Article Rewritten:
Theme: How do Crypto Quantitative Fund Managers Obtain Alpha?
Host
Zheng Naiqian @ZnQ_ 626
LUCIDA Founder
Champion of the 2019 Bgain Digital Asset Trading League Season 1 Mixed Strategy Group;
Runner-up in April and champion in May of the 2020 TokenInsight Global Asset Quantitative Competition, Composite Strategy Group; Season champion of the 2020 TokenInsight x KuCoin Global Asset Quantitative Competition, Composite Strategy Group;
Guests
Ruiqi @ShadowLabsorg
Founder of ShadowLabs, Investment Director of DC Capital
Quantitative product management scale exceeds $300 million
Market-making consulting advisor for multiple exchanges and well-known projects
Wizwu @wuxiaodong 10
Fund Manager of RIVENDELL CAPITAL Multi-Factor Subjective Strategy Fund
Computer and finance background
20M non-traditional crypto strategies
Focus on on-chain and off-chain data mining and neutral multi-factor strategies
What is the framework of a fund manager’s Alpha strategy?
Zheng Naiqian @LUCIDA:
LUCIDA is a multi-strategy hedge fund. We ensure that our performance can withstand bull and bear markets by developing various low-correlation diversified strategies.
Using our proprietary funds as an example, our goal is to outperform the spot price of Bitcoin in a bull market. Therefore, we first perform macro timing by determining whether the market is at the bottom of a bear market or the peak of a bull market. This judgment is low frequency, typically once a year.
If we determine that the current market is at the bottom of a bear market, we will convert all our funds into full positions of Bitcoin and hold them throughout the bull market. In addition, we use quantitative strategies such as CTA, multi-factor strategies, and statistical arbitrage to enhance returns. These strategies are also the core source of Alpha in a bull market. We also dynamically adjust the allocation of funds between these strategies based on the current market environment to ensure optimal capital utilization.
If we determine that the market has reached the peak of a bull market, we will liquidate all our Bitcoin holdings and convert them into USD to weather the bear market. During the bear market, we also use strategies such as CTA and options volatility arbitrage to increase our USD holdings until the next market cycle.
Therefore, all contributions to Alpha can be divided into two categories: first, macro timing judgments of bull and bear markets, which is one of our core competitive advantages. Second, the enhancement of returns through quantitative strategies. For example, it is not realistic to accurately buy at $10,000 and sell at $50,000 as Bitcoin rises. Therefore, we use quantitative strategies to enhance returns and ensure that we can outperform the increase in the price of Bitcoin.
Wizwu:
When it comes to Alpha strategies, it is related to the nature of our fund’s assets. We have received a lot of native funds from the crypto industry, which are all coins. Therefore, we have to passively earn Alpha, which is essentially a strategy for generating returns. In this strategy, we have multi-factor strategies and 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. If we hold too many assets, it will be difficult to outperform the market. If we hold too few assets, we will have to compete with project parties and investors. Therefore, our framework is to do everything.
For example, if we discover a factor, different people have different approaches to deal with it, including neutral, subjective, and quantitative approaches. This represents different trading strategies. Therefore, we combine subjective and multi-factor strategies together. In the crypto market, there is no precedent for this. Therefore, our focus is on flexibility and data-driven approaches.
However, we do not have a research department like the top-level funds in the crypto industry because we do not have as many resources and a broad vision as they do. Therefore, our approach is to be flexible and data-driven. Different people make different profits in the market, similar to the futures market where industries make money from industries, and quant funds make money from quant strategies. The methodology is different, and the profits are different.
Overall, our main focus is on coin-based strategies. We hope that our strategies can achieve a Sharpe ratio of 3-4 and an annualized return of over 10%. We do very little macro timing or do it at a very low frequency. Based on this, we derive factors through insights into the market, which can be applied to various strategies, including subjective and multi-factor strategies.
During the factor mining process, we like to adopt some factors from the futures or stock markets for testing. We also have our own trading experience.
Ruiqi:
We are a purely quantitative and fully automated team. Therefore, when we initially designed the structure framework for Alpha, we followed the principles of high engineering and high automation, relying heavily on data-driven and execution-driven approaches. We internally divide our Alpha framework into execution Alpha and predictive Alpha.
The crypto market is highly decentralized, with numerous trading tools. For example, if I want to hedge a trading position, I can choose to trade futures or spot, or trade on different exchanges. Therefore, at the execution level, we compare the costs of different markets, such as futures prices, basis, fees, slippage, and borrowing costs. After comprehensive comparison, we choose the tools with the lowest costs. In this part, we aim to achieve an annualized return of 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. Based on these predictions, we adjust our risk exposure in different assets.
However, there is a special situation where predictive Alpha is somewhat coupled with execution Alpha. For example, when I make a prediction, it may only solve 20% of the problem, and the remaining 80% comes from whether I can execute it successfully. This includes order placement techniques, probability analysis of execution, and conditional probabilities of capital costs. These factors involve both execution and prediction elements, and we work within this framework to achieve breakthroughs in Alpha.
When we perform performance attribution, the contributions of these two types of Alpha are different. For example, as mentioned earlier, the target of execution Alpha is to outperform the benchmark by 5% to 20%, so this part is relatively certain but has limited profit potential. Predictive Alpha is different. For example, some high-frequency predictions may have very small profits per trade. This is mixed with many execution Alpha factors, but for some medium to low-frequency predictions, they may have a higher proportion in predictive Alpha.
What is your market outlook for Crypto? What do you think about the Crypto market?
WizWu:
As mentioned earlier, we should make money in different markets. We make logical analysis profits in futures markets, and we do the same in the crypto market. The crypto market itself is characterized by high volatility. For example, the funding rate of U-based returns has a minimum annualized return of about 20% in a bull market. Therefore, to make money, we need to think about how to make use of these characteristics. If we enter as U, we may start with arbitrage. This is a risk-free profit in the arbitrage category.
In the current bull market, the risk-free rate of return on Pendle is around 30% to 40%. Assuming we calculate the most accurate Sortino ratio, what is subtracted at the end is the expected minimum return. After subtracting this, the remaining profit from a risk strategy is not much, so this is also a reason why we focus on coin-based Alpha.
My market view is “hot money,” wherever there is money to be made, where the logic is clear, that’s where I will go.
The market rotation rhythm in the crypto market this year is similar to that of the A-share market. In the past five to six years, the A-share market had a main theme every year. For example, there was carbon neutrality early on, and AI this year. However, in the history of the crypto market that I have experienced and reviewed, there were only such main themes this year. AI and Meme. Before this, the crypto market did not have main themes, and it was truly a dull market. This is also a difference between this year and the past. So, if you can catch AI or Meme in the crypto market this year, you can make a lot of money.
When capturing the hotspots and sector rotation patterns of the Crypto industry, momentum is very important. In addition to data, we also pay attention to sentiment on Twitter. However, if there are few targets, the data we can focus on is the intrinsic value of the targets themselves.
We have an internal tool similar to Wind. We have been doing factor analysis for almost two years, and we store market and sentiment data from Twitter. However, we don’t pay much attention to sectors because we don’t capture sector rotation in this way. Our factors identify coins with good elasticity within sectors and buy these assets.
Ruiqi:
We believe that Crypto is a highly speculative market, mainly driven by 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, Crypto has more emotional and event-driven trading. This makes it suitable for capturing through quantitative methods, which aligns with our trading advantage.
As the market has developed to this day, competition has intensified, whether in terms of execution or prediction. Currently, there is a wide range of options, and there are still high-structure opportunities. The sources of these structural trading opportunities are still filled with emotions and events. The market has started to differentiate structurally.
Firstly, the predictability of the market has increased in terms of pricing efficiency in older assets. Specific characteristics we can observe are: in the past, a trend could take several hours or even a day or two to develop, but now a 10-minute trend can end, and the large errors caused by different factors can be quickly corrected. However, we still find good Alpha in new assets.
If we participate in Altcoins, we find that each person’s narrative includes new assets, whether it’s contests, entrepreneurship, or new trends. We find that the factors we used on these assets are still effective. However, it is difficult to obtain new assets. For example, technology implementation, data access, and the stability of trading patterns are somewhat lacking.
The contributions of different factors in the Crypto market vary. These factors’ underlying sources of returns are different.
Wizwu:
The characteristic of the Crypto market is high funding rates, which can be understood as basis in futures. If we understand them as the same thing, the volatility of basis in the Crypto market is significant. Arbitrage strategies and other alternative factors are built around this logic.
Additionally, due to the high market volatility, some coins within differentiation have high elasticity, so timing is essential to obtain profits. Therefore, we tried momentum and found that neutral momentum can only achieve the level of Bitcoin in a bull market. Without timing, it is difficult to achieve significant excess returns. This is also related to the trading mechanisms in the Crypto market.
Furthermore, the data provided by exchanges and some off-market data are different from traditional markets. Therefore, many of our excess returns come from these unique factors and strategies that have been overplayed in traditional markets.
Ruiqi:
One representative of sentiment factors is the momentum factor, which essentially involves chasing trends. The profits from this factor mainly come from market overreactions.
For example, when retail investors see a coin rising, they usually believe that the uptrend will continue and start buying. At this point, we can take advantage of the momentum and profit from it. In addition, we can engage in momentum reversal trading, which is based on judgments about market overreactions. These trades revolve around utilizing market overreactions to generate profits.
Profits from event factors mainly come from the repricing of assets, which takes some 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, Bitcoin prices may experience significant fluctuations. In such cases, quick reactions and adjustments canEarning profits through trading is possible. In the perspective of high-frequency trading, many traders are insensitive to trading costs, which leads them to conduct all their trades in a single market when making large trades. This behavior can have a significant impact on the market, resulting in 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 the crypto market?
Zheng Naiqian from LUCIDA:
In recent years, I have clearly felt that people are the core element of alpha. Although the crypto industry has developed a lot, the average level of practitioners, especially participants in the secondary market, is significantly different from that of 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 scattered. Obtaining data is a headache for many teams, but how can you build models without data?
I think if institutions have obvious advantages 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, we must explore strategies based on these characteristics.
One core issue is that 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 provide stable USDT dividends, almost none. So when we try to calculate value, P/E ratio, or market earnings ratio, we will find that no matter how we calculate, they are far less than the arbitrage returns in terms of USDT. Therefore, it is not feasible to measure alpha in the crypto market using value factors from traditional financial markets.
In the crypto market, we need to focus on different core values compared to traditional markets. In traditional stock markets, value and earnings factors are core, while in the crypto market, we may pay more attention to market expectations, which are optimistic estimates of the future and everything derived from achieving these expectations.
A specific example of a factor is the value factor, such as the change in the number of native token addresses holding 10 to 100 USDT in Layer 2 (L2) solutions like MATIC. This often indicates market trends. When a public chain is about to see explosive applications or large-scale adoption, the increase in these small holders is usually a positive signal. They are often in resonance with market sentiment and prices, and it happens early. In terms of the characterization of this factor, addresses holding balances in the range of $10 to $100 are more like real users.
Ruiqi:
I have summarized several differences: information asymmetry caused by market fragmentation, chasing trends and market volatility, market manipulation, and the difference in asset management product patterns between the crypto market and traditional financial markets.
Zheng Naiqian from LUCIDA:
I have noticed that more than 80% of secondary market teams are engaged in neutral arbitrage strategies, so the homogenization between strategies is very serious.
From an investment perspective, the principles of these strategies are not complicated, and if you do low-frequency trading, you don’t need to spend too much energy on trade execution. This leads to more than 80% of products being involved in arbitrage, so it is not appropriate to do CTA, options, or multi-factor strategies compared to statistical arbitrage in terms of input-output ratio. This also applies to high-frequency trading. You can optimize all the trading details by switching equipment, but compared to this arbitrage, there is a significant deviation in managing scale. So do you think arbitrage will become the mainstream of the entire market in the future?
Wizwu:
Not only in the crypto market, but in traditional financial markets, bond trading is also a major part. The trading volumes of bonds at different levels are not low, so arbitrage trading will always exist. As long as we can operate under certain semi-compliant conditions, the arbitrage returns in the crypto market can at least reach two to six times that of traditional markets, providing 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 high-capacity choices. These strategies may be truly recognized by the market after the arbitrage returns decrease. At that time, when we look at the Sharpe ratio of our strategies, it will look very good. Now, the arbitrage returns are calculated in terms of USDT. Thanks to the unified accounts of exchanges, we can also run similar strategies using coin-based calculations. So our current direction is to run arbitrage with USDT and manage risks with coins, which is the best allocation method.
Ruiqi:
I basically agree with Wiz’s viewpoint.
First, the market is highly fragmented, and there are barriers to entry for funds. These problems may be difficult to solve in the next two to three years. Therefore, in the visible future, there will still be arbitrage opportunities. Even if the arbitrage space decreases, the trading volume and capacity of arbitrage trading will still be the major part of the market.
However, by then, 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 may be the situation. For some asset management projects, they will settle for providing risk-reward ratios that have been adjusted and have a relatively good cost-effectiveness, such as statistical arbitrage and CTA strategies. After two to three years, such conditions may begin to emerge.
Zheng Naiqian from LUCIDA:
The architecture of crypto asset management products is significantly different from that of A-shares. I have observed that the most mainstream products in A-shares are index enhancement, benchmarked against broad-based indexes such as the CSI 300, 500, or 1000. Products based on index enhancement should be the best sellers. The underlying of most index enhancements is achieved through multi-factor models.
But I found that these types of 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 too high. For example, on PENDLE, I almost buy all USDT. In this case, I won’t choose my own strategy because when my strategy is adjusted for 30% risk and divided by volatility, its performance is even worse than the Sharpe ratio and other indicators in the traditional futures market.
So, I think when risk-free returns in the market are so high, everyone will naturally choose risk-free returns. Calculating the proportion of strategy standards must subtract risk-free returns. When we calculate using the market’s true risk-free return (30% annualized), everything becomes futile no matter how we calculate.
Our multi-factor strategies have become more diversified. Initially, they were designed based on neutral multi-factor strategies in A-shares or traditional futures. But later, they gradually became more diverse and subjective factors were added. I think the core reason is that the market’s drawdown cycle is short and changes very quickly. In this case, implementing multi-factor strategies faces some framework problems. We cannot only look at the recent two years of market trends to prove that a certain factor is long-term effective.
In traditional markets, we may explore a factor and test it in both A-shares and US stocks. If it is effective in US stocks for 20 years and in A-shares for 5 years, we can say that 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 may only be able to 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 there are very few teams doing trend trading on 100 different assets. 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:
First, the issue of data length. Most assets may have only experienced one cycle, and there is no longer data to verify and backtest.
Second, even for assets that have experienced multiple cycles, such as EOS, it became inactive after 2017 and 2018, making it difficult to include it in the pool of assets. There are many similar assets in the crypto market. They can complete several cycles and maintain activity and liquidity, but they are very few. Basically, only Bitcoin and Ethereum. Other assets like Solana also remained inactive for a long time and only recently became active.
Third, relatively speaking, the effectiveness of time series factors may be more significant in practice than cross-sectional factors. The underlying logic is a long-term response to emotional momentum, and we can plan it well using traditional trend trading frameworks. However, the stability of relative strength factors in cross-sectional trading is unstable because many assets themselves are unstable. They are not like traditional commodities or stocks that have experienced multiple bull and bear market cycles, and the relative strength comparison is relatively stable. In the crypto market, these assets in this wave may disappear in the next wave, and it is impossible to verify the existence of relative strength comparison.
What do you think is the scale to measure the value of crypto assets? Where is the value of crypto assets?
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 attract attention, it can gain value. This may have some similarities with the market momentum mentioned by Wiz, but I think it is not exactly the same. Simply put, it is more like a product of an eyeball economy. In the long run, we expect and many practitioners and VCs are working to promote 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.
Easter Egg: How do you view the market now? What do you think the future holds for Bitcoin in the short term? (This is a subjective, speculative question.)
Wiz:
Speculatively speaking, it is shaking at this position, and there is not much upward space. Even if it breaks a new high, the increase may only be about 30%, and then it may have a correction. Based on the current level, I think major global risk assets may not have much upward potential. This is a truly speculative answer, very exposed.
Ruiqi:
I will be more optimistic because I think the interest rate cut has not started yet. Although I didn’t have much faith in Bitcoin before, I am now basically a half-believer in Bitcoin. So, I think it is 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 and mainly trades CTA/statistical arbitrage/option volatility arbitrage strategies, currently managing a scale of $30 million.
Falcon (https://falcon.lucida.fund/) is a next-generation Web3 investment infrastructure incubated by Lucida in June 2022. It is based on a multi-factor model and helps users “select,” “buy,” “manage,” and “sell” crypto assets.
