UMA Protocol.md

Sponsor Prize

UMA Protocol

Prize Requirements

Builders can use OO as a decentralized truth machine that is flexible enough to handle ambiguity and real-world data with human-powered dispute resolution. UMA’s OO secures and empowers a diverse set of Web3 applications, including cross-chain bridges, insurance protocols, prediction markets and customizable DAO tooling products.

🥇 $5,000 in $UMA 🥈 $2,000 in $UMA 🏊 Pool Prize — $3,000 distributed between all qualifying submissions

Please add a sentence or two on why you're applicable for this prize.

We are built a data visualization dashboard that monitors UMA Mainnet Voting Entities using The Graph. Our chatbot component also answers questions about UMA Protocol.

We retrieve data about UMA Protocol's Mainnet Voting Entities: User, PriceIdentifier, PriceRequest, PriceRequestRound, VoterGroup, CommittedVote, RevealedVote, RewardsClaimed.

• Users can:
  • Find the users who vote the most
  • Find the users who vote correctly the most
  • Sort data by PriceRequest value
  • Look up details about any individual user

Link to the code where the tech is used.

Goose.ai/dashboard/utils/uma-mainnet-voting-data-retrieval.py

Lines 6 to 231 in 3b43b6e

	# The UMA subgraph indexes data from UMA contracts over time. It organizes data
	# about tokenholders, contracts, DVM requests, voting, and more. The subgraph
	# updates for each UMA contract interaction.
	# https://thegraph.com/explorer/subgraph?id=41LCrgtCNBQyDiVVyZEuPxbvkBH9BxxLU3nEZst77V8o&view=Overview
	API_KEY = 'dc600989321fa0012e06d1a111a29d60'
	API_URL = 'https://gateway.thegraph.com/api/'+API_KEY+'/subgraphs/id/41LCrgtCNBQyDiVVyZEuPxbvkBH9BxxLU3nEZst77V8o'
	# Retrieve data about: User, PriceIdentifier, PriceRequest, PriceRequestRound,
	# VoterGroup, CommittedVote, RevealedVote, RewardsClaimed
	def prepareJSON(query):
	r = requests.post(API_URL, json={'query': query})
	return json.loads(r.text)
	def voterGroups(num):
	query = """
	{
	voterGroups(first: %d){
	id
	price
	round
	votes{
	id
	}
	totalVoteAmount
	totalVoteAmount
	votersAmount
	won
	}
	}
	""" % num
	json_data = prepareJSON(query)
	voterGroups_data = json_data['data']['voterGroups']
	voterGroups_df = pd.DataFrame(voterGroups_data)
	return voterGroups_df
	# Users, descending by votes correctly voted for
	def userDescendingByNumCorrectVotes():
	query = """
	{
	users(where: {countRetrievals_not: null}, orderBy: countRetrievals, orderDirection: desc) {
	id
	address
	countReveals
	countRetrievals
	votesCommited{
	id
	}
	}
	}"""
	json_data = prepareJSON(query)
	users_data = json_data['data']['users']
	users_df = pd.DataFrame(users_data)
	return users_df
	def userDescendingByNumVotes():
	query = """
	{
	users(where: {countReveals_not: null}, orderBy: countReveals, orderDirection: desc) {
	id
	address
	countReveals
	countRetrievals
	votesCommited{
	id
	}
	}
	}"""
	json_data = prepareJSON(query)
	users_data = json_data['data']['users']
	users_df = pd.DataFrame(users_data)
	return users_df
	# Query that retrieves info about any specific user
	def user(id):
	query = f"""
	query {{
	user(id: "{id}") {{
	id
	address
	countReveals
	countRetrievals
	votesCommited
	}}
	}}
	"""
	json_data = prepareJSON(query)
	user_data = json_data['data']['user']
	user_df = pd.DataFrame(user_data, index=[0])
	return user_df
	def priceRequestsAscending():
	query = """
	{
	priceRequests(where: {resolutionTimestamp_not: null}, orderBy: resolutionTimestamp, orderDirection: asc){
	id
	isResolved
	price
	latestRound
	time
	identifier
	ancillaryData
	resolutionTransaction
	resolutionTimestamp
	resolutionBlock
	rounds{
	id
	}
	committedVotes{
	id
	}
	revealedVotes{
	id
	}
	rewardsClaimed{
	id
	}
	}
	}
	"""
	json_data = prepareJSON(query)
	priceRequests_data = json_data['data']['priceRequests']
	priceRequests_df = pd.DataFrame(priceRequests_data)
	return priceRequests_df
	def priceRequestsDescending():
	query = """
	{
	priceRequests(where: {resolutionTimestamp_not: null}, orderBy: resolutionTimestamp, orderDirection: desc){
	id
	isResolved
	price
	latestRound
	time
	identifier
	ancillaryData
	resolutionTransaction
	resolutionTimestamp
	resolutionBlock
	rounds{
	id
	}
	committedVotes{
	id
	}
	revealedVotes{
	id
	}
	rewardsClaimed{
	id
	}
	}
	}
	"""
	json_data = prepareJSON(query)
	priceRequests_data = json_data['data']['priceRequests']
	priceRequests_df = pd.DataFrame(priceRequests_data)
	return priceRequests_df
	def firstPriceRequests(num):
	query = """
	{
	priceRequests(first: %d){
	id
	isResolved
	price
	latestRound
	time
	identifier
	ancillaryData
	resolutionTransaction
	resolutionTimeStamp
	resolutionBlock
	rounds{
	id
	}
	committedVotes{
	id
	}
	revealedVotes{
	id
	}
	rewardsClaimed{
	id
	}
	}
	}
	""" % num
	json_data = prepareJSON(query)
	priceRequests_data = json_data['data']['priceRequests']
	priceRequests_df = pd.DataFrame(priceRequests_data)
	return priceRequests_df
	def firstPriceIdentifiers(num):
	query = """
	{
	priceIdentifiers(first: %d) {
	id
	isSupported
	priceRequests {
	id
	}
	}
	}
	""" % num
	json_data = prepareJSON(query)
	priceIdentifiers_data = json_data['data']['priceIdentifiers']
	priceIdentifiers_df = pd.DataFrame(priceIdentifiers_data)
	return priceIdentifiers_df
	def firstUsers(num):
	query = """
	{
	users(first: %d) {
	id
	address
	countReveals
	countRetrievals
	}
	}""" % num
	json_data = prepareJSON(query)
	users_data = json_data['data']['users']
	users_df = pd.DataFrame(users_data)
	return users_df

How easy is it to use the API / Protocol? (1 - very difficult, 10 - very easy)

10 🌟

Additional Feedback

Thank you for the clean documentation and up-to-date Subgraph! We were able to find some ways to deliver insightful voting data easily to our potential users.