Earning profits through trading is possible. In the perspective of high-frequency trading, many traders are insensitive to trading costs, resulting in them conducting all their trades on a single market when conducting large-scale transactions. This behavior can significantly impact the market and create arbitrage opportunities. Liquidity factors are long-term effective in high-frequency markets and are one of the important tools for fund managers to obtain Alpha.
Compared to traditional financial markets, what are the differences in methodology for obtaining Alpha in the Crypto market? How can we obtain more Alpha in Crypto?
Zheng Naiqian from LUCIDA:
In recent years, I have clearly felt that people are the most essential element for Alpha. Although the Crypto industry has developed a lot, the average level of practitioners in the Crypto industry, especially in the secondary market, is significantly different from the A-share market.
The second point is data. The infrastructure of this market is very poor. There are almost no comprehensive data providers like WIND and Bloomberg in the A-share market. The data quality is poor and highly dispersed. Obtaining data is a headache for many teams, but how can you build models without data?
I think if institutions have a clear advantage over their peers in terms of talent and data, it will be a stable source of excess returns.
Wizwu:
Compared to traditional financial markets, the Crypto market has several notable characteristics: high volatility, high elasticity of small currencies, and intense speculative sentiment. To obtain Alpha in the Crypto market, we must explore strategies based on these characteristics.
One core issue is that risk-free arbitrage profits in the Crypto market are too high. This is destructive to the value factors in the Crypto market because there are very few projects that can provide stable USDT dividends, almost none. Therefore, when we try to calculate value, PE ratio, or market-to-earnings ratio, we will find that no matter how we calculate, they are far inferior to arbitrage profits in U terms. Therefore, it is not feasible to measure the Alpha of the Crypto market using value factors from traditional financial markets.
In the Crypto market, we need to focus on core values that are different from traditional markets. In traditional stock markets, value and PE ratio are core factors, while in the Crypto market, we may pay more attention to market expectations, optimistic estimates of the future, and everything derived from these expectations.
A specific example of a factor is the value factor, such as the change in the number of addresses holding 10 to 100 U (USDT) of the native token in Layer 2 (L2) solutions like MATIC. This often indicates certain market trends. When a public chain is about to have a popular application or large-scale adoption, the increase in these small holders is usually a positive signal. It tends to resonate with market sentiment and prices, and it is also early. These addresses essentially represent individuals, the issue of whether there are many or few people. From the perspective of this factor, addresses holding balances in the range of $10 to $100 U are more like real users.
Ruiqi:
I have summarized several differences: information asymmetry caused by market decentralization, chasing trends and market volatility, market manipulation, and differences in the asset management product landscape between the Crypto market and traditional financial markets.
Zheng Naiqian from LUCIDA:
I have noticed that more than 80% of secondary teams are engaged in neutral arbitrage strategies, resulting in a serious homogenization of strategies.
From an investment perspective, the principles of these strategies are not complex, and if you focus on low-frequency trading, you don’t need too much effort in trade execution. This leads to more than 80% of the products being involved in arbitrage, making it inappropriate to engage in CTA, options, or multi-factor strategies compared to statistical arbitrage. Even for high-frequency trading, optimizing all trading details by transferring equipment, there is still a significant deviation in managing scale compared to this type of arbitrage. So, do you think arbitrage will become the mainstream of the entire market in the future?
Wizwu:
Not only in the Crypto market, in the traditional financial market, bond trading is also a big part. The trading volume of bonds at different levels is not low, so arbitrage trading will always exist. As long as it can be operated under certain semi-compliant premises, the arbitrage returns in the Crypto market can be at least two to six times higher than those in traditional markets, providing a high capacity and profit space for arbitrage trading, so this situation will continue to exist.
As for other strategies, such as CTA strategies, they are also a high-capacity choice. The market may truly recognize these strategies after the arbitrage returns decrease, and at that time, when we look at the Sharpe ratio of our strategies, it will be very good. Currently, arbitrage returns are calculated based on U, thanks to the unified accounts of exchanges, we can also run similar strategies using coin denominations. So our current approach is to run arbitrage with U and manage risks with coins, which is the best allocation method.
Ruiqi:
I generally agree with Wiz’s viewpoint.
First, the market is highly decentralized, and there are barriers to entry for funds. These problems may be difficult to solve in the next two to three years. Therefore, within the visible two to three years, there will still be arbitrage opportunities. Even if the arbitrage space decreases, the trading volume and fund capacity of arbitrage trading will still dominate the market.
However, at that time, arbitrage may not exist in the form of asset management products. It will be more self-operated by high-frequency quantitative teams, and they will mainly keep the profits for themselves without additional profit distribution to the market. This will likely be the situation. For some asset management projects, they will settle for providing adjusted risk-reward ratios that are still cost-effective, such as statistical arbitrage and CTA strategies. After two to three years, such conditions may start to appear.
Zheng Naiqian from LUCIDA:
The architecture of Crypto asset management products is significantly different from A-shares. I have observed that the most popular products in A-shares are index funds, regardless of whether they are benchmarked against the CSI 300, CSI 500, or CSI 1000. These index-based products are the best sellers. The majority of index funds are implemented based on multi-factor models.
But I found that these products are almost non-existent in the Crypto market. As far as I know, there are probably less than 10% of teams developing multi-factor strategies. Why is the proportion of teams developing multi-factor strategies so small?
Wizwu:
The reason is that the returns on USDT in the market are too high. For example, on PENDLE, I almost buy only USDT. In this case, I won’t choose my own strategy. Because when my strategy’s risk is reduced by 30% and divided by volatility, its performance is even worse than the Sharpe ratio and other indicators in the traditional futures market.
Therefore, I think in a market where risk-free returns are so high, everyone will naturally choose risk-free returns. When measuring the standard of a strategy, the proportion based on risk-free returns needs to be subtracted. When we calculate using the true risk-free return of this market (30% annualized), everything becomes futile, and no matter how we calculate, it doesn’t make sense.
Our multi-factor strategy becomes more diversified. Initially, it was designed based on neutral multi-factor strategies in A-shares or traditional futures. But later on, it gradually became more diversified and included more subjective factors. I think the core reason is that the market’s drawdown cycles are short, and changes happen very quickly. In this case, implementing multi-factor strategies faces some framework issues. We can’t just look at the recent two years of market trends to prove that a factor is long-term effective.
In traditional markets, we may explore a factor and test it in A-shares as well as US stocks. If it is effective in US stocks for 20 years and in A-shares for 5 years, then we can say it is an effective factor and can be used for large-scale operations. However, in the Crypto market, it is difficult to have such verification opportunities with this factor. We can only look at one or two years of backtesting, which is not reasonable in terms of the framework.
Ruiqi:
My perception may be different, and it also depends on our understanding of this framework.
What I have observed is that there are more people engaged in time-series trading on mainstream coins, such as Bitcoin and Ethereum. But when it comes to conducting trend trading on 100 assets, there are very few teams doing it. There are more teams doing time-series trading, but fewer doing cross-sectional trading. This is the phenomenon I have observed.
If we want to attribute it, I think there are several reasons:
Firstly, the issue of data length. Most assets may have only experienced one cycle, and there is no longer data to verify and backtest.
Secondly, even for assets that have experienced multiple cycles, such as EOS, it became inactive after 2017 and 2018, making it difficult to be included in the pool of assets. There are many similar assets in the Crypto market. Very few assets can go through several cycles and maintain activity and liquidity. Basically, there are only Bitcoin and Ethereum. Others like Solana were also dormant for a long time and only recently became active.
Thirdly, relatively speaking, the effectiveness of time-series factors may be more significant in practice than cross-sectional factors. The underlying logic is a response to emotional momentum that exists in the long term, and we can plan it well using traditional trend trading frameworks. On the other hand, the stability of relative strength factors in a cross-sectional context is unstable because many assets themselves are not stable. Unlike traditional commodities or stocks that have gone through multiple bull and bear market cycles, the relative strength comparison is relatively stable. In the Crypto market, these targets in this wave may disappear in the next wave, making it impossible to verify the existence of relative strength comparisons.
What do you think is the scale used to measure the value of Crypto assets? Where does the value of Crypto assets lie?
Ruiqi:
Based on the current situation, the value in the Crypto market is equivalent to attention. In other words, it is currently an attention-driven market. Regardless of the underlying logic of a project, as long as it can gain attention, it can gain value. This may have some similarities to the market momentum mentioned by Wiz, but I don’t think it is exactly the same. Simply put, it is more like a product of an economy that seeks attention. In the long run, we expect and many practitioners and VCs are also working towards a direction where future value is reflected in the competitiveness of actual applications and ecosystems. But at least for now, the market’s state is not entirely like this.
Bonus: How do you view the market now? What do you think the future trend of Bitcoin will be? (Subjective opinions without responsibility)
Wiz:
Based on a random opinion, it is shaking at this position, and there is not much upward space. Even if it breaks new highs, the increase may only be about 30%, and then it may experience a correction. From the current level, I think there is not much upward space for major global risk assets. This is a truly random opinion, and it’s quite revealing.
Ruiqi:
I will be more optimistic because I think the interest rate cut has not started yet. Although I didn’t have a belief in Bitcoin before, I now consider myself a half-believer in Bitcoin. Therefore, I think it is still possible to reach 150,000 within this bull market cycle in two years.
About LUCIDA FALCON
Lucida (https://www.lucida.fund/) is a leading quantitative hedge fund that entered the Crypto market in April 2018. It mainly trades CTA, statistical arbitrage, options volatility arbitrage, and other strategies, and currently manages $30 million.
Falcon (https://falcon.lucida.fund/) is a new generation of Web3 investment infrastructure. It is based on a multi-factor model and helps users “select,” “buy,” “manage,” and “sell” crypto assets. Falcon was incubated by LUCIDA in June 2022.
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