dollars.

He doesn't regret it: "You can't earn back all the money."

This perhaps also explains why he didn't continue to seek the next "thousandfold coin."

Compared to betting on the rise or fall of a particular asset, he began to become increasingly interested in another matter: Whether it is possible to find a way to continuously make money based not on market direction, but on market structure itself.

That year was 2017.

Lao K himself might not have realized that a seemingly accidental "arbitrage" would ultimately turn into a nine-year career.

From Earning 100% a Month to 20% a Year: Quantitative Earnings Are Getting Harder

In 2017, earning doubled in a month.

In 2021, earnings doubled in a year.

By 2026, an annualized return of 20% to 30% is already considered "good" earnings in Lao K's eyes.

Over nine years, within the same industry, the rate of return has formed a completely opposite curve. Why?

Lao K lamented: The era when quant could easily make money is coming to an end.

When he first started working in quantitative trading in early 2017, the market was still very early. Information, liquidity, and prices between exchanges were not fully synchronized, and as long as he found price differentials, he could earn considerable returns.

Lao K recalls that in the early days, when the team engaged in cross-exchange arbitrage, they could double their principal in a month. It was not because the strategies were complex, but because the market at that time was inefficient with few competitors, and many opportunities that today have been swiftly eliminated by algorithms could even be captured manually back then.

However, the good days did not last long.

In 2018, the market entered a bear phase, with Bitcoin plummeting from nearly 20,000 dollars to just over 3,000 dollars.

For ordinary investors, the fear is falling coin prices.

But for arbitrage teams, the scariest part is not the downturn, but the absence of trading.

Without trading volume, price differentials are hard to form; without volatility, strategies lack profits.

A bear market doesn't necessarily mean losing money; it might mean there's simply no money to be made.

During those years, the altcoin assets that the team had accumulated previously shrank significantly, at one point suffering losses of about 80%. Fortunately, at that time the team comprised only seven members, with very low costs. Everyone held on with basic salaries while continuing to invest the earned money into servers and system operations, thus gradually getting through.

In 2021, quantitative trading entered another golden era. The insane rise of altcoins brought unprecedented liquidity and volatility, creating opportunities for nearly all strategies, including arbitrage, market making, and trend trading.

Lao K still remembers a legendary market-making case in the industry: One day, the return was 15 times.

One team made this kind of return using a high-frequency market-making strategy in extreme market conditions.

Of course, such returns cannot simply be replicated with large funds, but at least it shows that during that era, there were indeed highly profitable opportunities unimaginable today.

Later, Lao K increasingly realized that what truly benefited quant trading was not only bull markets.

Quantitative trading thrives on "inefficiency."

As long as the market is chaotic, fragmented, and immature enough, price differentials will occur; and as more and more funds, algorithms, and institutions enter, the market becomes increasingly efficient, and the profits that once belonged to quantitative trading will naturally shrink.

After 2022, the market again entered a period of slumps, but Lao K discovered that this time, what truly changed the industry was not the bear market.

It was that the market became increasingly efficient.

Price differentials that once existed for several minutes or even tens of minutes may now be eliminated within just milliseconds by algorithms.

Previously, a 100,000 yuan account with a few people who understood coding could find several opportunities; now, behind those same opportunities stand more and more professional institutions, low-latency systems, and algorithms.

It's not that quant cannot make money anymore; it's that everyone has learned how to make money.

Lao K's team also began to adjust. They gradually reduced their altcoin exposure, replacing part of their quantitative base positions with BTC and ETH, which have better liquidity and stronger market consensus.

By 2026, he felt this market resembles a localized Bitcoin market, and the liquidity and funding support for altcoins have become increasingly difficult to recover to past levels.

After nine years, Lao K's rate of return has also shown a clear downward trajectory:

2017: doubled in a month.

2021: doubled in a year.

2026: annualized 20% to 30%.

The numbers appear increasingly smaller, but the competition behind them is intensifying.

In the past, making money relied on an immature market; now, making money depends on being faster, steadier, and not dying before others.

This does not mean that quantitative trading has become ineffective. On the contrary, it means that the market has transformed from a land of inefficiency to a crowded poker table.

The market is becoming increasingly efficient, with fewer super profits, yet quant trading is resembling an actual financial business more and more.

After nine years, Lao K is still doing the same thing: searching for price differentials in the market.

Only that the era when earning doubled in a month has already passed.

With No More Money to Earn, They Begin to Search for the Next 'Pond'

Quantitative teams rarely remain loyal to any one asset.

Wherever there is liquidity, volatility, and previously undiscovered price differences, they will go there.

Lao K's team is no exception.

Over the past nine years, they have experienced three distinct migrations: from the mainland to Hong Kong, from arbitrage to market-making, to transitioning from altcoins to tokenized US stocks.

The first migration was to a different place.

After 2022, as Hong Kong began to embrace Web3 again, Lao K seriously considered a question: If quantitative trading were to continue for another ten years, where should the company be based?

Ultimately, they chose Hong Kong. They registered a company, settled in Cyberport, and resolved identity issues through various talent schemes. This year, Lao K even sold his family's house, completely cutting off his retreat.

For him, the greatest attraction of Hong Kong is not any specific policy, but three words: Safety, compliance, and certainty.

In the past, quant trading was more like a few individuals sitting in front of computers making trades; but when the team decided to operate long-term, issues such as company registration, bank accounts, fund custody, and team recruitment all became problems that needed to be solved.

They realized for the first time: Quantitative trading is not just about trading strategies; it is a financial business that requires long-term management.

The second migration was to a different method of making money.

In 2017, they started by utilizing cross-exchange arbitrage: buy where it’s cheap, sell where it’s expensive.

However, as the market became increasingly crowded, this method became difficult to replicate past profits. Lao K put it frankly: "The top 10% of institutions take 90% of the profits, while the remaining 90% of institutions are only scrambling for the 10% leftovers."

So, they began expanding from arbitrage to market-making, trend strategies, and also started experimenting with new tools like AI.

However, what truly raised their alarm was the altcoin market they were most familiar with.

By the first half of 2026, Lao K noticed that the liquidity of altcoins was significantly shrinking. With less trading volume and less funding, the opportunities that could accommodate strategies were becoming fewer.

He said a vivid remark: "When the pond's water dries up, the fish will naturally disappear."

Thus, they began searching for the next "pond."

This time, they focused on tokenized US stocks.

In June of this year, the team first put out 10,000 dollars to test a tokenized US stock strategy.

Three months later, this fund had increased to 300,000 dollars.

Although this strategy currently accounts for less than one-tenth of the team's total funds, Lao K believes it represents a new direction. In the past, they traded native crypto assets; now, they are starting to explore tokenized versions of traditional US stocks like Tesla and Nvidia.

The greatest appeal of these assets to them lies in: one side being the price anchor of traditional financial markets, while the other side is the 7×24 hour trading characteristic of the crypto market.

Thus, the team began redeveloping trend indicators suitable for all-day trading, attempting to transfer the trading experience accumulated over the past nine years into a new market.

Looking back, the changes in this team are actually quite interesting.

In 2017, they were searching for price differentials in the altcoin market;

In 2022, they moved the company to Hong Kong, seeking a more certain operating environment;

In 2026, they started looking for the next "pond" with liquidity, volatility, and opportunity.

After nine years, the change has never been exclusive to strategies.

The market has changed, the assets have changed, the city has changed, but their logic of searching for "inefficient markets" remains the same.

This might be the most realistic way for quantitative teams to traverse cycles: not to sit idle in a market waiting for it to return, but to seek the next pond when that market runs dry.

Having Earned a Thousandfold, They Could Not Avoid FTX and FCoin

After nine years in quantitative trading, Lao K has experienced almost all types of extreme market conditions: the "94 incident" of 2017, the "312 incident" of 2020, the "519 incident" of 2021, and the "1011 incident" in October 2025.

For ordinary investors, these days usually mean crashing prices and losses; but for quantitative teams, extreme market conditions sometimes signify opportunities.

While others fear volatility, quantitative teams might be waiting for it.

Because the more extreme the market fluctuations, the greater the price deviations between different exchanges. Normally, a price differential of only 0.1% could quickly expand to 1% or more in extreme conditions. For arbitrage strategies, this means more profits to capture.

The bull market in 2017, the "312 incident" of 2020, and other extreme conditions have all provided ample arbitrage opportunities for Lao K's team.

But herein lies the problem. Quantitative profits are predicated on the proper functioning of the entire trading system.

When FCoin encountered issues in 2018, Lao K's team directly lost about 20% of their principal.

This was not a loss due to a strategy failure. If a strategy loses 20%, at least one can understand why; but when an exchange has problems, you might not have made any mistake and yet your money is gone.

Years later, a similar situation occurred again.

During the collapse of FTX in 2022, Lao K's team had already heightened their risk awareness, keeping little money on FTX and ultimately only suffering an 80,000 dollar loss.

However, the impact from FTX was even greater for him.

Because FCoin was merely a rapidly rising exchange at that time, whereas FTX had already become one of the top platforms in the industry. Many professional institutions had once considered placing funds on such a platform to be a relatively safe choice.

But, as it turned out, size, brand, and professional image do not guarantee fund safety.

This also changed how Lao K later judged exchanges. In the past, he primarily looked at fees, depth, and API stability; now, he also asks: Who is holding the funds? Who regulates the platform? Is there a true separation between client assets and platform assets?

Moreover, quantitative teams like Lao K typically do not concentrate their funds in one exchange, but rather distribute them across multiple platforms. Binance, OKX, Bitget, Gate, Coinbase, KuCoin, Hyperliquid, Lao K said, they now have funds on all of these platforms, just at different allocation ratios.

Because he increasingly realizes: You think you're doing arbitrage, but you're potentially taking on credit risk for the exchange.

This is also the most contradictory aspect of quantitative trading.

Theoretically, arbitrage strategies can minimize directional risks—you do not need to predict whether BTC will go up or down tomorrow. Yet, to execute the strategy, you must place funds on the exchanges.

Therefore, what quant truly needs to manage is never just the model.

The model addresses "how to make money," while risk management addresses "whether the money made can be retained."

Nine years ago, when he first started arbitraging, the most important consideration was where to find price differentials. Nine years later, he is still searching for differentials.

Only now, before putting money in, he first thinks: What happens if this exchange disappears tomorrow?

This may be the biggest change he has experienced after nine years in quantitative trading.

Ten Teams, Eight Losses: The "Quant Paradox" Hidden within the Fortress

Lao K conducted an "experiment" using his own funds.

In recent years, he has provided funding to over a dozen external quantitative teams through API sub-accounts, with each investment ranging from 10,000 dollars to 100,000 dollars.

His intention was simple: on one hand, to communicate with peers and explore any worthy new strategies; on the other hand, to make a little investment return on the side.

The two sides agreed that if profits were made, Lao K would give the team 20% to 30% of the profit; if there were losses, all losses would be borne by Lao K.

However, after investing in this way, the results surprised him: About 80% of the fourteen teams ultimately lost money.

Some had to emergency stop losses after a drawdown of 20% to 30%, and some even went bankrupt to zero. Only a handful of teams could consistently generate positive returns over the long term.

This experiment conducted using real money also allowed Lao K to clearly see a paradox hidden in the quantitative industry: Top-tier quantitative teams that can survive bull and bear markets often do not lack funds.

They have their own capital and stable strategy capacity, leaving no significant need for external financing. More importantly, even if you have the money, you might not be able to invest it.

Ironically, those teams that are easy to access and willing to accept external funding are often smaller in scale and have strategies and risk control that are still immature.

The easier it is to invest in something, the less it may be what you truly want to invest in; conversely, the ones truly worth investing in might never be available to you.

How did these teams end up losing money?

Lao K found that many people mistakenly believe that "quantitative" means guaranteed profits. But in fact, quant can only program trading rules but cannot eliminate risks.

Some teams pursued high returns by leveraging altcoins and could instantaneously go bankrupt in extreme conditions; others engaged in stablecoin circular loans, continuously stacking leverage, and once encountering black swan events like "1011," their profits from the past one or two years, even principal, could vanish all at once.

As a result, Lao K later evaluated a quantitative team based on four main aspects:

First, operational time. Have they experienced extreme market conditions like "312," "519," and "1011"?

Second, return rate. Annualized strategies with 100% return sound attractive, but often come with capacity limits and are challenging to maintain over the long term.

Third, maximum drawdown. For the same rate of return, the smaller the drawdown, the healthier the fund's curve typically is.

Fourth, management fees. The actual profits need to account for management fees which are deducted.

"Of course, when we say 'eight out of ten quant teams are losing money,' we refer to the average individuals or those quant teams that are accessible to us," Lao K emphasized.

After nine years, he has seen numerous thousandfold coins as well as the collapse of exchanges, where market-making funds could also double in a day or teams could go bankrupt to zero.

Quantitative trading is neither a myth nor a scam. It merely makes the methods of earning and losing money more programmatic.

Making quick money through luck is not difficult; the challenge lies in surviving in this market for nine years.