Compound USDT change proposal

_charts.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "civilian-advocacy",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "# import scipy\n",
    "import time\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "destroyed-composer",
   "metadata": {},
   "source": [
    "# Compound USDT rates visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "viral-survivor",
   "metadata": {},
   "outputs": [],
   "source": [
    "before = pd.read_json('before.json')\n",
    "after = pd.read_json('after.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "trying-semiconductor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x139a9a5e0>]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 520,
       "width": 703
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(12,9))\n",
    "ax = plt.gca()\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "\n",
    "plt.axhline(y=40, c=\"grey\", linewidth=0.5)\n",
    "plt.axhline(y=30, c=\"grey\", linewidth=0.5)\n",
    "plt.axvline(x=0, c=\"grey\", linewidth=0.5)\n",
    "plt.axvline(x=80, c=\"grey\", linewidth=0.5)\n",
    "plt.axvline(x=100, c=\"grey\", linewidth=0.5)\n",
    "\n",
    "plt.plot(before['pct'], before['borrow_apy']*100, c=\"grey\", linewidth=5)\n",
    "plt.plot(before['pct'], before['supply_apy']*100, c=\"grey\", linewidth=5)\n",
    "\n",
    "\n",
    "plt.plot(after['pct'], after['borrow_apy']*100, c=[.5882,.4118,.9294], linewidth=5)\n",
    "plt.plot(after['pct'], after['supply_apy']*100, c=[0,.8275,.5843], linewidth=5)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "forward-advertising",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}

after.json

{"pct": [100, 99, 98, 97, 96, 95, 94, 93, 92, 91, 90, 89, 88, 87, 86, 85, 84, 83, 82, 81, 80, 79, 78, 77, 76, 75, 74, 73, 72, 71, 70, 69, 68, 67, 66, 65, 64, 63, 62, 61, 60, 59, 58, 57, 56, 55, 54, 53, 52, 51, 50, 49, 48, 47, 46, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0], "supply": [147831050226, 139906249997, 132111681885, 124447345887, 116913242007, 109509370240, 102235730591, 95092323057, 88079147638, 81196204336, 74443493148, 67821014077, 61328767121, 54966752281, 48734969556, 42633418947, 36662100454, 30821014077, 25110159815, 19529537669, 14079147639, 13729368814, 13383989724, 13043010367, 12706430744, 12374250854, 12046470698, 11723090276, 11404109587, 11089528632, 10779347410, 10473565923, 10172184169, 9875202148, 9582619861, 9294437308, 9010654488, 8731271402, 8456288050, 8185704432, 7919520547, 7657736395, 7400351977, 7147367293, 6898782343, 6654597126, 6414811642, 6179425893, 5948439877, 5721853595, 5499667046, 5281880231, 5068493149, 4859505802, 4654918188, 4454730307, 4258942160, 4067553747, 3880565067, 3697976121, 3519786909, 3345997430, 3176607686, 3011617674, 2851027396, 2694836852, 2543046042, 2395654965, 2252663622, 2114072012, 1979880136, 1850087994, 1724695585, 1603702910, 1487109969, 1374916761, 1267123287, 1163729547, 1064735540, 970141266, 879946727, 794151921, 712756849, 635761510, 563165905, 494970034, 431173896, 371777492, 316780821, 266183885, 219986681, 178189212, 140791476, 107793474, 79195205, 54996670, 35197869, 19798801, 8799467, 2199866, 0], "borrow": [159817351596, 152777777776, 145738203956, 138698630135, 131659056315, 124619482494, 117579908674, 110540334854, 103500761033, 96461187213, 89421613392, 82382039572, 75342465752, 68302891931, 61263318111, 54223744290, 47184170470, 40144596650, 33105022829, 26065449009, 19025875189, 18788051749, 18550228309, 18312404869, 18074581430, 17836757990, 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before.json

{"pct": [100, 99, 98, 97, 96, 95, 94, 93, 92, 91, 90, 89, 88, 87, 86, 85, 84, 83, 82, 81, 80, 79, 78, 77, 76, 75, 74, 73, 72, 71, 70, 69, 68, 67, 66, 65, 64, 63, 62, 61, 60, 59, 58, 57, 56, 55, 54, 53, 52, 51, 50, 49, 48, 47, 46, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0], "supply": [113513127851, 107630244005, 101843274351, 96152218890, 90557077623, 85057850549, 79654537669, 74347138982, 69135654488, 64020084187, 59000428079, 54076686167, 49248858445, 44516944918, 39880945584, 35340860442, 30896689496, 26548432742, 22296090180, 18139661813, 14079147639, 13729368814, 13383989724, 13043010367, 12706430744, 12374250854, 12046470698, 11723090276, 11404109587, 11089528632, 10779347410, 10473565923, 10172184169, 9875202148, 9582619861, 9294437308, 9010654488, 8731271402, 8456288050, 8185704432, 7919520547, 7657736395, 7400351977, 7147367293, 6898782343, 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0.0010411667023879634, 0.0009069111821933262, 0.0007819305594012338, 0.0006662218310860002, 0.0005597817847113884, 0.0004626069982529568, 0.00037469524127109466, 0.0002960436065162497, 0.00022665084484541076, 0.00016651362942221581, 0.00011563169199435563, 7.40026866312693e-05, 4.162592493428541e-05, 1.8500041559121883e-05, 4.624861596802532e-06, 0.0]}

new_rate.py

# 1. Set your WEB3_INFURA_PROJECT_ID and ETHERSCAN_TOKEN environment varibles
# 2. Save this as a brownie script named new_rate.py
# 3. brownie run new_rate main --network mainnet-fork

from brownie import *
import math
import json

cusdt = Contract.from_explorer("0xf650C3d88D12dB855b8bf7D11Be6C55A4e07dCC9")
rates = Contract.from_explorer("0xfb564da37b41b2f6b6edcc3e56fbf523bd9f2012")

blocksPerYear = rates.blocksPerYear()
baseRatePerBlock = rates.baseRatePerBlock()
multiplierPerBlock = rates.multiplierPerBlock()
kink = rates.kink()
jumpMultiplierPerBlock = rates.jumpMultiplierPerBlock()
reserveFactorMantissa = cusdt.reserveFactorMantissa()
owner = rates.owner()

def apy(r):
    n = blocksPerYear
    r = r * blocksPerYear / 1e18
    return math.pow((1 + r/n ),n) - 1

def write_usage_rates(name):
    pct = []
    borrow = []
    supply = []
    borrow_apy = []
    supply_apy = []
    for i in range(0,101):
        pct.append(100-i)
        br = rates.getBorrowRate(i*1e32, (100-i)*1e32, 0)
        sr = rates.getSupplyRate(i*1e32, (100-i)*1e32, 0, reserveFactorMantissa)
        borrow.append(br)
        supply.append(sr)
        borrow_apy.append(apy(br))
        supply_apy.append(apy(sr))
    
    with(open(name, "w") as f):
        j = json.dumps({
            "pct":pct,
            "supply":supply,
            "borrow":borrow,
            "borrow_apy":borrow_apy,
            "supply_apy":supply_apy
            })
        f.write(j)
        print("100%% utilization rate: %f" % borrow_apy[0])

def try_jump_rate(jumpMultiplierPerYear):
    """Use exsisting deployed parameters except for jumpMultiplierPerYear."""
    rates.updateJumpRateModel(
        0,
        "0x000000000000000000000000000000000000000000000000008e1bc9bf040000",
        jumpMultiplierPerYear,
        "0x0000000000000000000000000000000000000000000000000b1a2bc2ec500000",
        {"from":owner}
    )
    br = rates.getBorrowRate(0*1e32, 100*1e32, 0)
    return apy(br)


def main():
    print("Rate before")
    write_usage_rates("before.json")

    print("Change to new rate")
    # Old value: 1090000000000000000
    print(try_jump_rate(1480000000000000000))

    print("Rate after")
    write_usage_rates("after.json")