Optimizing ETH Staking Rewards through Validator Selection
If you've been in the blockchain space long enough, you are likely familiar with the critical roles of validator in the Ethereum network. You are also probably aware that becoming a validator traditionally requires staking 32 ETH – a considerably high financial commitment that has historically prevented many would-be validators from entering the ecosystem.
But the landscape has evolved. Today, innovative staking mechanisms, like liquid staking, have been adopted to significantly lower the entry barrier posed by the past staking requirement.
With this, platforms like Chainnodes allow interested ETH holders to stake as little as 2 ETH and earn a portion of the validation rewards. With liquid staking, validators get to pool smaller contributions from multiple holders and stake collectively, democratizing access to reward while enhancing the security of Ethereum's Proof-of-Stake.
Now that several protocols offer this service, choosing the best liquid staking platform requires a careful selection process based on real-time metrics and performance indicators like those discussed in this article.
Here are a couple of things you should consider to know the best liquid staking protocols to stake with.
Validator uptime and performance
The reward earned for running the validating node is proportional to the validator uptime. Contrarily, validators run the risk of slashing or getting low rewards if they fail to keep up performance and failed achieve the required uptime consistently.
This is where selecting a validator to stake with can be a little tricky because a validator with low uptime means low reward. Typically, validators that aim for about 99.9% uptime are able to maintain trust and ensure continuous operation.
Discerning efficient performance, on the other hand, is a metric that needs to be tracked over time. Hence, active participation in the ecosystem community and gaining practical insight into its operation on metric tracking platforms allow stakers to ascertain the level of efficiency.
Protocols System Features
Given the innovation of technology, liquid staking protocols have gone beyond merely running validator nodes to implementing tools and infrastructure that make staking and protecting stakers' assets extensively easier—additionally, Ethereum rewards validators for capturing MEV attacks during block proposals, which means a high reward.
However, these protocols constantly have to balance efficiency with innovative development and cost, which means, at times, they may sacrifice one feature to implement another. For instance, leading liquid staking protocols like Chainnodes operate with improved latency, assets protection techniques and MEV attack prevention.
When selecting a validator to work with, checking out some of the core features of the protocols and uncovering what features are prioritized enables you to choose the best liquid staking protocol that aligns with your risk tolerance.
System adaptability
Blockchain networks, including the Ethereum network, are essentially dynamic. For instance, the Ethereum ecosystem has witnessed notable changes over time, which have resulted in both structural development and technology upgrades. Given that blockchain is a developing technology, more tools are being developed to enhance operational efficiency and facilitate swift and seamless trading.
This raises the question of how long a staking protocol can keep up with these constant changes in order to not only run efficiently but also to remain relevant and sustainable. To get your answer, a closer understanding of the protocol infrastructure is vital, and you can get a clear overview by reading the project's whitepaper, following up with roadmaps, and staying updated on developments.
Risk management
Beyond the ability to validate with efficient tools like dedicated nodes and more, a protocol's ability to manage risk is also one feature that stands out. Here, a careful consideration of historical data, patterns and performance is required.
Even when the operation is down or when the ecosystem suffers slashing, its ability to stabilize activities and manage the aftermath can determine the impact of the penalty sustained in the long run. Hence, an ecosystem that manages risk transparently while advising users on the best course of action depending on their risk appetite is an ideal one to stake with.
Why select the best validators to stake with using dynamic metrics?
The question is, why should anyone go to this length before finally selecting the best validator to stake with when they can add their tokens to a pool and wait for reward distribution?
Here are (4) four answers to that:
- Maximizing profit: By selecting validators dynamically, stakers can maximize profits by avoiding underperforming validators and capitalizing on those with high uptime and efficiency.
- Lower risk: With a good uptime percentage and diverse risk management portfolio, a validator can avoid slashing and other penalties, which reduces the level of risk borne by stakers.
- Good insight: Dynamic selection metrics allow stakers to get a good understanding of the concept of staking, the protocol, and the Ethereum ecosystem, which can help make proper investment decisions relying on other cryptocurrency aspects.
- Asset stability: While cryptocurrency is a volatile investment by its nature, moving between validators means your asset suffers constant gas fees, and it is also probably exposed to additional MEV attacks. Changing validators repeatedly also exposes stakers to complete asset loss. However, careful and dynamic selection ensures stable staking.
Conclusion
Dynamic validator selection requires taking a more adaptive approach. Hence, real-life metrics and performance indicators are employed to determine the best available option. In addition to this, stakers must monitor their dashboards consistently. This allows stakers to get an overview of the reward distribution schedule, identify when the reward is lower and discern factors that may occasion such development.
Stakers can then rely on this veritable data to determine when the selected validator is underperforming and swiftly decide on whether to change the validator or otherwise.