AAVE_LPOOL_CONTRACT = "0x794a61358D6845594F94dc1DB02A252b5b4814aD" #Aave v3 Lending Pool Contract on Polygon ASTMATIC_CONTRACT = "0xEA1132120ddcDDA2F119e99Fa7A27a0d036F7Ac9" #Aave Polygon STMATIC (aPolSTMATIC) AAVE_STMATIC_ORACLE_CONTRACT = "0x97371dF4492605486e23Da797fA68e55Fc38a13f" # Aave Oracle stMATIC AAVE_WMATIC_ORACLE_CONTRACT = "0xAB594600376Ec9fD91F8e885dADF0CE036862dE0" # Aave Oracle WMATIC CURVE_POOL_CONTRACT = "0xFb6FE7802bA9290ef8b00CA16Af4Bc26eb663a28" WMATIC_TOKEN_CONTRACT = "0x0d500B1d8E8eF31E21C99d1Db9A6444d3ADf1270" BALANCER_POOL_CONTRACT = "0xBA12222222228d8Ba445958a75a0704d566BF2C8" BALANCER_POOL_ID_OLD = "0xaf5e0b5425de1f5a630a8cb5aa9d97b8141c908d000200000000000000000366" #old pool before September 2022 BALANCER_POOL_ID_NEW = "0x8159462d255c1d24915cb51ec361f700174cd99400000000000000000000075d" #new pool after September 2022 QUICKSWAP_POOL_CONTRACT = "0x65752C54D9102BDFD69d351E1838A1Be83C924C6" risk_rating = ['A','B+','B','B-','C','D','liquidation'] def get_dune_data(query_id,dune_key): query_link=f'https://api.dune.com/api/v1/query/{query_id}/execute' headers = {'x-dune-api-key': dune_key} data = '{}' query_execution_link= requests.post(query_link, headers=headers, data=data) query_execution_id=query_execution_link.json()['execution_id'] status_link=f'https://api.dune.com/api/v1/execution/{query_execution_id}/status' status_query = requests.get(status_link, headers=headers, data=data) while status_query.json()['state'] != 'QUERY_STATE_COMPLETED' : time.sleep(10) status_query = requests.get(status_link, headers=headers, data=data) result_link=f'https://api.dune.com/api/v1/execution/{query_execution_id}/results' result_query = requests.get(result_link, headers=headers, data=data) results_df=pd.DataFrame(result_query.json()['result']['rows']) results_df=results_df.rename(columns = {'amount':'amount', 'wallet':'user','ethdebt':'ethdebt'}) return results_df def get_user_stats(vuser): #gets information about user from Aave Lending pool contract abi = json.load(open("AaveLPpolygon.json")) contract = w3.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(w3.to_checksum_address(vuser)).call() return (collateral_eth, debt_eth, available_borrows_eth, current_liquidation_threshold, ltv, healthfactor, emode) def get_astmatic_balance(vuser): #gets current aSTMATIC balance of user abi = json.load(open("aSTMATIC.json")) contract = w3.eth.contract(address=ASTMATIC_CONTRACT, abi=abi) balance = contract.functions.balanceOf(Web3.to_checksum_address(vuser)).call() return balance def get_stmatic_usd_price(): #gets stMATIC:USD price from Aave Oracle abi = json.load(open("OracleSTMATIC.json")) contract = w3.eth.contract(address=AAVE_STMATIC_ORACLE_CONTRACT, abi=abi) price = contract.functions.latestAnswer().call() price = price / pow(10,8) return price def get_wmatic_usd_price(): #gets WMATIC:USD price from Aave Oracle abi = json.load(open("OracleWMATIC.json")) contract = w3.eth.contract(address=AAVE_WMATIC_ORACLE_CONTRACT, abi=abi) price = contract.functions.latestAnswer().call() price = price / pow(10,8) return price @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("VariableDebtTokenPolygon.json")) contract = w3.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("StableDebtTokenPolygon.json")) contract = w3.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_maticdebt_balance(df): buf = [] tasks = [(user, get_debt_balance(user,'0x4a1c3aD6Ed28a636ee1751C69071f6be75DEb8B8', '0xF15F26710c827DDe8ACBA678682F3Ce24f2Fb56E')) for user in df["user"]] for user, task in tasks: balance: float = task.result() # type: ignore buf.append({"user": user, "maticdebt": balance}) return buf def get_scale_dic(collateral_loan_ratio): return [i for i in collateral_loan_ratio] 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_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 = ['stMATIC','cnt'] risk_distr['stMATIC'] = risk_distr['stMATIC'] risk_distr['percent'] = (risk_distr['stMATIC']/risk_distr['stMATIC'].sum())*100 risk_distr['average'] = risk_distr['stMATIC']/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 matic_last_price(): #gets current MATIC price from coingecko req = requests.get(f'https://api.coingecko.com/api/v3/simple/token_price/ethereum?contract_addresses=0x7D1AfA7B718fb893dB30A3aBc0Cfc608AaCfeBB0&vs_currencies=usd&include_market_cap=false&include_24hr_vol=false&include_24hr_change=false&include_last_updated_at=false' ).json() price = req['0x7d1afa7b718fb893db30a3abc0cfc608aacfebb0']['usd'] return price def stmatic_last_price(): #gets current stMATIC price from coingecko req = requests.get(f'https://api.coingecko.com/api/v3/simple/token_price/polygon-pos?contract_addresses=0x3A58a54C066FdC0f2D55FC9C89F0415C92eBf3C4&vs_currencies=usd&include_market_cap=false&include_24hr_vol=false&include_24hr_change=false&include_last_updated_at=false' ).json() price = req['0x3a58a54c066fdc0f2d55fc9c89f0415c92ebf3c4']['usd'] return price

Aave V3 (Polygon) stMATIC Collateral Risk Monitor

Bin 1.1: users with e-mode and with >=80% collateral - stMATIC and >=80% debt - MATIC

Bin 1.2: users without e-mode and with >=80% collateral - stMATIC and >=80% debt - MATIC

Bin 2: Aave users with stMATIC collateral and >=80% debt - not MATIC

Bin 3: All other Aave V3 users with stMATIC collateral on Polygon