AAVE_LPOOL_CONTRACT = "0x87870Bca3F3fD6335C3F4ce8392D69350B4fA4E2" #Aave v3 Lending Pool Contract AAVE_ORACLE_CONTRACT = "0x54586bE62E3c3580375aE3723C145253060Ca0C2" # Aave Oracle v3 AWSTETH_CONTRACT = "0x0B925eD163218f6662a35e0f0371Ac234f9E9371" #Aave Ethereum wstETH (aEthwstETH) v3 CURVE_POOL_CONTRACT = "0xDC24316b9AE028F1497c275EB9192a3Ea0f67022" BALANCER_POOL_CONTRACT = "0xBA12222222228d8Ba445958a75a0704d566BF2C8" BALANCER_POOL_ID = "0x32296969ef14eb0c6d29669c550d4a0449130230000200000000000000000080" WETH_TOKEN_CONTRACT = "0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2" OINCH_POOL_CONTRACT = "0xC1A900Ae76dB21dC5aa8E418Ac0F4E888A4C7431" SUSHI_POOL_CONTRACT = "0xc5578194D457dcce3f272538D1ad52c68d1CE849" DAI_TOKEN_CONTRACT = "0x6B175474E89094C44Da98b954EedeAC495271d0F" curve_abi = json.load(open("curvepoolabi.json")) balancer_abi = json.load(open("balancerpoolabi.json")) oneinch_abi = json.load(open("oneinchpoolabi.json")) sushi_abi = json.load(open("sushipoolabi.json")) #list of collateral stats: token contract, variable debt, stable debt, decimals, penalty, liq.treshold, atoken(collateral), liq treshold e-mode, penalty e-mode CBETH = ('cbeth',"0xBe9895146f7AF43049ca1c1AE358B0541Ea49704", "0x0c91bcA95b5FE69164cE583A2ec9429A569798Ed", "-",18, 0.075, 0.74,"0x977b6fc5dE62598B08C85AC8Cf2b745874E8b78c", 0.93, 0.01) RETH = ('reth',"0xae78736Cd615f374D3085123A210448E74Fc6393", "0xae8593DD575FE29A9745056aA91C4b746eee62C8", "-",18, 0.075, 0.74,"0xCc9EE9483f662091a1de4795249E24aC0aC2630f", 0.93, 0.01) risk_rating = ['A','B+','B','B-','C','D','liquidation'] def get_userlist(vlink): #collects information about users that interacted with aSTETH contract, vlink - Flipside API link r = requests.get(f"{vlink}").json() df = pd.DataFrame(r) return df def get_user_stats(vuser): #gets information about user from Aave Lending pool contract abi = json.load(open("AaveLP.json")) contract = web3.eth.contract(address=AAVE_LPOOL_CONTRACT, abi=abi) (collateral_eth, debt_eth, available_borrows_eth, current_liquidation_threshold, ltv, healthfactor) = contract.functions.getUserAccountData(Web3.to_checksum_address(vuser)).call() emode = contract.functions.getUserEMode(Web3.to_checksum_address(vuser)).call() return (collateral_eth, debt_eth, available_borrows_eth, current_liquidation_threshold, ltv, healthfactor, emode) def get_steth_eth_price(): #gets current ETH price from Aave Oracle abi = json.load(open("AaveOracle.json")) contract = web3.eth.contract(address=AAVE_ORACLE_CONTRACT, abi=abi) price = contract.functions.getAssetPrice(Web3.to_checksum_address('0x7f39C581F595B53c5cb19bD0b3f8dA6c935E2Ca0')).call() price = price / pow(10,18) return price def get_asteth_balance(vuser): #gets current aSTETH balance of user abi = json.load(open("AToken.json")) contract = web3.eth.contract(address=AWSTETH_CONTRACT, abi=abi) balance = contract.functions.balanceOf(Web3.to_checksum_address(vuser)).call() return balance def get_atoken_balance(user: str, vtoken_contract) -> float: ## function to get user's aToken balance address = web3.to_checksum_address(user) return web3.eth.contract(address=vtoken_contract, abi=json.load(open("AToken.json"))).functions.balanceOf(address).call() def eth_last_price(): #gets current ETH price from coingecko req = requests.get(f'https://api.coingecko.com/api/v3/simple/price?ids=ethereum&vs_currencies=usd&include_market_cap=false&include_24hr_vol=false&include_24hr_change=false&include_last_updated_at=false' ).json() price = req['ethereum']['usd'] return price def wsteth_last_price(): #gets current wstETH price from coingecko req = requests.get(f'https://api.coingecko.com/api/v3/simple/price?ids=wrapped-steth&vs_currencies=usd&include_market_cap=false&include_24hr_vol=false&include_24hr_change=false&include_last_updated_at=false' ).json() price = req['wrapped-steth']['usd'] return price def get_asset_usd_price(vtoken_contract): abi = json.load(open("AaveOracle.json")) contract = web3.eth.contract(address=AAVE_ORACLE_CONTRACT, abi=abi) price = contract.functions.getAssetPrice(Web3.to_checksum_address(vtoken_contract.lower())).call() return price def get_risks(data, collateral_loan_ratio): #calculates the risk level for each position and returns the positions sorted by risk dfrisk = pd.DataFrame(data = data) risk_rating_list = get_scale_dic(collateral_loan_ratio) dfrisk['risk_rating'] = [(x > risk_rating_list[0] and 'A') or (risk_rating_list[1] < x <= risk_rating_list[0] and 'B+') or (risk_rating_list[2] < x <= risk_rating_list[1] and 'B') or (risk_rating_list[3] < x <= risk_rating_list[2] and 'B-') or (risk_rating_list[4] < x <= risk_rating_list[3] and 'C') or (risk_rating_list[5] < x <= risk_rating_list[4] and 'D') or (risk_rating_list[5] <=x and 'liquidation') for x in dfrisk['healthf']] return dfrisk.query('amount > 0').sort_values(by = 'healthf', ascending = False) def get_distr(data): #calculates and returns a pivot table by risk levels risk_distr = data.pivot_table(index = 'risk_rating', values = ['amount'], aggfunc = ['sum', 'count']) risk_distr.columns = ['wstETH','cnt'] risk_distr['wstETH'] = risk_distr['wstETH'] risk_distr['percent'] = (risk_distr['wstETH']/risk_distr['wstETH'].sum())*100 risk_distr['average'] = risk_distr['wstETH']/risk_distr['cnt'] median_a0 = data.query('risk_rating == "A"')['amount'].median() data.loc[data['risk_rating']=='A','median'] = median_a0 median_b0 = data.query('risk_rating == "B+"')['amount'].median() data.loc[data['risk_rating']=='B+','median'] = median_b0 median_b1 = data.query('risk_rating == "B"')['amount'].median() data.loc[data['risk_rating']=='B','median'] = median_b1 median_b2 = data.query('risk_rating == "B-"')['amount'].median() data.loc[data['risk_rating']=='B-','median'] = median_b2 median_c0 = data.query('risk_rating == "C"')['amount'].median() data.loc[data['risk_rating']=='C','median'] = median_c0 median_d0 = data.query('risk_rating == "D"')['amount'].median() data.loc[data['risk_rating']=='D','median'] = median_d0 median_il = data.query('risk_rating == "is liquidating"')['amount'].median() data.loc[data['risk_rating']=='liquidation','median'] = median_il median_distr = data.pivot_table(index = 'risk_rating', values = ['median'], aggfunc = 'min') risk_distr = risk_distr.merge(median_distr, on = 'risk_rating', how = 'outer') return risk_distr.reindex(['A','B+','B','B-','C','D','liquidation']).fillna(0) def get_scale(collateral_loan_ratio): #calculates and returns scale-table risk_ratio1 = [str(f'{100/i:.0f}%') for i in collateral_loan_ratio] risk_ratio2 = ['0']+risk_ratio1[:-1] risk_ratio = [f'{risk_ratio2[i]} - {risk_ratio1[i]}' for i in range(len(risk_ratio1))] risk_ratio.append('>=100%') collateral_loan_ratio_list1 = [str(f'{i:.2f}x') for i in collateral_loan_ratio] collateral_loan_ratio_list2 = ['>']+ collateral_loan_ratio_list1[:-1] collateral_loan_ratio_list = [f'{collateral_loan_ratio_list1[i]} - {collateral_loan_ratio_list2[i]}' for i in range(len(collateral_loan_ratio_list2))] collateral_loan_ratio_list.append('=< 1.00x') risk_rating = ['A','B+','B','B-','C','D','liquidation'] return pd.DataFrame(data={'health factor' : collateral_loan_ratio_list, 'risk ratio' : risk_ratio, 'risk rating' : risk_rating}) def get_scale_dic(collateral_loan_ratio): return [i for i in collateral_loan_ratio] @unsync def get_debt_balance(vuser,varcontract, stabcontract): #gets current debt token balance of user if (varcontract == '-'): var_balance = 0 else: abi = json.load(open("VariableDebtToken.json")) contract = web3.eth.contract(address=Web3.to_checksum_address(varcontract), abi=abi) var_balance = contract.functions.balanceOf(Web3.to_checksum_address(vuser.lower())).call() #gets current debt token balance of user if (stabcontract == '-'): stable_balance = 0 else: abi = json.load(open("StableDebtToken.json")) contract = web3.eth.contract(address=Web3.to_checksum_address(stabcontract), abi=abi) stable_balance = contract.functions.balanceOf(Web3.to_checksum_address(vuser.lower())).call() balance = var_balance + stable_balance return balance @unsync def parse_ethdebt_balance(df): buf = [] tasks = [(user, get_debt_balance(user,'0xeA51d7853EEFb32b6ee06b1C12E6dcCA88Be0fFE', '0x102633152313C81cD80419b6EcF66d14Ad68949A')) for user in df["user"]] for user, task in tasks: balance: float = task.result() # type: ignore buf.append({"user": user, "ethdebt": balance}) return buf @unsync def parse_atoken_balance(df, vtoken_symbol, vtoken_contract): ## function to parse user's atoken balance buf = [] tasks = [(user, get_atoken_balance(user, vtoken_contract)) for user in df["user"]] for user, task in tasks: abalance: float = task#.result() # type: ignore buf.append({"user": user, f'{vtoken_symbol}': abalance}) return buf

Aave V3 wstETH Collateral Risk Monitor

Bin 1.1 Users with e-mode and with >=80% of collateral - wstETH, and >= 80% of debt - ETH

Bin 1.2 Users without e-mode and with >=80% of collateral - wstETH, and >= 80% of debt - ETH

Bin 2: Users with wstETH collateral and >=80% debt - not ETH

Bin 3: All other users with wstETH collateral