> For the complete documentation index, see [llms.txt](https://lazyotter.gitbook.io/lazyotter/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://lazyotter.gitbook.io/lazyotter/products/yield-index.md).
# Yield index
In any investment decision-making process, two crucial factors to consider are return and risk. While we already have robust risk data, integrating yield data seemed like the natural next step. Initially, we thought this would be straightforward, but we quickly realized that credible yield data is challenging to obtain. After evaluating several data providers, we encountered common issues: the data were either incomplete or unverifiable.
When pulling data from centralized databases, we found that only well-known projects were listed, leaving out newer or smaller projects. Moreover, the calculation of APY (Annual Percentage Yield) is not standardized or consistent across projects, making it difficult to rely on these figures. Recognizing these limitations, we decided to build our own database.
#### Building a Reliable Yield Database
To ensure the highest level of accuracy, we developed an advanced simulation engine capable of mimicking the withdrawal and reward harvesting processes. This engine performs real-time simulations of the entire yield earning cycle, including the sale of reward tokens and realization of impermanent loss, to precisely calculate the actual yield. Our methodology accommodates the intricacies of various protocols, especially those with complex reward structures such as vesting schedules. This sophisticated approach allows us to generate the most accurate and reliable yield data, empowering users to make well-informed decisions based on comprehensive, real-world insights.
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