Index funds are slowly becoming a recognizable trademark of the emerging cryptocurrency asset class. Due to their inherent ability to reduce volatility, track the market, and provide dynamic risk adjusting exposure, they can help people get started dabbling with digital assets without requiring extensive effort.
Many traditional investors will turn towards services like Bitwise or Crypto20 to manage their cryptocurrency funds. These services provide a small selection of index funds that are convenient for people who haven’t spent time researching the market and just want to throw their money into anything. However, there will be those people who want more options than a few curated indexes. They will want to strategically design their own index based on what is working the best in the market at this moment.
Developing your own indexing strategy requires some initiative, but the knowledge that’s acquired through the process will become invaluable in better understanding how your strategy is working for you.
There are a number of aspects we should consider when constructing our indexing strategy. Each one of these components will play a crucial role in ensuring we are being effective.
Before getting into the technical details of the index fund, the first thing we need to do is decide on the assets which should be in the index. This is fundamentally what matters more than anything else we will do with our index.
In general, the most common way to select assets for a cryptocurrency index is to prioritize those with the highest market cap. That means calculating the cryptocurrencies with the highest market cap and selecting the top 10, 20, or 30.
Calculating the market caps of each individual asset can be time-consuming, so services like CoinMarketCap can be used to accelerate the process.
There are times when you might want to include specific assets in an index, even if they don’t abide by their asset selection methodology. While this wouldn’t be possible when using services like Bitwise or Crypto20, building our own index fund provides us the flexibility to adjust our index to include assets that don’t abide by strict selection criteria.
An example would be if we want to create an index of the top 10 assets by market cap, but based on our research, we have a strong belief the asset currently ranked 15 by market cap will soon pump. In this case, we can construct an index that includes assets from rank 1 to 10, but also include the asset which is currently ranked 15 by market cap.
Similar to ‘inclusions’, there are times when you may want to exclude an asset from an index, even when the asset strictly meets the criteria to be included in the index. Constructing a personalized index allows us to make these decisions without impacting our entire indexing strategy.
One example of common assets that are excluded are stablecoins like USDT, TUSD, and USDC. Since most people place their funds into cryptocurrencies to get exposure to digital assets, keeping stablecoins in an index serves no purpose for our intentions. Additionally, your philosophy may dictate that no forks should be included in an index. In that case, it is your prerogative to eliminate assets that are forks of other cryptocurrencies to abide by your philosophy.
Most index funds will use a form of dynamic asset selection in order to automate the process of how assets are added or removed from the index. Continuing from the previous examples regarding constructing a market index based on the top ‘X’ assets by market cap, this would allow the allocations to update as the market changes and the top 10 assets are replaced by new assets.
One option for how to evaluate when to add or remove an asset from the index is to use a ‘buffer zone’. This defines a threshold at which point an incoming asset should be considered for addition to the index. A common example is the use of a 5% ‘buffer zone’. When a new asset increases in market cap to become 5% higher than another asset in the index, the new asset is then added to the index, removing the asset which was beat out.
It is not only important to have a clear understanding of the assets that should be in your custom index, but also the weighting each asset should hold in the index. This will influence how much of your total value is placed into each individual asset.
Weighting an index by market cap is the most common strategy people take when allocating funds. Using a market cap weighting means the index is as close as possible to tracking the actual value held in the market being tracked. In this case, cryptocurrency.
A market cap weighted index means the funds are distributed to each constituent based on each individual asset’s contribution to the sum of all asset’s market caps in the fund. An example would be if you have three assets, the first asset ‘A’ has a market cap of 4, the second asset ‘B’ has a market cap of 3, and the third asset ‘C’ has a market cap of 1. In this case, a fund consisting of these three assets would be comprised of 50.00% ‘A’, 37.5% ‘B’, and 12.5% ‘C’.
An index fund can also be weighted using the square root of the market cap. Often, this method is used when there are some assets that massively shift the weighting of the index when using a market cap weighting. These are typically assets with significantly higher market caps than any other asset in the index. An example of an asset that currently fits this description is Bitcoin. At the time of writing, Bitcoin holds nearly 70% of all value in the market, making it the largest contributor to the global cryptocurrency market cap by a significant margin.
Due to the large allocation for Bitcoin that a market cap weighted index prescribes, many people elect to implement a square root market cap weighted index instead. Rather than directly summing the market caps of each asset, the square root of the assets market cap is summed. Using the example from the previous section, let’s say we have an asset ‘A’ with a market cap of 4, asset ‘B’ with a market cap of 3, and asset ‘C’ with a market cap 1. A fund with square root market cap weighting with these three assets would therefore be comprised of 42.26% ‘A’, 36.6% ‘B’, and 21.14% ‘C’.
Comparing the examples for square root market cap weightings and market cap weightings demonstrates how the weightings for the largest market cap assets are dampened while those of the lowest market cap are bumped. This prevents the distribution of a fund from becoming heavily weighted towards a single asset.
The most simple allocation weighting strategy for a fund is to evenly weight each asset in the portfolio. Each asset having the exact same value of funds allocated. While this strategy is far less common than market cap weighted strategies, some recent research has found evenly allocated indexes have tended to outperform market cap weighted indexes historically. This suggests evenly allocated index funds are worth the attention when considering which strategy is best for your personalized index fund.
An example of an evenly weighted index fund is one that has 10 assets and each asset is allocated 10% of the fund value.
The cryptocurrency market is still dominated by a few major players. The top two assets alone comprise 75% of the market cap. The tenth asset only holds .5% of the market cap. This vast disparity can lead to a lack of diversification in an index fund when allocating by market cap. Instead of using a square root market cap or evenly allocated index fund, another option is to implement a minimum weight for the index. That way every asset can provide a healthy contribution to the index.
An example would be setting a minimum threshold of 5% for a top 10 market cap index fund. Each asset that would have held less than 5% of the weight in the fund will get bumped to 5%. The remaining allocations will then be allocated based on market cap to distribute the funds.
Similar to the discussion in the above section about minimum weighted allocations, there are also times when you want to provide a cap on the maximum amount a single asset can be allocated. This is ideal for asset classes like cryptocurrency where a single asset like Bitcoin can consume as much as 70% of the market cap. Under these conditions, it may be ideal to provide a maximum percent which can be allocated to a single asset.
An example of a maximum weighting would be a max weighting of 25% on a top 10 market cap index fund. The result of this restriction would be to cap any asset which holds over 25% of the market cap to 25%. Once capped, the remaining assets will have their allocations determined based on their market cap.
Before releasing your index fund into the wild, it’s vital to determine the optimal rebalancing strategy. Rebalancing provides a way to reset your fund back to its target allocations by selling part of the assets which performed well to buy those assets which didn’t perform as well.
At the end of each rebalance, your allocations should match the percentages which are defined by your allocation distribution strategy. That means if your allocation distribution strategy dictates you should have %50 of asset ‘A’ and 50% of asset ‘B’, at the end of a rebalance your index fund will reach those target allocations by buying and selling each of these assets.
Over time, the individual assets in an index fund will drift in value. Some assets will go up in value and some will go down when compared to other assets in the index fund. This causes the allocations to deviate from the desired target allocations which are defined in your allocation strategy. When this happens, rebalancing comes in handy to re-align the portfolio with our target allocations.
Periodic rebalancing is the most simple rebalancing strategy we can implement for our index fund. Essentially, a periodic rebalance uses a time interval to determine when the next rebalance should take place. If we choose weekly rebalances, then the index fund will rebalance on a weekly basis. Every week at the same time, the index fund will be rebalanced. Similarly, if we select monthly rebalancing, the index fund will rebalance once a month at the same time.
Due to the volatility of the cryptocurrency market, recent studies have discovered that more frequent rebalancing intervals have tended to outperform longer intervals that are common in the traditional market.
Threshold rebalancing is a strategy that has only recently been explored in the cryptocurrency market. Unlike the time-based trigger for periodic rebalancing, threshold rebalancing uses the movements of the market to determine when to execute a rebalance. When assets in the index fund deviate from their target allocations further than a given threshold, the entire index fund will be rebalanced. The decision to base the execution of the rebalance on how far the assets have deviated from their target allocations provides a “need-based” strategy. Basically, the index fund will only rebalance if there is enough need from the individual constituents in the index.
As an example, let’s imagine we have an index fund with 2 assets. Asset ‘A’ has a target allocation of 70% and asset ‘B’ has a target allocation of 30%. For the threshold rebalancing strategy, say we wanted to implement a 10% threshold. The index fund would then execute a rebalance when either asset had a current allocation that crossed outside the range of the target allocation plus or minus 10% of the target allocation. For asset ‘A’, that would mean an allocation of 63% or 77%. For asset ‘B’ that would be 33% or 27%. Once either of these assets crosses these allocation thresholds, the entire index will be rebalanced to once again reach their target allocations of 70% and 30% respectively.
The need-based nature of threshold rebalancing has demonstrated, in recent studies, superior performance when compared to periodic rebalancing historically.
Many index funds will need a mechanism for what happens when value is added or removed from the fund. One of the most popular of these mechanisms is dollar-cost averaging.
In the context of index funds, dollar-cost averaging (DCA) is the process of distributing deposited funds across the index in order to attempt to reach the target allocations of the index. This can reduce the dependence the index has on rebalancing in some cases since the fund will become balanced (to at least a certain extent) during each DCA event.
Imagine you have a portfolio of two assets. Asset ‘A’ has a target allocation of 70% with a current allocation of 65% and asset ‘B’ has a target allocation of 30% with a current allocation of 35%. When a deposit is made, the DCA will attempt to distribute the funds such that at the end of the DCA, asset ‘A’ will have an allocation of 70% and asset ‘B’ will have an allocation of 30%.
Developing an index fund that manages hundreds of millions of dollars or even billions of dollars means you will need to think critically about how the trades will be executed to reduce the impact your fund has on the market.
It’s not as easy as throwing market orders onto an exchange’s order book and hoping for the best. People will expect precise order execution and a strategy for how the fund will minimize fees.
Placing open orders on an exchange adds liquidity and provides a way for other traders to take your order. In many cases, exchanges will reward this addition of liquidity to the exchange by providing lower trading fees for maker orders.
This is typically the first strategy for reducing fees and also not crossing the bid-ask spread to further improve the outcome in your favor.
The second and more complex strategy for improving the order execution is to route trades through alternative pairs to get the best rate possible. By calculating every trade segment in real-time during a rebalance, we can find the path between different assets that results in the highest yield.
With rebalancing, the complexity of these algorithms grows exponentially. Since a rebalance doesn’t simply trade one asset for another asset, but numerous assets for numerous other assets, there are millions of possible trading options during a single rebalance of 10 assets.
As a result, many rebalancing strategies need to find ways to simplify the problem and focus on the most promising variables during a rebalance.
After defining your strategy, the next important part of building your index is to implement the strategy. This is where Shrimpy comes into focus. Shrimpy is the most advanced index fund builder in the cryptocurrency market. Not only does Shrimpy support a cryptocurrency indexing tool which is designed for people who want to manage their index through the portfolio management application, but the Shrimpy Crypto Trading APIs also provide a complete set of API endpoints which connect to every major exchange so developers can implement their own indexing strategy as well.
There are a number of ways to implement your cryptocurrency indexing strategy. The most popular indexing application in the market is Shrimpy. This resource can help you automate your entire index from start to finish. Each of the aspects of an index that was discussed above is supported by the Shrimpy automated indexing tool. The details of how to set this up in Shrimpy can be found in this article.
That’s not the extent of the Shrimpy Portfolio Management application, however. Carefully track and segment your funds across every major exchange, participate in the social trading features by copying a leader’s portfolio, or backtest indexing strategies with precise market data.
Shrimpy provides the most complete professional portfolio management solution in the cryptocurrency market.
The Universal Crypto Trading APIs combine the worlds of portfolio automation with API development. Shrimpy brings forward the only APIs which are specifically designed for developers to construct indexes, build scalable crypto trading applications, and manage diverse portfolios.
Developers can construct a personal custom index or carefully scale a global application that supports 100,000 active users. It’s all possible with the Universal Crypto Trading APIs.
These APIs aren’t limited to only indexing the market. They provide a complete set of endpoints for executing real-time and algorithmic trading strategies. Live order book data can be accessed across every major exchange, historical data is available as far back as 2012, and smart order routing trading endpoints are available for everyone.
It’s the only way developers should be building applications in the crypto market.
With these Indexing tools in hand, you now have every resource you need to implement the most complete indexing strategy possible. Define the future of cryptocurrency indexing by building your own strategy and exploring everything Shrimpy has to offer.
Developers have found their calling and a home. It’s now time to get building. Develop some of the most innovative platforms in the crypto market by leveraging powerful APIs.
You don’t need to just take our word for it, we have one of the fastest-growing development communities in the cryptocurrency market which can vouch for Shrimpy.
Try it out today and unlock a new world of possibilities when it comes to developing cryptocurrency trading applications or services.
Each day Shrimpy executes over 200,000 automated trades on behalf of our investor community. And joining them is easy.
After you sign up and connect your first exchange account, you’ll deploy an investment-maximizing strategy in as few as 5-minutes.
Whether you create your own rebalancing strategy or completely custom automation, the ability to walk your own path belongs in the hands of every crypto investor.
A crypto robo-advisor is a platform that manages your cryptocurrency portfolio automatically. Here are 5 robo-advisors to use in 2023.
Explore the top 8 best crypto tools of 2023! Streamline trading, optimize portfolio management, and enhance your crypto tax strategy today.
Crypto trading bots help traders save time, execute orders faster, and trade more accurately. Here's a step-by-step guide on how to build a crypto trading bot.