The value of the crypto market is equivalent to attention.
Topic: How do Crypto Quantitative Fund Managers Obtain Alpha?
Host
Zheng Naiqian @ZnQ_626
LUCIDA Founder
2019 Bgain Digital Asset Trading League Season 1 Hybrid Strategy Group Champion
2020 TokenInsight Global Asset Quantitative Competition, April Runner-up, May Champion, Season Champion
2021 TokenInsight x KuCoin Global Asset Quantitative Competition, Season Champion
Guests
Ruiqi @ShadowLabsorg
ShadowLabs Founder & DC Capital Investment Director
Managed quantitative products with a scale of over 300 million USD
Market-making consulting advisor for multiple exchanges and well-known projects
Wizwu @wuxiaodong10
RIVENDELL CAPITAL Multi-factor & Subjective Strategy Fund Manager
Computer + finance background
20M non-traditional crypto strategies
Focus on on-chain and off-chain data mining and neutral multi-factor strategies
What does the framework of the fund manager's Alpha strategy look like?
Zheng Naiqian @LUCIDA:
LUCIDA is a multi-strategy hedge fund. We ensure our performance can withstand bull and bear markets by developing various low-correlation diversified strategies.
As an example with our proprietary funds, our profit target is to outperform the spot price increase of Bitcoin in a bull market. Therefore, we first make a macro timing judgment of the market, determining whether the market is at the bottom of a bear market or the top of a bull market. This judgment is very low frequency, roughly on an annual basis.
If we believe the current market is at the bottom of a bear market, we will convert all funds to full Bitcoin positions and hold throughout the entire bull market. Additionally, we will use quantitative strategies such as CTA, multi-factor strategies, and statistical arbitrage strategies to enhance returns. These strategies are also the core source of Alpha during a bull market. We dynamically adjust the capital allocation between these strategies based on the current market environment to ensure efficient use of funds.
If we believe the market has reached the top of a bull market, we will liquidate all Bitcoin positions and convert to USD to weather the bear market. During the bear market, we will also use strategies such as CTA and options volatility arbitrage to increase USD holdings until the next market cycle.
Therefore, all contributions to Alpha include two main categories: 1. Macro timing judgments of bull and bear markets, which is also one of our core competitive advantages. 2. Enhanced returns from quantitative strategies. For example, it is unrealistic to accurately buy at $10,000 and sell at $50,000 for Bitcoin. Therefore, we use quantitative strategies to enhance returns and ensure that we can outperform the increase in Bitcoin's price.
Wizwu:
When it comes to Alpha strategies, it is related to the nature of our fund's capital. We have received a lot of native crypto funds, all of which are in crypto, so we have to passively earn Alpha. Essentially, it is a strategy for increasing returns. Within this strategy, we have multi-factor strategies and some subjective strategies.
As an institution, we need to consider many factors when making subjective trades, including holding periods and the liquidity of smaller coins. These factors limit the number of available assets for us to trade. Holding too many assets can lead to dilution and inability to outperform the market, while holding too few assets leads to competition with project and investment parties. Therefore, our framework is to do everything.
For example, if we discover a factor, different people have different approaches to handling it, including neutral, subjective, and quantitative approaches. This represents different trading approaches. Therefore, I combine subjective and multi-factor approaches. Because there is no precedent for this in the crypto market, we have stock market factor strategies driven by data; there are also value-based strategies, but we have not found them; there are also some futures, especially futures analysis based on inventory and supply and demand relationships. Therefore, all of this relies heavily on our understanding of data and trading clues.
However, we do not have a research department for primary crypto funds, as we do not have as many resources and as broad a perspective as they do. Therefore, we focus on flexibility and data-driven approaches. Different people make different money in the market, similar to the futures market where industries make money from industries, quant makes money from quant, and subjective makes money from subjective. The methodology is different, and the money earned in the end is also different.
Overall, our main focus is on crypto, and we hope that our strategies can achieve a Sharpe ratio of 3-4 and an annualized return of over 10%. We do very little or very low-frequency macro timing. Based on this, we derive factors from market insights, which can be applied to various strategies, including subjective and multi-factor strategies.
In the process of factor mining, we like to transfer some factors from the futures or stock markets for testing and also rely on our own trading experience.
Ruiqi:
We are a purely quantitative and automated team, so when we initially designed the framework for Alpha, we followed highly engineered and highly automated principles. Therefore, we heavily rely on data-driven and execution. We internally divide our Alpha framework into execution Alpha and predictive Alpha.
Crypto exchanges are very decentralized, and there are many investment tools. For example, if I want to assess the risk exposure of a trade, I can choose to trade futures or spot, and I can also choose to trade on different exchanges. Therefore, at the execution level, we compare the costs of different markets, such as futures and spot prices, basis, fees, trading slippage, and borrowing costs. After comprehensive comparison and selection of the lowest costs, we aim to achieve an annualized return of approximately 5% to 20%, which we consider as execution Alpha.
The second part is predictive Alpha, which mainly involves predictions on different levels, periods, and assets, including time-series and cross-sectional predictions. We adjust our risk exposure on different assets based on these predictions.
However, there is a special case where predictive Alpha is somewhat coupled with execution Alpha. For example, if we make a prediction, only about 20% of the problem is solved by the prediction, and the remaining 80% comes from our ability to execute it. This includes order placement, probability analysis of execution, and conditional probability of capital costs, which involve both execution and predictive factors. Overall, we operate within this framework to achieve breakthroughs in our Alpha.
When we perform performance attribution, the contributions of these two types of Alpha are different. For example, as mentioned earlier, the goal of execution Alpha is to outperform the benchmark by 5% to 20%, so it is relatively certain but with limited profit potential. Predictive Alpha is different. For example, some of our high-frequency predictions have very thin profits per trade, which are mixed with a lot of execution Alpha, but for some medium to low-frequency predictions, their contribution to predictive Alpha may be higher.
What is your view of the Crypto market? What kind of market do you think Crypto is?
WizWu:
As mentioned earlier, we should make different money in different markets. We make logical analysis money in futures, and we do the same in Crypto. The characteristic of the Crypto market itself is high volatility. For example, the return on U-based assets, the return on funding rates in a bull market is at least 20% on an annualized basis. Therefore, we need to think about how we can make money based on these characteristics. If we enter with U, we might start with arbitrage. This is a risk-free return from arbitrage.
In the current bull market, the risk-free return rate on Pendle is around 30% to 40%. Assuming we calculate the most accurate Sortino ratio, the expected minimum return is subtracted. After subtracting, as a risk strategy, the remaining return is not much, so this is also a reason for us to do Alpha based on crypto.
My market view is hot money. Wherever there is money to be made and the logic is clear, that's where I'll invest.
This year, the market rotation rhythm in the Crypto market is very similar to the A-share market. In the past five or six years, A-shares have had a main theme every year. For example, there was carbon neutrality earlier, and this year it's AI. However, in my experience and review of the bull and bear markets in the crypto market, there has only been such a main theme this year. This year, there's AI and Meme. Before this, there was no main theme in the crypto market, and it was really a very dull market, which is also a difference between this year and previous years. So, this year, if you can catch AI and Meme in the crypto market, you can make a lot of money.
When capturing the hotspots and sector rotation patterns in the Crypto industry, momentum is a very important part. In addition to data, we also pay attention to sentiment on Twitter. However, if there are few assets, the data we can pay attention to is still related to the value of the assets themselves.
Internally, we have a tool similar to Wind. We have been doing factor analysis for almost two years, and we store market and Twitter sentiment data in it. But we don't pay much attention to sectors because we don't capture sector rotation in this way. Our factors will select coins with good elasticity within the sectors and buy these assets.
Ruiqi:
We believe that Crypto is a highly speculative market, mainly composed of continuous trading and occasional event trading. This is also the reason why we continue to participate in the market.
Compared to other financial assets or markets, it is more suitable to capture the emotional and event-driven trading components. Therefore, this is consistent with our trading advantages.
As the market has developed, there has been increased competition in both trading execution and prediction. However, there are still opportunities with high structural characteristics, which are filled with a lot of emotional and event-driven elements. The market is beginning to undergo structural differentiation.
Firstly, in terms of market predictability, the effectiveness of pricing on traditional assets has been further improved. Specifically, we can see that in the past, a trend might take several hours or even a day or two to ferment, but now a trend may end in 10 minutes, and the significant errors brought by different factors will be quickly corrected. However, we still find good Alpha in new assets.
If we also participate in some Altcoins, we will find that each person's narrative will include some new assets. Whether it's a competition, entrepreneurship, or a new trend, we find that the factors we previously used are still effective for these assets. However, new assets are difficult to obtain. For example, there are some deficiencies in technical implementation, data access, and the stability of trading patterns.
In the Crypto market, how do different factors contribute? What are the underlying sources of returns for these factors?
Wizwu:
One of the characteristics of the Crypto market is the high funding rate, which can be understood as the basis for futures. If we understand them as the same thing, the fluctuation of the monthly basis in the Crypto market is significant. Arbitrage characteristics are built around this, as well as alternative factors that are likely built around this logic.
Additionally, due to the high market volatility and the high elasticity of some coins in differentiation, timing is crucial to making real money. Therefore, we tried momentum and found that neutral momentum can only reach the level of Bitcoin in a bull market. Without timing, it is difficult to see a good excess return. This is also related to the trading mechanism of Crypto.
Furthermore, the data provided by exchanges and some off-exchange data is different from traditional markets. Therefore, many of our excess returns come from these unique characteristics and strategies that have been overplayed in traditional markets.
Ruiqi:
One of the representative emotional factors is the momentum factor, which essentially involves chasing gains and cutting losses. The profit from this factor mainly comes from the market's overreaction.
For example, when retail investors see a certain coin rising, they usually believe that this upward trend will continue, and they rush to buy. At this point, we can ride the wave and profit from it. Additionally, we can engage in momentum reversal trading, based on the judgment of the market's overreaction, to ambush and reverse the operation. The core of these trades is to use the market's overreaction to generate returns.
The profit from event-driven factors mainly comes from the repricing of assets, which requires a certain reaction time. For example, by monitoring data on Twitter or potential data from major events, we can quickly react after an event occurs. For example, when CPI data is released, the price of Bitcoin may experience significant fluctuations. In this case, quick reaction and trading can generate profits.
From the perspective of high-frequency trading, many traders are not sensitive to trading costs, leading them to conduct all trades on a single market when conducting large trades. This behavior can have a significant impact on the market, creating 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.
What are the differences in methodology for obtaining Alpha in the Crypto market compared to traditional financial markets? How can more Alpha be obtained in Crypto?
Zheng Naiqian@LUCIDA:
In recent years, I have clearly felt that people are possibly the most core element of Alpha. Although the Crypto industry has developed a lot, the average level of practitioners in the Crypto industry, especially participants 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 is almost no comprehensive data provider like Wind or 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 model without data?
I think if institutions have a clear advantage in talent and data compared to their peers, it will be a stable source of excess returns.
Wizwu:
Compared to traditional financial markets, the Crypto market has several significant characteristics: high volatility, high elasticity of small coins, and strong speculative sentiment. To obtain Alpha in the Crypto market, strategies must be developed around these characteristics.
A core issue is that the risk-free arbitrage returns in the Crypto market are too high. This is destructive for value factors in the Crypto market because there are very few projects that can bring stable USDT dividends, almost none. Therefore, when we want to calculate value, PE, or P/E ratio, we find that no matter how we calculate it, it is far inferior to the arbitrage returns in U-based assets. Therefore, using value factors from traditional financial markets to measure Alpha in the Crypto market is not feasible.
In the Crypto market, the core value we need to focus on is different from traditional markets. In traditional stock markets, value and P/E ratio factors are core, but in the Crypto market, we may place more emphasis on market sentiment, which is the optimistic estimate of future expectations and everything derived from these expectations.
A specific factor example is the value factor, such as MATIC in Layer 2 (L2) solutions. The change in the number of native token addresses holding 10 to 100 U (USDT) often indicates market trends. When a public chain is about to welcome a popular application or widespread adoption, the increase in these small balance holders is usually a positive signal, which often resonates with market sentiment and prices, and it is also early. Essentially, these addresses represent individuals, representing the issue of more or fewer people. From the perspective of this factor, the balance held in addresses of 10-100 USD types is more like a real user.
Ruiqi:
I have summarized several differences: Information asymmetry caused by market dispersion. The dispersion of the Crypto market leads to information asymmetry. Non-professional investors find it difficult to understand the market situation, making arbitrage opportunities particularly obvious.
Chasing and Market Volatility
In contrast to traditional financial markets, assets in the Crypto market are typically traded across multiple regional markets. This dispersion makes chasing gains and panic selling more common, and frequent switching of attention and irrational trading by investors is more prevalent in the Crypto market. Market Manipulation Market manipulation is more common in the Crypto market than in traditional markets.
For most ordinary investors, it is difficult to utilize this phenomenon for trading or designing trading strategies. However, for some high-frequency trading firms, they can engage in market manipulation on a larger scale than in traditional markets to gain Alpha. This behavior is illegal in traditional markets and can lead to imprisonment.
Differences in Asset Management Products in the Crypto Market
Zheng Naiqian@LUCIDA:
I have found that over 80% of secondary teams are mainly engaged in very neutral arbitrage strategies, leading to a high degree of homogenization among strategies.
From an investment perspective, the principles of these strategies are not complex, and if you focus on lower frequency trading, you don't need too much effort in trade execution. This has led to over 80% of products being involved in statistical arbitrage, so it seems inappropriate to invest in other strategies such as CTA or multi-factor strategies compared to statistical arbitrage. Even high-frequency trading, when you optimize all your trading details, still has a significant deviation in managing scale compared to statistical arbitrage. So, do you think statistical arbitrage products will become the mainstream of the entire market in the future?
Wizwu:
Not only in the Crypto market, but also in traditional financial markets, bond trading is a major component. Trading volumes of bonds at different levels are also not low, so arbitrage trading will continue to exist. As long as it can be operated under some semi-compliant conditions, the arbitrage returns in the Crypto market can at least reach two to six times that of traditional markets, providing a very 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 this type of strategy only after the arbitrage returns come down. At that time, the Sharpe ratio of our strategy will look very good. Currently, arbitrage returns are calculated in U terms, thanks to the unified accounts of exchanges, we can also run similar strategies using coin terms. So our current direction is to run arbitrage in U and risk in coins, which is the best allocation method.
Ruiqi:
I basically agree with Wiz's viewpoint.
Firstly, the highly dispersed market and the so-called barriers to entry make it difficult to resolve these issues in the next two to three years. Therefore, the arbitrage space will continue to exist in the visible two to three years. Even if the arbitrage space decreases, the trading volume and capital capacity of arbitrage trading will still be the major component of the market.
However, by that time, arbitrage may not exist in the form of asset management products. It will be more of a proprietary trading by high-frequency quant teams, mainly for these teams to directly take the profits without additional profit distribution to the market. For some asset management projects, they will have to settle for providing adjusted risk-return ratios, which are still relatively okay in terms of cost-effectiveness, such as statistical arbitrage and CTA strategies. After two to three years, such a situation may begin to emerge.
Zheng Naiqian@LUCIDA:
The structure of Crypto asset management products is also significantly different from A-shares. I have observed that the most mainstream products in A-shares are index funds, regardless of whether they are benchmarked against the 300, 500, or 1000 broad-based indexes. These products based on index funds should be the best-selling. Most of the underlying factors of index funds are implemented using multi-factor models.
But I have found that such products are almost non-existent in the Crypto market. I know that there are probably less than 10% of teams developing multi-factor strategies. Why is the proportion of teams developing multi-factor strategies so low?
Wizwu:
The reason is that the returns on USDT in the market are just too high. For example, on PENDLE, I almost exclusively buy USDT. In this situation, I wouldn't choose my own strategy. Because when my strategy is reduced by 30% of the risk and then divided by volatility, its performance is not even as good as the Sharpe ratio and other indicators in the traditional futures market.
So, I think in a market where risk-free returns are so high, everyone will naturally choose risk-free returns. Calculating in this way, the proportion of standard strategy measurement needs to subtract a risk-free return. When we calculate using the true risk-free return in this market (annualized 30%), everything becomes futile, and it makes no sense no matter how we calculate it.
Our multi-factor strategy has become more diversified. Initially, when designing it, we did indeed design it based on a neutral multi-factor strategy in A-shares or traditional futures. But later, it gradually became more diversified and subjective, incorporating more subjective factors. I think the core reason is that the market's drawdown period is very short, and the changes are very fast. In this situation, implementing a multi-factor strategy has some framework issues. We cannot prove that a certain factor is long-term effective based on recent market trends.
In traditional markets, we may explore a factor, and it needs to be tested in both A-shares and 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. But in the Crypto market, it is difficult to have such validation opportunities for using this factor to implement a neutral strategy. We may only be able to look at one or two years of backtesting, which is not very 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 doing trend trading on Bitcoin and Ethereum. However, there are very few teams doing trend trading on 100 assets. There are more people doing time-series trading, but fewer doing cross-sectional trading, and this is what I have observed.
If we were to attribute it, I think there are several main reasons:
Firstly, the issue of data length. Most assets may have only experienced one cycle, with 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, and very few can complete several cycles and maintain activity and liquidity. Basically, only Bitcoin and Ethereum can do so. Others, like Solana, also became inactive for a long time and only recently became active.
Thirdly, relatively speaking, the effectiveness of time-series factors in practice may be more significant than the effectiveness of cross-sectional factors. The underlying logic is the long-term response to emotional momentum, and we can plan it very well using a traditional trend trading framework. However, the relative strength and weakness of cross-sectional factors are unstable because many assets themselves are unstable. They are not like traditional commodities or stocks, which have experienced multiple bull and bear market cycles, and the relative strength and weakness comparison is relatively stable. In the Crypto market, this wave of assets may disappear in the next wave, and it is impossible to verify whether the relative strength and weakness comparison exists.
How do you measure the value of Crypto assets? Where does the value of Crypto assets lie?
Ruiqi:
From the current situation, the value of 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's entirely the same. Simply put, this is more like a product of an attention economy. In the long run, we expect and many practitioners and VCs are also working towards a direction where future value is as much as possible reflected in the competitiveness of actual applications and ecosystems. But at least for now, the market's status is not entirely like this.
Bonus: How do you currently view the market? How do you think Bitcoin will perform in the near future? (Subjective, off-the-cuff, no responsibility kind of answer).
Wiz:
Off-the-cuff, at this position, there isn't much room for upward movement, and even if it breaks a new high, the increase may only be around 30%, and then it may have to pull back. From the current level, I think major risk assets globally may not have much room for significant upward movement. This is really off-the-cuff, and it's quite revealing.
Ruiqi:
I would be more optimistic because I don't think the interest rate cut has started yet. Although I didn't have much faith in Bitcoin before, I now basically consider myself half a Bitcoin believer. So, I think it's still possible for it to reach 150,000 within the next two years of this bull market cycle.
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