> For the complete documentation index, see [llms.txt](https://docs.securd.org/documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.securd.org/documentation/securd-models/lp-token-rating-model/large-scale-analysis.md). # Large scale analysis In this section, we consider the 1500 most important liquidity pools of Uniswap V2 in order to study the distribution of VeV and VeS. On the first hand, regarding the VeV, we obtain:

Figure 2: Distribution of VeV in Uniswap V2.

The distribution of VeV is concentrated around 100%, which implies that the majority of liquidity pools belong to the MRM category 7:

Figure 3: Distribution of MRM in Uniswap V2.

MRM1234567
Number6411343245497
*Table 7: Number of liquidity pools per MRM category in Uniswap V2.* In the following, we list the pairs that belong to the MRM categories: * 1: WISE-WETH, FNK-USDT, USDC-USDT, DAI-USDC, XXi-WETH and XFI-WETH; * 2: stETH-WETH, MONGOOSE-WETH, FRAX-USDC and DAI-USDT. On the other hand, regarding the distribution of VeS, we obtain:

Figure 4: Distribution of VeS in Uniswap V2.

Unlike the VeV, the VeS is less concentrated, which implies that the LRM categories are better distributed:

Figure 5: Distribution of LRM in Uniswap V2.

LRM1234567
Number510223683129524
*Table 8: Number of liquidity pools per LRM category in Uniswap V2.* In the following, we list the pairs that belong to the LRM categories: * 1: WISE-WETH, WETH-USDT, USDC-WETH, FNK-USDT and DAIUSDC; * 2: USDC-USDT, DAI-WETH, XXi-WETH, MC-WETH, UNI-WETH, ELON-WETH, WBTC-WETH, BigSB-USDC, PAXG-WETH and WETHSUPER. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.securd.org/documentation/securd-models/lp-token-rating-model/large-scale-analysis.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.