fee_dump.py

from web3 import Web3
import json
import pandas as pd

w3 = Web3(Web3.HTTPProvider(f'https://arb-mainnet.g.alchemy.com/v2/L_HXVya-RV6U6SwLHWf1eUdlgwN20J-7'))

# we need data for the following strike and maturity
strike = 204169420152563078078024764
maturity = 1686830400

def fees(amount, txn_fees):
    # since shortAmount is already deducted of 3% fee, so we reconstruct actual value and then deduct
    return amount * (txn_fees/((2**16) - int((2**16 - 1) * 0.03))) 

# token0 is 0x7a5D193fE4ED9098F7EAdC99797087C96b002907 (plsARB)
# token1 is 0xFF970A61A04b1cA14834A43f5dE4533eBDDB5CC8 (USDC)

# strike = token1/token0
# if strike > (1 >> 128) then short is in denomination of token0
# else the denomination is in token1
# since latter condition is true, hence shortAmount is in USDC (which makes sense since plsARB spot price is 0.54)

with open("abi/TimeswapV2Pool.json") as f:
    abi = json.load(f)["abi"]
with open("abi/TimeswapV2PeripheryNoDexLendGivenPrincipal.json") as f:
    abi_lend_given_principal_nodex = json.load(f)["abi"]

pool = w3.eth.contract(address='0x58f6EC313C993e306e9F2aa4F91C3e32Cd3F3430', abi=abi)
print("[-] fetching Deleverage events")
delev_events = pool.events.Deleverage.get_logs(fromBlock=82775904)
mint_events = pool.events.Mint.get_logs(fromBlock=82775904)

df = pd.DataFrame(columns=(['txn', 'lent (in USDC)', 'fee earned (USDC, 3%)'] + [ f'{2.5*(i+1)}%' for i in range(int(80/2.5))]))
df_relative = pd.DataFrame(columns=(['txn', 'lent (in USDC)', 'fee earned (USDC, 3%)'] + [ f'{2.5*(i+1)}%' for i in range(int(80/2.5))]))

minted_amount = 0
for i in mint_events:
    if i.args.strike == strike and i.args.maturity == maturity:
        # print(i)
        minted_amount += i.args.long1Amount
duration = maturity - w3.eth.get_block(i.blockNumber).timestamp #2933817

fee = 0
for i in delev_events:
    if i.args.strike == strike and i.args.maturity == maturity:
        # print(i)
        fee += fees(i.args.shortAmount, int((2**16 - 1) * 0.03))
        df.loc[len(df)] = [f'https://arbiscan.io/tx/{i.transactionHash.hex()}', i.args.long1Amount/1e6, (fees(i.args.shortAmount, int((2**16 - 1) * 0.03))/1e6)] + [(fees(i.args.shortAmount, int((2**16 - 1) * (2.5 * (x+1))/100))/1e6) for x in range(int(80/2.5))]
        df_relative.loc[len(df)] = [f'https://arbiscan.io/tx/{i.transactionHash.hex()}', i.args.long1Amount/1e6, (fees(i.args.shortAmount, int((2**16 - 1) * 0.03))/1e6)] + [((fees(i.args.shortAmount, int((2**16 - 1) * (2.5 * (x+1))/100))/1e6))/(i.args.long1Amount/1e4) for x in range(int(80/2.5))]

print("-------Calculating APR------")
print("fee:", fee/1e6)
print("Principal Amount:", minted_amount/1e6)
# APR = (fee earned/principle amount) * (seconds in year/duration)
print(duration)
apr = (fee/minted_amount) * (31536000/duration)
print(f'APR: {apr * 100}%')

df.to_csv("test.csv")
df_relative.to_csv("test_relative.csv")