LUCIDA: How do Crypto quantitative fund managers obtain Alpha?

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This AMA topic and guest introduction

Topic: How do Crypto quantitative fund managers obtain Alpha?


Zheng Naiqian @ZnQ_ 626

  • LUCIDA Founder

  • 2019 Bgain Digital Asset Trading League Season 1 Mixed Strategy Group Champion;

  • 2020 TokenInsight Global Asset Quantitative Competition, runner-up in April, champion in May, and third place in the composite strategy group;

  • 2021 TokenInsight x KuCoin Global Asset Quantitative Competition, third place in the compound strategy group season;


Ruiqi @ShadowLabsorg

  • Founder of ShadowLabs Investment Director of DC Capital

  • Quantitative product management scale exceeds US$300 million

  • Market making consultant for many exchanges and well-known projects

Wizwu @wuxiaodong 10

  • RIVENDELL CAPITAL Multi-factor subjective strategy fund manager

  • Computer + Finance composite 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 a fund manager’s Alpha strategy look like?

Naiqian Zheng@LUCIDA:

LUCIDA is a multi-strategy hedge fund. We develop various low-correlation diversified strategies to ensure that our performance can survive bull and bear markets.

Let me take proprietary funds as an example. Our profit goal is to outperform the spot price increase of Bitcoin in a bull market, so we will first do a macro market timing, that is, determine whether the market is at the bottom of a bear market and the top of a bull market. This judgment is very low frequency, probably in years.

If we believe that the current market is at the bottom of a bear market, we will convert all our funds into full positions in Bitcoin and hold them throughout the bull market. On this basis, we will use quantitative strategies such as CTA, multi-factor strategies, and statistical arbitrage strategies to enhance returns, which are also the core sources of Alpha in a bull market. At the same time, we will dynamically adjust the capital allocation between these strategies according to the current market environment to ensure the utilization rate of funds.

If we think the market has reached the top of the bull market, we will sell all our Bitcoins and convert them into US dollars to survive the bear market. We will also use strategies such as CTA and option volatility arbitrage in the bear market to increase the amount of US dollars until the next market cycle.

Therefore, all Alpha contributions include two categories: 1. Macro-timing judgment of bull and bear markets, which is also one of our core competitiveness. 2. Enhanced returns from quantitative strategies. For example, if Bitcoin rises from 10,000 to 50,000, it is unrealistic to accurately buy the bottom at 10,000 and sell at 50,000. Then we will use quantitative strategies to enhance returns and ensure that we can outperform the rise of Bitcoin.


Speaking of Alpha strategy, it is related to the capital attributes of our fund. We have received a lot of native funds from the cryptocurrency circle, all of which are cryptocurrencies, so we have to passively earn Alpha, which is essentially an index-added strategy. In this index-added strategy, we have multi-factor strategies and some subjective strategies.

As an institution, we need to consider many things when doing subjective trading, including holding period, liquidity of small coins, etc. These factors mean that we have fewer targets to choose from. If we hold too many positions, it will be easy to be dispersed and we will not be able to outperform the market; if we hold too few positions, we will have to compete with the project investors, so our framework is to do everything.

For example, if we find a factor, different people have different ways of dealing with it, some are neutral, some are subjective, and some are quantitative, which represent different trading ideas, so I count subjective and multi-factor together. Because there is no such precedent in the Crypto market, for us, there are both factor strategies in the stock market, which are data-driven; there are also value-based ones, but we haven’t found them; there are also some futures, especially the analysis ideas of inventory and supply and demand in futures. So all of this depends on our ability to understand data and trading clues.

But we don’t have the investment research department of the first-tier funds native to the cryptocurrency circle, because we don’t have as many resources and such a broad vision as they do. We focus on being flexible and data-driven. So different people in the market make different money, which is a bit like the futures market. The industry makes industry money, the quantitative makes quantitative money, and the subjective makes subjective money. The methodologies are different, and the money earned in the end is also different.

Generally speaking, we mainly use the currency standard. If we use the currency standard, we hope that our strategy can reach a Sharpe ratio of 3-4, and the annualized return should reach more than 10%. Macro timing will be done less or very low frequency. Based on this, we derive factors through some insights into the market. These factors can be applied to various strategies, including subjective, multi-factor, and so on.

In the process of digging out factors, we like to move some factors from the futures or stock markets for testing, and we also have our own trading experience.


We are a purely quantitative and automated team, so when we first designed the Alpha structure framework, we followed the principles of high engineering and high automation, so we rely heavily on data-driven and execution. We internally divide our Alpha framework into execution Alpha and prediction Alpha.

Crypto exchanges are very scattered, and there are many investment tools. For example, if I want to obtain the risk exposure of a transaction, I can choose to trade futures or spot, or I can choose to trade on different exchanges. So we will compare the capital costs of different markets at the execution level, such as the price of futures and spot, basis, handling fees, transaction slippage, borrowing costs, etc. After comprehensively comparing different costs, we will try to choose the lowest cost tool. In this part, we can achieve an annualized level of about 5% to 20% through comparison and selection, and we regard this category as the executed Alpha.

The second part is the predicted Alpha, which mainly refers to the predictions at different levels, different cycles and different targets, including time series and cross-sectional ones. We will adjust our risk exposure on different titles based on the predictions.

However, there is a special situation. The predicted Alpha will be somewhat coupled with the executed Alpha in processing. For example, I predict a direction now, but the prediction I made may only solve 20% of the problem. The remaining 80% comes from whether I can actually execute it. This part includes order placement techniques, transaction probability analysis, conditional probability of capital cost, etc. These are all related to certain execution factors and certain prediction factors. Generally speaking, we complete our Alpha breakthrough under such a system.

When we attribute performance, the proportions contributed by these two parts of Alpha are also different. For example, the execution Alpha mentioned just now, our goal is to outperform the Benchmark by 5% to 20%, so this part is relatively certain, but the upper limit of the profit is relatively limited. The prediction Alpha is different. For example, some of our high-frequency predictions have very small profits per transaction, and they are mixed with a lot of execution Alpha. However, for some medium and low-frequency predictions, their proportion in prediction Alpha may be relatively high.

What is your view on the Crypto market? What kind of market do you think Crypto is?


As mentioned earlier, we should make different money in different markets. We make money from logical analysis in futures, and the same is true for Crypto. The characteristic of the Crypto market itself is high volatility. For example, the return of the U-based fund rate is at least 20% annualized in a bull market. So if we want to make money, we have to think about how we make money based on these characteristics. If we come in with U, we may use it for arbitrage first. This is a risk-free return of arbitrage.

Crypto is in a bull market now, and the risk-free rate of return on Pendle is 30% to 40%. Assuming we calculate the most accurate Sortino ratio, the final minus is the expected minimum return. After the minus, as a risk strategy, the remaining return is actually not much, so this is one of the reasons why we do currency-based Alpha.

My view of the market is hot money. I will take profits wherever there is money to be made and wherever the logic is clear.

The market rotation rhythm of the Crypto market this year is very similar to that of the A-shares. In the past five or six years, the A-shares have had a main line every year. For example, it was carbon neutrality at first, and this year it is AI. But in history, among the bull and bear markets in the cryptocurrency market that I have experienced and reviewed, this is the only time that there is such a main line. This year, there is one AI and one Meme. Before this, there was no main line in the cryptocurrency market. It was really a very boring market. This is also the difference between this year and the previous ones. So this year, if you can catch AI and Meme in the cryptocurrency market, you can make a lot of money.

When capturing the hot spots and rotation patterns of the Crypto industry, momentum itself is the most important part. In addition to data, we also pay attention to public opinion on Twitter. However, if there are few targets, the data we can pay attention to is still the value of the target itself.

We have a tool internally that is somewhat similar to Wind. We have been working on factors for almost two years. We store the market and Twitter sentiment. However, we do not pay much attention to sectors, because we do not catch sector rotation in this way. Our factors will select the coins with better elasticity in the sector and buy these assets.


We believe that Crypto is a highly speculative market, mainly composed of a large number of continuous transactions and occasional event transactions. This is also the reason why we continue to participate in the market.

Compared with other financial targets or markets, it has more emotional trading and event trading, which is more suitable for quantitative capture, so it is also consistent with our trading advantages.

Today, market competition has intensified. Whether in terms of trading execution or forecasting, there are now a hundred flowers blooming and a hundred boats vying for the stream, but there are still some highly structural opportunities. The sources of these structural trading opportunities are still full of emotions and events. The market has begun to undergo structural differentiation.

First, in terms of market predictability, the effectiveness of pricing for old assets has been further improved. Specifically, we can see that in the past, a trend might take several hours or even one or two days to ferment, but now it may end in 10 minutes. The huge errors caused by different factors will be corrected quickly. However, we found that there is still good Alpha in new assets.

If we also participate in some Altcoins, we will find that there are some new assets in everyones narrative, whether it is competition, entrepreneurship, or new trends, you will find that the factors used before are still effective on these assets. However, new assets are difficult to obtain. For example, your technical implementation, data access, and the stability of the trading model are somewhat lacking.

What is the contribution of different factors in the Crypto market? What are the underlying sources of returns for these factors?


The characteristic of the Crypto market is the high funding rate, that is, the large basis. For futures, the basis can be understood as the monthly spread. Assuming that they are understood as the same thing, the volatility of the monthly spread in the Crypto market is very large. Features such as arbitrage are built around this kind of thing, and alternative factors may also be built around this logic.

In addition, due to the large market volatility, some of the coins in the differentiation are very elastic, so the real money you can make depends on timing. So we tried this momentum and found that the neutral momentum is the same as the Bitcoin level in the bull market. If you don’t choose the timing, it is difficult to see a good excess return. This is also closely related to the trading mechanism of Crypto.

In addition, the data and some over-the-counter data that our exchange can provide are also different from those in the traditional market. Therefore, many of our excess returns are derived from these unique features and strategies that have been played out in the traditional market.


One of the representatives of sentiment factors is the momentum factor, which is essentially a factor of chasing ups and downs. The profits of this factor mainly come from the overreaction of the market.

For example, when retail investors see a currency rising, they usually think that the upward trend will continue, so they follow suit and buy. At this point, we can add fuel to the fire and profit from it. In addition, we can also conduct momentum reversal transactions, based on the judgment of market overreaction, and ambush and reverse operations in advance. The core of these transactions is to take advantage of the markets overreaction to gain profits.

The profits of event factors mainly come from the repricing of assets, which requires a certain reaction time. For example, by monitoring data on Twitter or potential data of large market trends, you can react quickly after an event occurs. For example, when CPI data is released, the price of Bitcoin may fluctuate violently. In this case, reacting quickly and trading can make a profit.

From the perspective of high-frequency trading, many traders are insensitive to transaction costs, which leads to them often conducting all transactions in a single market when conducting large transactions. This behavior will have a greater impact on the market, thus bringing arbitrage opportunities. The liquidity factor is effective in the high-frequency market for a long time and is one of the important tools for fund managers to obtain Alpha.

What do you think is the difference in methodology if we want to get some Alpha in the Crypto market compared to the traditional financial market? How can we get more Alpha in Crypto?

Naiqian Zheng@LUCIDA:

In recent years, I have clearly felt that people may be the most core element of Alpha. Although the Crypto industry has developed a lot, compared with the A-share market, there is a significant generation gap between the average level of Crypto industry practitioners, especially those in the secondary market.

The second point is that the data and the infrastructure of this market are really poor. There is almost no complete data supplier, like Wind and Bloomberg in the A-share market. The data quality is poor and highly fragmented. Getting data is a headache for many teams, but how can you model without data?

I think if an institution has obvious advantages over its peers in terms of talent and data, it will be a very stable source of excess returns.


Compared with traditional financial markets, the Crypto market has several notable characteristics: high volatility, high elasticity of small currencies, and strong hype. To obtain Alpha in the Crypto market, you must explore strategies around these characteristics.

A core problem is that the risk-free arbitrage returns in the Crypto market are too high. This is devastating to the value factor of the Crypto market, because there are very few projects that can bring stable USDT dividends. So when we want to calculate the value, PE, and price-earnings ratio, we will find that no matter how we calculate it, it is far less than the arbitrage returns on a U basis. Therefore, it is not feasible to use the value factors in the traditional financial market to measure the Alpha of the Crypto market.

In the Crypto market, the core values we need to focus on are different from those in the traditional market. In the traditional stock market, factors such as value and price-to-earnings ratio are the core, while in the Crypto market, we may pay more attention to the price-to-dream ratio, that is, the optimistic estimate of future expectations and everything derived from achieving these expectations.

A specific example of a factor is a value factor. For example, in the Layer 2 (L2) solution, MATIC, the change in the number of native token addresses holding 10 to 100 U (USDT) can often indicate some market trends. When a public chain is about to usher in a blockbuster application or large-scale adoption, the increase in these small holders is usually a positive signal. It is often more resonant with market sentiment and prices, and it is also relatively early. An address like this essentially represents a person, which is a question of how many people there are. From the perspective of this factor characterization, you think that an address with a balance of 10 to 100 US dollars is more like a real user.


I have summarized several differences: Information asymmetry caused by market fragmentation The decentralization of the Crypto market leads to information asymmetry. It is difficult for non-professional investors to understand the market situation, so arbitrage opportunities are particularly obvious.

Unlike traditional financial markets, assets in the Crypto market are usually traded in multiple regional markets. Therefore, this dispersion makes the phenomenon of chasing ups and downs and running around more common. Frequent switching of investors attention and irrational transactions are more common in the Crypto market. Market manipulation is more common in the Crypto market than in traditional markets.

For most ordinary investors, it is difficult to use this phenomenon to trade or design trading strategies. However, for some high-frequency trading companies, they are able to manipulate the market on a larger scale than in traditional markets to obtain Alpha. This behavior is illegal in traditional markets and will lead to jail time.

The difference between the asset management product structure of the Crypto market and the traditional financial market

Naiqian Zheng@LUCIDA:

I found that more than 80% of secondary teams use very neutral arbitrage strategies, so the homogeneity between strategies is very serious.

From the perspective of investment, the principle of the strategy itself is not complicated, and if you are doing low-frequency, you don’t need too much energy in transaction execution, so more than 80% of the products are all in the arbitrage track, so when you do some CTA or options, multi-factor strategies, compared with this kind of statistical arbitrage, the input-output is particularly inappropriate. This is also true for high-frequency trading, and then you transfer equipment and optimize all your transaction details, but in the end, your management scale is still significantly different from this kind of arbitrage. So do you think that arbitrage products will become the mainstream of the entire market in the future?


Bond trading is also a big part of the traditional financial market, not just the Crypto market. The trading volume of bonds of different levels is not low, so arbitrage trading will always exist. As long as it can be operated under some semi-compliant premise, the arbitrage income of the Crypto market can be at least two to six times that of the traditional market, which provides 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 large-capacity option. Such strategies may need to wait until the arbitrage income drops before they are truly recognized by the market. At that time, the Sharpe ratio of our strategy will become very good. Now the arbitrage income is calculated based on the U standard. Thanks to the unified account of the exchange, we can also run similar strategies through the currency standard. So our current direction is to use U to run arbitrage and use currency to run risks. This is the best allocation method.


I basically agree with Wiz.

First, the market is highly fragmented, and there are so-called barriers to entry. These problems may be difficult to solve in the next two to three years. Therefore, in the foreseeable two to three years, arbitrage space will continue to exist. Even if the arbitrage space is reduced, the transaction volume and capital capacity of arbitrage will still account for the majority of the market.

But by then, arbitrage may not exist in the form of asset management products. It will be more self-operated by high-frequency quantitative teams, mainly the high-frequency teams directly eat up the profits themselves, and no additional profits will be distributed to the market. For some asset management projects, they will settle for the second best and provide adjusted risk-return ratios with OK cost-effectiveness, such as statistical arbitrage and CTA strategies. In the next two or three years, such soil may begin to emerge.

Naiqian Zheng@LUCIDA:

The structure of Crypto asset management products is also very different from that of A-shares, because I have observed that the most mainstream products of A-shares are index-added products. Whether it is a broad-based index such as 300, 500 or 1000, products based on index-added products should be the best-selling. Most of the underlying layers of index-added products are realized by multi-factor models.

But I found that there are almost no such products in the Crypto market. I know that there are probably less than 10% of teams developing multi-factor strategies. Why are there so few teams developing multi-factor strategies?


The reason is that the returns of USDT in the market are too high. For example, I buy almost all of the USDT on PENDLE. In this case, I will not choose my own strategy. Because when my strategy deducts 30% of the risk and divides it by volatility, its performance is not even as good as the Sharpe ratio and other indicators of the traditional futures market.

Therefore, I think that when the market risk-free return is so high, everyone will naturally choose the risk-free return. According to this calculation method, the proportion of the strategy standard should be reduced by a risk-free return. When we use the real risk-free return of this market (annualized 30%) to calculate, everything becomes futile and there is no meaning in any calculation.

Our multi-factor strategy has become more diversified. When we first designed it, we did design it according to the neutral multi-factor strategy of A-shares or traditional futures. But later it gradually became more diversified and added more subjective factors. I think the core reason is that the retracement cycle of this market is very short and the changes are very fast. In this case, there are some framework problems in implementing multi-factor strategies. We cant just look at the market trends in the past two years to prove that a certain factor is effective in the long run.

In the traditional market, we may dig up a factor and test it not only in A-shares but also in US stocks. If it is effective for 20 years in US stocks and 5 years in A-shares, we can say that this is an effective factor and can be used for large-scale capital operations. However, in the Crypto market, it is difficult to have such a verification opportunity to use this factor to make a neutral strategy. It may only be possible to use a backtesting cycle of one or two years to take a look, which is not very reasonable in terms of the framework.


My feelings may be different, depending on our understanding of this framework.

What I have observed is that there are more people doing time-series trading on mainstream coins, such as trend trading on Bitcoin and Ethereum. But if we say that there are trend trading on 100 tokens, there are very few such teams. There are many people doing time-series trading, but few doing cross-sectional trading. This is the phenomenon I have observed.

If I have to attribute it, I think there are mainly the following reasons:

First, there is 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 assets that have gone through multiple cycles, such as EOS, became inactive after 2017 and 2018 and are difficult to be selected into the target pool. There are many similar targets in the Crypto market, and there are few assets that can go through several circles and maintain activity and liquidity, basically only Bitcoin and Ethereum. Others, such as Solana, have also been silent for a long time and have only recently become active.

Third, relatively speaking, the effectiveness of the time series factor may be more significant than the effectiveness of the cross-sectional factor in practice. Its underlying logic is that the response to emotional momentum exists for a long time, and we can plan it well using the traditional trend trading framework. The relative strength factor of the cross section is unstable because many targets are unstable in themselves. They are not like traditional commodities or stocks, which have experienced multiple bull and bear market cycles, and the relative strength comparison is relatively stable. In the Crypto market, the target of this wave may disappear in the next wave, and it is impossible to verify whether the relative strength comparison exists.

What do you think is the standard for measuring the value of Crypto assets? What is the value of Crypto assets?


From the current situation, the value of the Crypto market is equivalent to attention. In other words, it is now an attention-driven market. No matter what the underlying logic of the project is, as long as it can gain attention, it can gain value. This may have some similarities with the market momentum mentioned by Wiz, but I don’t think it is exactly the same. In short, this is more like a product of the eye-catching economy. In the long run, we expect and many practitioners and VCs are also working hard to promote a direction that the future value is reflected in the competitiveness of practical applications and ecosystems as much as possible. But at least at present, the state of the market is not exactly like this.

Easter egg: What do you think of the market now? How do you think Bitcoin will develop in the future? (Subjective and irresponsible)


If I were to guess, it would not have much room to go up if it keeps shaking at this position. Even if it breaks a new high, the increase may only be about 30%, and then it may have to pull back. At the current level, I think the worlds major risk assets may not have much room to go up. Im really guessing, this is very revealing.


I am more optimistic because I think the interest rate cut has not started yet. Although I did not believe in Bitcoin before, I am basically a half-believer in Bitcoin now. Therefore, I think it is still possible to reach 150,000 within two years in this bull market cycle.


Lucida ( ) is an industry-leading quantitative hedge fund that entered the Crypto market in April 2018. It mainly trades CTA/statistical arbitrage/option volatility arbitrage and other strategies, and currently manages US$30 million.

Falcon ( ) is a new generation of Web3 investment infrastructure. It helps users “select”, “buy”, “manage” and “sell” crypto assets based on a multi-factor model. Falcon was incubated by Lucida in June 2022.

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