Merging

When passing a list of FieldPaths to any query() function, subgrounds will merge them into a single query.

Warning

This is only true if the FieldPaths originate from the same subgraph.

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Python

In this query, we are taking the top 4 curve pools by cumulative volume and analyzing the top 3 days by daily total revenue

from subgrounds import Subgrounds
sg = Subgrounds()

curve = sg.load_subgraph(
    "https://api.thegraph.com/subgraphs/name/messari/curve-finance-ethereum")

# Partial FieldPath selecting the top 4 most traded pools on Curve
most_traded_pools = curve.Query.liquidityPools(
    orderBy=curve.LiquidityPool.cumulativeVolumeUSD,
    orderDirection="desc",
    first=4,
)

# Partial FieldPath selecting the top 2 pools by daily total revenue of
#  the top 4 most traded tokens.
# Mote that reuse of `most_traded_pools` in the partial FieldPath
most_traded_snapshots = most_traded_pools.dailySnapshots(
    orderBy=curve.LiquidityPoolDailySnapshot.dailyTotalRevenue,
    orderDirection="desc",
    first=3,
) 

# Querying:
#  - the name of the top 4 most traded pools, their 2 most liquid 
# pools' token symbols and their 2 most liquid pool's TVL in USD
sg.query_df([
    most_traded_pools.name,
    most_traded_snapshots.dailyVolumeUSD,
    most_traded_snapshots.dailyTotalRevenueUSD,
])

GraphQL

In this query, we are taking the top 4 curve pools by cumulative volume and analyzing the top 3 days by daily total revenue

query {
  liquidityPools(first: 4, orderBy: cumulativeVolumeUSD, orderDirection: desc) {
    name
    dailySnapshots(first: 3, orderBy: dailyTotalRevenueUSD, orderDirection: desc) {
      dailyVolumeUSD
      dailyTotalRevenueUSD
    }
  }
}

Note

This becomes very helpful when chaining partial FieldPaths together since you can leverage normal python constructs to help organize the data as you want to access and query() will handle the rest!