PyTokens ( income as yield)-floatingRR.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from decimal import Decimal\n",
    "import time \n",
    "import decimal\n",
    "math = decimal.Context()\n",
    "import numpy \n",
    "import random\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Token:       \n",
    "    def __init__(self, _name = \"\"):\n",
    "        self.balances = {}\n",
    "        self.totalSupply = 0\n",
    "        self._name = _name\n",
    "    \n",
    "    def mint(self, address, amount):\n",
    "        assert(amount > 0)\n",
    "        self.balances[address] = self.balances.get(address, 0) + amount\n",
    "        self.totalSupply += amount\n",
    "        return True\n",
    "        \n",
    "    def burn(self, address, amount):\n",
    "        assert(amount > 0)\n",
    "        if self.balances[address] < amount:\n",
    "            raise Exception(\"burn: Cannot burn more value than present at address. Requested: {} Available: {}\".format(amount, self.balances[address]))\n",
    "        self.balances[address] = self.balances.get(address, 0) - amount\n",
    "        self.totalSupply -= amount\n",
    "        return True\n",
    "    \n",
    "    def balanceOf(self, address):\n",
    "        return self.balances.get(address, 0)\n",
    "    \n",
    "    def name(self):\n",
    "        return self._name\n",
    "    \n",
    "    def transferFrom(self, sender, recipient, amount):\n",
    "        assert(amount >= 0)\n",
    "        # always round\n",
    "        if self.balanceOf(sender) >= amount:\n",
    "            self.balances[sender] = self.balances.get(sender, 0) - amount\n",
    "            self.balances[recipient] = self.balances.get(recipient, 0) + amount    \n",
    "            return True\n",
    "        else:\n",
    "            raise Exception(\"transferFrom: Cannot transfer more than balance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basic test of Token class\n",
    "\n",
    "token = Token()\n",
    "token.mint(\"A\", 5)\n",
    "assert(token.balanceOf(\"A\") == 5)\n",
    "\n",
    "token.transferFrom(\"A\", \"B\", 3)\n",
    "assert(token.balanceOf(\"B\") == 3)\n",
    "assert(token.balanceOf(\"A\") == 2)\n",
    "\n",
    "token.transferFrom(\"A\", \"C\", 2)\n",
    "assert(token.balanceOf(\"A\") == 0)\n",
    "assert(token.balanceOf(\"C\") == 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class blockchain:\n",
    "    \n",
    "    def __init__(self, _timestamp, _block):\n",
    "        self.timestamp = _timestamp\n",
    "        self.block = _block\n",
    "    \n",
    "    def incrementBlock(self):\n",
    "        self.block += 1\n",
    "        self.timestamp += 15\n",
    "    \n",
    "    def updateTimestamp(self, _timestamp):\n",
    "        self.timestamp = _timestamp\n",
    "    \n",
    "    def now(self):\n",
    "        return self.timestamp\n",
    "\n",
    "\n",
    "class InterestToken(Token):\n",
    "    # rate is a yearly rate, converted to a rate compounded per second\n",
    "    def __init__(self, _name, _blockchain, rate, _rateAccumulator):\n",
    "        Token.__init__(self, _name)\n",
    "        self.blockchain = _blockchain\n",
    "        self.lastUpdate = self.blockchain.now()\n",
    "        self.rateAccumulator = _rateAccumulator\n",
    "        # 31622400 is the number of seconds in a year\n",
    "        self.rate = math.power(1 + Decimal(rate), 1/Decimal(31622400))  \n",
    "        self.interestRateLimit = math.power(1 + Decimal(2), 1/Decimal(31622400)) \n",
    "    \n",
    "    def balanceOfUnderlying(self, address):\n",
    "        return self.balanceOf(address) * self.rateAccumulator\n",
    "    \n",
    "    def transferUnderlying(self, sender, recipient, amount):\n",
    "        value = amount / self.rateAccumulator\n",
    "        return self.transferFrom(sender, recipient, value)\n",
    "    \n",
    "    def totalSupplyUnderlying(self):\n",
    "        return self.totalSupply * self.rateAccumulator\n",
    "        \n",
    "    def accrueInterest(self):\n",
    "        now = self.blockchain.now()\n",
    "        if now > self.lastUpdate:\n",
    "            total_time =  now - self.lastUpdate\n",
    "            self.rateAccumulator = math.power(self.rate, total_time) * self.rateAccumulator\n",
    "            self.lastUpdate = now\n",
    "    \n",
    "    def updateRate(self, update):\n",
    "        print(\"Using old updateRate for some reason!\")\n",
    "        newRate = math.power(1 + Decimal(update), 1/Decimal(31622400)) - 1\n",
    "        self.rate = self.rate + newRate\n",
    "        if self.rate < Decimal(1):\n",
    "            self.rate = Decimal(1)\n",
    "        if self.rate > self.interestRateLimit:\n",
    "            self.rate = self.interestRateLimit\n",
    "    \n",
    "    def yearlyRate(self):\n",
    "        return math.power(self.rate, Decimal(31622400)) - 1\n",
    "    \n",
    "    def mintInUnderlying(self, address, amount):\n",
    "        return self.mint(address, amount/self.rateAccumulator)\n",
    "        \n",
    "    def burnInUnderlying(self, address, amount):\n",
    "        return self.burn(address, amount/self.rateAccumulator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basic tests of Interest Token Class\n",
    "\n",
    "chain = blockchain(0,1)\n",
    "token = InterestToken(\"Token\", chain, .02, 1)\n",
    "token.mint(\"A\", 1000)\n",
    "#token.lastUpdate = token.lastUpdate - 31622400\n",
    "chain.updateTimestamp(31622400)\n",
    "token.accrueInterest()\n",
    "token.balanceOfUnderlying(\"A\")\n",
    "assert(int(token.balanceOfUnderlying(\"A\")) == 1020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using old updateRate for some reason!\n",
      "Using old updateRate for some reason!\n",
      "Using old updateRate for some reason!\n",
      "Using old updateRate for some reason!\n",
      "Using old updateRate for some reason!\n"
     ]
    }
   ],
   "source": [
    "chain = blockchain(0,1)\n",
    "token = InterestToken(\"Token\", chain, .02, 1)\n",
    "token.mint(\"A\", 1000)\n",
    "#token.lastUpdate = token.lastUpdate - 31622400\n",
    "# Note that the per second rates are multiplied, not added\n",
    "token.updateRate(.001)\n",
    "token.updateRate(.001)\n",
    "token.updateRate(-.001)\n",
    "token.updateRate(.001)\n",
    "token.updateRate(.001)\n",
    "#print(token.yearlyRate())\n",
    "chain.updateTimestamp(31622400)\n",
    "#print(token.rate)\n",
    "token.accrueInterest()\n",
    "assert(int(token.balanceOfUnderlying(\"A\")) == 1023)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# class QX:\n",
    "#  mint(address, amount):\n",
    "#  redeem(address, amount):\n",
    "#   measureRatio(self)\n",
    "#\n",
    "class QX:\n",
    "    def __init__(self, _uToken : Token , _sToken : Token):\n",
    "        self.uToken = _uToken\n",
    "        self.pyToken = _sToken\n",
    "        self.underlying = Decimal('0')\n",
    "        self.synthetic  = Decimal('0')   # denominated in pyTokens\n",
    "        self.address = '0xQX_' + _uToken.name()\n",
    "        self.book = dict()\n",
    "\n",
    "    def mint(self, address, amount):\n",
    "        if amount < 0:\n",
    "            raise Exception(\"mint: Cannot sell a negative amount. Amount: {}\".format(amount))\n",
    "        result = self.uToken.transferFrom(address, self.address, amount)\n",
    "        if result:\n",
    "            self.underlying += amount\n",
    "            result = self.pyToken.mintInUnderlying( self.address, amount)\n",
    "            if not result:\n",
    "                raise Exception(\"mint: Minting pyTokens failed. Amount: {}\".format(amount))\n",
    "            result = self.pyToken.transferUnderlying(self.address, address, amount)\n",
    "            if not result:\n",
    "                raise Exception(\"mint: Token transfer out failed. Amount: {}\".format(amount))\n",
    "        else:\n",
    "            raise Exception(\"mint: Token transfer in failed. Amount: {}\".format(amount))\n",
    "    \n",
    "    def redeem(self, address, amount):\n",
    "        if amount < 0:\n",
    "            raise Exception(\"redeem: Cannot redeem a negative amount. Amount: {}\".format(amount))\n",
    "        if self.underlying < amount:\n",
    "            raise Exception(\"redeem: Cannot withdraw more than available in reserves\")\n",
    "        result = self.pyToken.burnInUnderlying(address, amount)\n",
    "        if result:\n",
    "            outResult = self.uToken.transferFrom(self.address, address, amount)\n",
    "            if not outResult:\n",
    "                raise Exception(\"redeem: Token transfer out failed\")\n",
    "            self.underlying -= amount\n",
    "        else:\n",
    "            raise Exception(\"redeem: Token transfer in failed. Amount: {}\".format(amount))\n",
    "    \n",
    "    \n",
    "    def measureRatio(self):\n",
    "        try:\n",
    "            if self.underlying <= 0:\n",
    "                return Decimal('0')\n",
    "            return Decimal(self.underlying) / Decimal(self.pyToken.getTotalDebt() + self.underlying)\n",
    "        except Exception as e:\n",
    "            print(\"underlying: {} TotalDebt:{}\".format(self.underlying, self.pyToken.getTotalDebt()))\n",
    "            raise e\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Oracle:\n",
    "    def __init__(self, _tokenA, _tokenB):\n",
    "        self.tokenA = _tokenA\n",
    "        self.tokenB = _tokenB\n",
    "        # Price is stored in terms of TokenB\n",
    "        self.price = 0 \n",
    "    \n",
    "    # Assumes price is in terms of Token B\n",
    "    def currentPrice(self):\n",
    "        return self.price\n",
    "    \n",
    "    # Assumes price is in terms of Token B\n",
    "    def setPrice(self, price):\n",
    "        self.price = price\n",
    "        \n",
    "    def setPriceInToken(self, token, price):\n",
    "        if token == self.tokenA:\n",
    "            self.price = 1 / price\n",
    "        else:\n",
    "            self.price = price\n",
    "            \n",
    "    def getPriceInToken(self, token):\n",
    "        if token == self.tokenA:\n",
    "            return Decimal('1') /self.price\n",
    "        else:     \n",
    "            return self.price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simple Test of Oracle\n",
    "ETH = Token(\"ETH\")\n",
    "DAI = Token(\"DAI\")\n",
    "oracle = Oracle(ETH, DAI)\n",
    "oracle.setPriceInToken(DAI, Decimal('100'))\n",
    "assert(oracle.getPriceInToken(DAI) == Decimal('100'))\n",
    "assert(oracle.getPriceInToken(ETH) == (Decimal('1')/Decimal('100')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Issues:\n",
    "# 1. it is possible to pay out more pyTokens that is offset by debt\n",
    "\n",
    "\n",
    "class pyToken (InterestToken):\n",
    "    def __init__(self, _blockchain, _underlying : Token, _oracle):  \n",
    "        super().__init__(\"\", _blockchain, Decimal('0'), Decimal('1'))\n",
    "        self.underlying = _underlying\n",
    "        self._name = \"py\" + self.underlying.name()\n",
    "        self.cdp = {self.underlying.name(): {}}\n",
    "        self.tokens = {}\n",
    "        self.address = '0x' + self.name()\n",
    "        # oracle for the pyToken <> pyToken pair\n",
    "        self.oracle = _oracle\n",
    "        self.qx = 0\n",
    "        self.interestUpdateAmount = Decimal('0.0001')\n",
    "        self.collateralizationRatio = Decimal('1.7')\n",
    "        self.debtAccumulator = self.rateAccumulator\n",
    "        self.debtRate = Decimal('1.0')\n",
    "        # totalDebt is normalized by the debtAccumulator\n",
    "        self.totalDebt = Decimal('0.0')\n",
    "        # bonus factor when liquidating\n",
    "        self.bonus = Decimal('1.1')\n",
    "        self.reservesTarget = Decimal('0.3')\n",
    "        self.adjustmentFreeWindow = Decimal('0.1')\n",
    "        # minimumSpread may be deprecated\n",
    "        self.minimumSpread = Decimal('0.1')\n",
    "        self.borrowFee = Decimal('0.1')\n",
    "        self.totalFeeIncome = Decimal('0.0')\n",
    "        self.impliedRate = Decimal('1.0')\n",
    "        self.blockchain = _blockchain\n",
    "        \n",
    "        \n",
    "    # Admin Functions\n",
    "    def setPair(self, _pairedpyToken : InterestToken):\n",
    "        self.pairedpyToken = _pairedpyToken  \n",
    "        self.tokens[self.pairedpyToken.name()] = self.pairedpyToken\n",
    "        self.cdp[self.pairedpyToken.name()] = {}\n",
    "        \n",
    "    def setQueueExchange(self,exchange):\n",
    "        self.qx = exchange\n",
    "        \n",
    "    def addCollateralType(self, token, oracle):\n",
    "        self.tokens[token.name()] = { 'token':token, 'oracle':oracle }\n",
    "        self.cdp[token.name()] = {}\n",
    "    \n",
    "    # User Functions\n",
    "    def addCollateral(self, token, address, amount):\n",
    "        if token not in self.tokens.keys():\n",
    "            return False\n",
    "        result = self.tokens[token]['token'].transferFrom(address, self.address, amount)\n",
    "        if result:\n",
    "            if address not in self.cdp[token]:\n",
    "                self.cdp[token][address] = {'amount':amount, 'debt':Decimal('0')}\n",
    "            else:\n",
    "                self.cdp[token][address]['amount'] += amount\n",
    "        else:\n",
    "            return False\n",
    "      \n",
    "    def withdrawCollateral(self, token, address, amount):\n",
    "        if token not in self.tokens.keys():\n",
    "            return False\n",
    "        # check oracle price\n",
    "        price = self.oracle.currentPrice()\n",
    "        minimumCollateral = self.oracle.currentPrice() * self.collateralizationRatio * \\\n",
    "                            (self.cdp[token][address]['debt'] * self.debtAccumulator - amount)\n",
    "        if self.cdp[token][address]['amount'] < minimumCollateral:\n",
    "            raise Exception(\"withdrawCollateral: Insufficient free capital to withdraw\")\n",
    "        result = self.tokens[token].transferFrom(self.address, address, amount)\n",
    "        if result:\n",
    "            self.cdp[token][address]['amount'] = self.cdp[token][address]['amount'] - amount\n",
    "    \n",
    "    # accrue for both debt and interest\n",
    "    def accrueInterest(self):\n",
    "        now = self.blockchain.now()\n",
    "        if now > self.lastUpdate:\n",
    "            total_time =  now - self.lastUpdate\n",
    "            accumulatedDebtInterestMultiplier = math.power(self.debtRate, total_time)\n",
    "            self.debtAccumulator = accumulatedDebtInterestMultiplier * self.debtAccumulator\n",
    "            # pay interest out to hodlers\n",
    "            newDebt = (accumulatedDebtInterestMultiplier - Decimal('1')) * (self.totalDebt * self.debtAccumulator)\n",
    "            # self.rateAccumulator = math.power(self.rate, total_time) * self.rateAccumulator\n",
    "            feeIncome = newDebt * self.borrowFee\n",
    "            self.totalFeeIncome += feeIncome\n",
    "            totalPyTokens = self.totalSupply * self.rateAccumulator\n",
    "            if self.totalSupply > Decimal('0'):\n",
    "                self.rateAccumulator = ((newDebt - feeIncome) + totalPyTokens)/self.totalSupply\n",
    "                self.impliedRate = math.power(((newDebt - feeIncome) + totalPyTokens)/totalPyTokens, Decimal(1)/Decimal(total_time))\n",
    "            else:\n",
    "                self.rateAccumulator = Decimal('1')\n",
    "            self.lastUpdate = now\n",
    "    \n",
    "    def yearlyRate(self):\n",
    "        return math.power(self.impliedRate, Decimal(31622400)) - 1\n",
    "    \n",
    "    # updateRate for both debt and interest\n",
    "    # this contract implicity assumes that this is called every block\n",
    "    def updateRate(self, update):\n",
    "        #newRate = math.power(1 + Decimal(update), 1/Decimal(31622400)) - 1\n",
    "        newRate = math.power(1 + Decimal(update), 1/Decimal(31622400)) \n",
    "        self.debtRate = self.debtRate * newRate\n",
    "        if self.debtRate < Decimal(1):\n",
    "            self.debtRate = Decimal(1)\n",
    "        if self.debtRate > self.interestRateLimit:\n",
    "            self.debtRate = self.interestRateLimit\n",
    "        if self.totalDebt == 0:\n",
    "            print(\"Reseting Rates\")\n",
    "            self.debtRate = Decimal(1)\n",
    "            self.rate = Decimal(1)\n",
    "            return\n",
    "        \n",
    "        # To-DO:\n",
    "        # Update rate to be equal to rate = (debt*debtRate - fees)/(debt + reserveRatioTarget * debt)\n",
    "        \n",
    "        #calculate pyToken rate. The \"1\" in the denominator is to avoid divide by zero. Can we do this better?\n",
    "        #debt = self.getTotalDebt()\n",
    "        #self.rate = Decimal(1) + ((self.debtRate-1) * debt - self.minimumSpread)/(1 + (debt + debt*self.reservesTarget))\n",
    "        #if self.rate < Decimal(1):\n",
    "        #    self.rate = Decimal(1)\n",
    "\n",
    "    def yearlyDebtRate(self):\n",
    "        return math.power(self.debtRate, Decimal(31622400)) - 1\n",
    "    \n",
    "    def getTotalDebt(self):\n",
    "        return self.totalDebt * self.debtAccumulator\n",
    "    \n",
    "    def getCollateral(self, token, address):\n",
    "        return self.cdp[token][address]['amount'] \n",
    "    \n",
    "    def getDebt(self, token, address):\n",
    "        return self.cdp[token][address]['debt'] \n",
    "    \n",
    "    def debtInUnderlying(self, token, address):\n",
    "        return self.cdp[token][address]['debt'] * self.debtAccumulator\n",
    "    \n",
    "    # borrow against a particular tokens worth of collateral\n",
    "    def borrow(self, token, address, amount):\n",
    "        if token not in self.tokens.keys():\n",
    "            raise Exception(\"Borrow: Token not found\")\n",
    "        if amount < 0:\n",
    "            raise Exception(\"Borrow: Cannot borrow negative amounts\")\n",
    "            self.tokens[token.name()] = { 'token':token, 'oracle':oracle }\n",
    "        price = self.tokens[token]['oracle'].getPriceInToken(self.underlying)\n",
    "        minimumCollateral = ( 1 / price) * self.collateralizationRatio * \\\n",
    "                            (self.cdp[token][address]['debt'] * self.debtAccumulator + amount)\n",
    "        if self.cdp[token][address]['amount'] < minimumCollateral:\n",
    "            raise Exception(\"Borrow: Insufficient collateral Available: {} Minimum Needed: {}\".format(self.cdp[token][address]['amount'], minimumCollateral))\n",
    "        # Add debt\n",
    "        self.cdp[token][address]['debt'] = self.cdp[token][address]['debt'] + Decimal(amount) / self.debtAccumulator\n",
    "        # Add borrow\n",
    "        self.mintInUnderlying(address, amount)\n",
    "        self.totalDebt += Decimal(amount) / self.debtAccumulator\n",
    "        try:\n",
    "            assert(self.balanceOfUnderlying(address) >= amount)\n",
    "        except:     \n",
    "            print(\"amount minted: {} balance: {}\".format(amount, self.balanceOfUnderlying(address)))\n",
    "        \n",
    "    def maximumBorrow(self, token, address):\n",
    "        price = self.tokens[token]['oracle'].getPriceInToken(self.underlying)\n",
    "        maximumBorrow = self.cdp[token][address]['amount'] * price * \\\n",
    "                         (1 / self.collateralizationRatio) \n",
    "        return maximumBorrow\n",
    "    \n",
    "\n",
    "       \n",
    "    def liquidate(self, token, addressToLiquidate, LiquidatorAddress, amount):\n",
    "        price = self.tokens[token]['oracle'].getPriceInToken(self.underlying)\n",
    "        maximumBorrow = self.maximumBorrow(token, addressToLiquidate)\n",
    "        debt = self.debtInUnderlying(token, addressToLiquidate)\n",
    "        if maximumBorrow < debt:\n",
    "            # We need to liquidate\n",
    "            if amount < debt:\n",
    "                raise Exception(\"liquidate: Must liquidate entire CDP at once\")\n",
    "            availableFunds = self.balanceOfUnderlying(LiquidatorAddress)\n",
    "            if availableFunds < amount:\n",
    "                raise Exception(\"liquidate: Insufficient funds to liquidate\")\n",
    "            \n",
    "            collateralToLiquidator = debt*self.bonus/price\n",
    "            if collateralToLiquidator > self.cdp[token][addressToLiquidate]['amount']:\n",
    "                raise Exception(\"liquidate: CDP liquidated while undercollateralized. Owed to Liquidator: {} Available: {}\".format(collateralToLiquidator, self.cdp[token][addressToLiquidate]['amount']))\n",
    "            # Only burn amount of debt--it's possible to specify more than debt and have liquidate succeed\n",
    "            self.burn(LiquidatorAddress, self.getDebt(token, addressToLiquidate))\n",
    "            # Transfer liquidated funds to Liquidator\n",
    "            self.tokens[token]['token'].transferFrom(self.address, LiquidatorAddress, collateralToLiquidator)\n",
    "            # Debit CDP account \n",
    "            self.cdp[token][addressToLiquidate]['amount'] -= collateralToLiquidator\n",
    "            # Wipe Debt\n",
    "            self.cdp[token][addressToLiquidate]['debt'] = 0\n",
    "            self.totalDebt -= Decimal(amount) / self.debtAccumulator\n",
    "        else:\n",
    "            raise Exception(\"liquidate: Cannot liquidate a working CDP\")\n",
    "        \n",
    "    # Position in a particular collateral token\n",
    "    def position(self, token, address):\n",
    "        return self.cdp[token][address]\n",
    "    \n",
    "    def repay(self, token, address, amount):\n",
    "        if token not in self.tokens.keys():\n",
    "            return False\n",
    "        if amount < 0:\n",
    "            return False        \n",
    "        if amount > self.debtInUnderlying(token, address):\n",
    "            raise Exception(\"repay: Cannot repay more than available debt\")\n",
    "        if amount > self.balanceOfUnderlying(address):\n",
    "            raise Exception(\"repay: Must have sufficient funds to repay.\")\n",
    "        # Decrease debt\n",
    "        result = self.burnInUnderlying(address, amount)\n",
    "        if not result:\n",
    "            raise Exception(\"repay: Burn Tokens failed\")\n",
    "        self.cdp[token][address]['debt'] = self.cdp[token][address]['debt'] - Decimal(amount) / self.debtAccumulator\n",
    "        self.totalDebt -= Decimal(amount) / self.debtAccumulator\n",
    "        assert(self.cdp[token][address]['debt'] >= 0)\n",
    "        \n",
    "    def updateRates(self):\n",
    "        if self.totalSupply == 0:\n",
    "            return\n",
    "        imbalance = self.qx.measureRatio()    \n",
    "        if imbalance > self.reservesTarget:\n",
    "            # lower interest rate\n",
    "            self.updateRate(-self.interestUpdateAmount)\n",
    "        elif imbalance < self.reservesTarget - self.adjustmentFreeWindow:\n",
    "            # raise interest rate\n",
    "            self.updateRate(self.interestUpdateAmount)\n",
    "\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.089910089910089989391554706\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Decimal('-1')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "debtRate = Decimal(1.1)\n",
    "totalDebt = Decimal(1000)\n",
    "minimumSpread = Decimal(.1)\n",
    "totalSupply = Decimal(1000)\n",
    "\n",
    "rate = Decimal(1) + ((debtRate-1) * totalDebt * (1 - minimumSpread))/(1 + totalSupply)\n",
    "print(rate)\n",
    "-Decimal('1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basic test of pyToken class\n",
    "\n",
    "chain = blockchain(1,1)\n",
    "BAT = Token(\"BAT\")\n",
    "DAI = Token(\"DAI\")\n",
    "oracleA = Oracle(BAT, DAI)\n",
    "\n",
    "collateral = Token(\"Collateral\")\n",
    "oracleC = Oracle(collateral, BAT)\n",
    "oracleC.setPrice(Decimal(1000000))\n",
    "pyBAT = pyToken(chain, BAT, oracleA)\n",
    "pyBAT.addCollateralType(collateral, oracleC)\n",
    "qx = QX(BAT , pyBAT)\n",
    "pyBAT.setQueueExchange(qx)\n",
    "\n",
    "oracleC.setPrice(Decimal(1000))\n",
    "BAT.mint(\"A\", Decimal('1000'))\n",
    "collateral.mint(\"B\", Decimal('1000'))\n",
    "pyBAT.addCollateral(\"Collateral\", \"B\", Decimal('100'))\n",
    "pyBAT.borrow(\"Collateral\", \"B\", Decimal('10'))\n",
    "pyBAT.repay(\"Collateral\", \"B\", Decimal('10'))\n",
    "pyBAT.borrow(\"Collateral\", \"B\", Decimal('10'))\n",
    "assert(pyBAT.balanceOf(\"B\") == Decimal('10'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Liquidation Test\n",
    "oracleC.getPriceInToken(collateral)\n",
    "debt = pyBAT.debtInUnderlying(\"Collateral\", \"B\")\n",
    "maxBorrow = pyBAT.maximumBorrow(\"Collateral\", \"B\")\n",
    "pyBAT.borrow(\"Collateral\", \"B\", maxBorrow-debt)\n",
    "# Now make repo undercollateralized\n",
    "oracleC.setPriceInToken(BAT, Decimal(900))\n",
    "pyBAT.mint(\"A\", Decimal('100000'))\n",
    "pyBAT.liquidate(\"Collateral\", \"B\", \"A\", pyBAT.debtInUnderlying(\"Collateral\", \"B\"))\n",
    "assert(pyBAT.debtInUnderlying(\"Collateral\", \"B\") == Decimal('0'))\n",
    "assert(collateral.balanceOf(\"A\") == Decimal('71.89542483660130718954248367'))\n",
    "assert(pyBAT.getCollateral(\"Collateral\", \"B\") + collateral.balanceOf(\"A\") == Decimal('100.0000000000000000000000000'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# External market that provides \"infinite\" liquidity\n",
    "class ReferenceExchange:\n",
    "    def __init__(self):\n",
    "        self.tokenPairs = {}\n",
    "        self.address = \"0xReferenceExchange\"\n",
    "    \n",
    "    # Add pair. The price is the number of tokenB per tokenA\n",
    "    def addPair(self, tokenA, tokenB):\n",
    "        firstToken = tokenB.name()\n",
    "        secondToken = tokenA.name()\n",
    "        if tokenA.name() > tokenB.name():\n",
    "            firstToken = tokenA.name()\n",
    "            secondToken = tokenB.name()\n",
    "        \n",
    "        if firstToken not in self.tokenPairs.keys():\n",
    "            self.tokenPairs[firstToken] = {secondToken: {}}\n",
    "        self.tokenPairs[firstToken][secondToken] = {'Base':tokenA, 'Quote':tokenB, 'Price':Decimal('0')}  \n",
    "        \n",
    "        # mint a lot of tokens for both sides\n",
    "        tokenA.mint(self.address, Decimal(1000000000))\n",
    "        tokenB.mint(self.address, Decimal(1000000000))\n",
    "\n",
    "    \n",
    "    def updatePairPrice(self, tokenA, tokenB, price):\n",
    "        firstToken = tokenB.name()\n",
    "        secondToken = tokenA.name()\n",
    "        if tokenA.name() > tokenB.name():\n",
    "            firstToken = tokenA.name()\n",
    "            secondToken = tokenB.name()\n",
    "        self.tokenPairs[firstToken][secondToken]['Price'] = price\n",
    "    \n",
    "    def trade(self, tokenA, tokenB, tokenAAmount, address):\n",
    "        # transfer in tokenA\n",
    "        result = tokenA.transferFrom(address, self.address, tokenAAmount)\n",
    "        if result: \n",
    "            firstToken = tokenB.name()\n",
    "            secondToken = tokenA.name()\n",
    "            if tokenA.name() > tokenB.name():\n",
    "                firstToken = tokenA.name()\n",
    "                secondToken = tokenB.name()\n",
    "            pair = self.tokenPairs[firstToken][secondToken]\n",
    "            multiplier = 0\n",
    "            if pair['Quote'].name() == tokenB.name():\n",
    "                multiplier = pair['Price']\n",
    "            else:\n",
    "                multiplier = 1/pair['Price']\n",
    "            # calculate amount of tokenB to send\n",
    "            tokens_to_send = tokenAAmount * multiplier\n",
    "            # send tokenB to address\n",
    "            transferR = tokenB.transferFrom(self.address, address, tokens_to_send)\n",
    "            return (tokens_to_send, transferR)\n",
    "        else:\n",
    "            raise Exception(\"Trade: Token transfer failed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Interface:\n",
    "    \n",
    "    def __init__(self, _chain : blockchain, _uTokenA : Token, _uTokenB : Token, oracle):\n",
    "        self.A = _uTokenA\n",
    "        self.B = _uTokenB\n",
    "        self.chain = _chain\n",
    "        self.oracle = oracle\n",
    "        self.pyA = pyToken(_chain, self.A, oracle)\n",
    "        self.pyB = pyToken(_chain, self.B, oracle)\n",
    "        self.pyA.setPair(self.pyB)\n",
    "        self.pyB.setPair(self.pyA)\n",
    "        self.qxA = QX(self.A , self.pyA)\n",
    "        self.qxB = QX(self.B , self.pyB)\n",
    "        self.pyA.setQueueExchange(self.qxA)\n",
    "        self.pyB.setQueueExchange(self.qxB)\n",
    "        # Add \"ETH\" as acceptable collateral\n",
    "        self.eth = Token(\"ETH\")\n",
    "        self.ethAOracle = Oracle(self.eth, self.A)\n",
    "        self.ethBOracle = Oracle(self.eth, self.B)\n",
    "        self.ethAOracle.setPrice(Decimal('100'))\n",
    "        self.ethBOracle.setPrice(Decimal(100/oracle.currentPrice()))\n",
    "        self.pyA.addCollateralType(self.eth, self.ethAOracle)\n",
    "        self.pyB.addCollateralType(self.eth, self.ethBOracle)\n",
    "        self.ref = ReferenceExchange()\n",
    "        self.ref.addPair(self.eth, self.A)\n",
    "        self.ref.updatePairPrice(self.eth, self.A, self.ethAOracle.currentPrice())\n",
    "        self.ref.addPair(self.eth, self.B)\n",
    "        self.ref.updatePairPrice(self.eth, self.B, self.ethBOracle.currentPrice())\n",
    "        \n",
    "    def updateInterestRates(self):\n",
    "        self.pyA.updateRates()\n",
    "        self.pyB.updateRates()\n",
    "        self.pyA.accrueInterest()\n",
    "        self.pyB.accrueInterest()\n",
    "    \n",
    "    def eachBlock(self):\n",
    "        self.chain.incrementBlock()\n",
    "        self.updateInterestRates()\n",
    "        \n",
    "        \n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Agent:\n",
    "    def __init__(self, _interface):\n",
    "        self.interface = _interface\n",
    "        self.address = numpy.random.randint(1,1000)\n",
    "        \n",
    "    def setup(self):\n",
    "        pass\n",
    "    \n",
    "    def performActions(self):\n",
    "        pass\n",
    "    \n",
    "    def shutdown(self):\n",
    "        pass\n",
    "    \n",
    "\n",
    "class YieldAgent(Agent):\n",
    "    def __init__(self, _interface):\n",
    "        super().__init__(_interface)\n",
    "        self.address = \"0xYA_HodlPY\" + str(self.address)\n",
    "        self.block = 0\n",
    "        \n",
    "    def setup(self):   \n",
    "        self.initialA = min(Decimal(round(numpy.random.lognormal(6, 1))), Decimal(1000))\n",
    "        self.initialB = min(Decimal(round(numpy.random.lognormal(6, 1))), Decimal(1000))\n",
    "        #self.initialA =  Decimal(1000000000)\n",
    "        #self.initialB =  Decimal(1000000000)\n",
    "        self.interface.A.mint(self.address, Decimal(self.initialA))\n",
    "        self.interface.B.mint(self.address, Decimal(self.initialB))\n",
    "        self.initial_exchange_rate = self.interface.oracle.currentPrice()\n",
    "        self.lowerRate = Decimal(numpy.random.uniform(0,7) / 100)\n",
    "        self.upperRate = self.lowerRate + Decimal(numpy.random.uniform(0,5) / 100)\n",
    "    \n",
    "    def performActions(self):\n",
    "        if self.interface.pyA.yearlyRate() > self.upperRate:\n",
    "            self.HODL()\n",
    "        elif self.interface.pyA.yearlyRate() < self.lowerRate:\n",
    "            self.unHODL()\n",
    "            \n",
    "    def HODL(self):\n",
    "        self.block = self.interface.chain.block\n",
    "        if self.interface.A.balanceOf(self.address) > 0:\n",
    "            aBalance = self.interface.A.balanceOf(self.address)\n",
    "            self.interface.qxA.mint(self.address, aBalance)\n",
    "            print(\"HODLing {} tokens at rate {}\".format(aBalance, round(self.upperRate, 4)))\n",
    "            \n",
    "    def unHODL(self):\n",
    "        if self.block - self.interface.chain.block < 10:\n",
    "            return\n",
    "        amount = min(self.interface.pyA.balanceOfUnderlying(self.address), self.interface.qxA.underlying)\n",
    "        amount = amount*Decimal('0.1')\n",
    "        if amount > 0:\n",
    "            # let's only remove 10% at a time\n",
    "            self.interface.qxA.redeem(self.address, amount)\n",
    "            print(\"unHODLing {} tokens\".format(amount*Decimal('0.99'), round(self.lowerRate, 4)))\n",
    "            \n",
    "            \n",
    "# Borrow agents lever up on ETH collateral by selling pyTokens\n",
    "class BorrowAgent(Agent):\n",
    "    def __init__(self, _interface):\n",
    "        super().__init__(_interface)\n",
    "        self.address = \"0xBA_longETH\" + str(self.address)\n",
    "        \n",
    "    def setup(self):\n",
    "        self.initialETH = min(Decimal(round(numpy.random.lognormal(3, 1))), Decimal(100))\n",
    "        self.interface.eth.mint(self.address, Decimal(self.initialETH ))\n",
    "        ethReserve = self.initialETH * Decimal('0.1')\n",
    "        self.each = (self.initialETH - ethReserve)/2\n",
    "        self.interface.pyA.addCollateral(\"ETH\", self.address, self.each )\n",
    "        self.interface.pyB.addCollateral(\"ETH\", self.address, self.each )\n",
    "        price = self.interface.ethAOracle.getPriceInToken(self.interface.A)\n",
    "        self.desiredBorrow = Decimal('0.5')*self.interface.pyA.maximumBorrow(\"ETH\", self.address)\n",
    "        self.interface.A.mint(self.address, self.desiredBorrow*1000)\n",
    "        self.lowerRate = Decimal(numpy.random.uniform(0,10) / 100)\n",
    "        self.upperRate = self.lowerRate + Decimal(numpy.random.uniform(0,5) / 100)\n",
    "    \n",
    "    def performActions(self):\n",
    "        if self.interface.pyA.yearlyDebtRate() > self.upperRate:\n",
    "            self.unBorrow()\n",
    "        elif self.interface.pyA.yearlyDebtRate() < self.lowerRate:\n",
    "            self.borrow()\n",
    "        uAmount = min(self.interface.pyA.balanceOfUnderlying(self.address), self.interface.qxA.underlying)\n",
    "        if uAmount > Decimal('0.1'):\n",
    "            self.interface.qxA.redeem(self.address, uAmount*Decimal(.99))\n",
    "            print(\"Withdrew {} tokens at {}\".format(round(uAmount*Decimal(.99), 4), round(self.lowerRate, 4) ))\n",
    "            \n",
    "            \n",
    "    def borrow(self):\n",
    "        price = self.interface.ethAOracle.getPriceInToken(self.interface.A)\n",
    "        #if we've already mostly borrowed what we desire, let's stop\n",
    "        if self.interface.pyA.debtInUnderlying(\"ETH\", self.address) > Decimal('.99') * self.desiredBorrow:\n",
    "            return\n",
    "        #initiate borrow\n",
    "        borrowRatio = Decimal('0.5')\n",
    "        amount = self.interface.pyA.maximumBorrow(\"ETH\", self.address) - self.interface.pyA.debtInUnderlying(\"ETH\", self.address)\n",
    "        borrow = (amount * borrowRatio) / (self.interface.pyA.collateralizationRatio )\n",
    "        if borrow > Decimal('0.1'):\n",
    "            self.interface.pyA.borrow(\"ETH\", self.address, borrow)\n",
    "            print(\"Borrowed {} tokens at {}\".format(round(borrow, 4), round(self.lowerRate, 4) ))\n",
    "\n",
    "    \n",
    "    def unBorrow(self):\n",
    "        amount = Decimal('0.95') * self.interface.pyA.debtInUnderlying(\"ETH\", self.address)\n",
    "        #funds = self.interface.A.balanceOf(self.address)\n",
    "        #toUnBorrow = min(debt, funds)\n",
    "        if amount > Decimal('0.1'):\n",
    "            try:\n",
    "                self.interface.qxA.mint(self.address, amount*Decimal(1.01))\n",
    "                self.interface.pyA.repay(\"ETH\", self.address, amount)\n",
    "                print(\"unBorrowed {} tokens at {}\".format(round(amount, 4), round(self.upperRate, 4) ))\n",
    "            except Exception as e: \n",
    "                print(\"failed to unborrow. amount:{} underlying balance: {} pyTokens: {} \".format(amount, self.interface.A.balanceOf(self.address), self.interface.pyA.balanceOfUnderlying(self.address)))\n",
    "                raise e\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Borrowed 54.4983 tokens at 0.0688\n",
      "Borrowed 85.6401 tokens at 0.0663\n",
      "Borrowed 38.4694 tokens at 0.0688\n",
      "Borrowed 60.4519 tokens at 0.0663\n",
      "Borrowed 132.3529 tokens at 0.0697\n",
      "Borrowed 93.4256 tokens at 0.0697\n",
      "Borrowed 171.2803 tokens at 0.0648\n",
      "Borrowed 120.9037 tokens at 0.0648\n",
      "Borrowed 108.9965 tokens at 0.0749\n",
      "Borrowed 76.9387 tokens at 0.0749\n",
      "HODLing 1000 tokens at rate 0.0092\n",
      "Withdrew 92.0380 tokens at 0.0688\n",
      "Withdrew 144.6312 tokens at 0.0663\n",
      "Withdrew 223.5209 tokens at 0.0697\n",
      "Withdrew 289.2623 tokens at 0.0648\n",
      "Withdrew 184.0760 tokens at 0.0749\n",
      "Withdrew 0.9204 tokens at 0.0688\n",
      "Withdrew 1.4463 tokens at 0.0663\n",
      "Withdrew 2.2352 tokens at 0.0697\n",
      "Withdrew 2.8926 tokens at 0.0648\n",
      "Withdrew 1.8408 tokens at 0.0749\n",
      "Borrowed 155.7093 tokens at 0.0738\n",
      "Withdrew 56.5649 tokens at 0.0738\n",
      "Borrowed 109.9125 tokens at 0.0738\n",
      "Withdrew 0.5656 tokens at 0.0738\n",
      "Borrowed 700.6920 tokens at 0.0973\n",
      "Borrowed 494.6061 tokens at 0.0973\n",
      "Borrowed 607.2664 tokens at 0.0811\n",
      "Borrowed 428.6587 tokens at 0.0811\n",
      "Borrowed 54.4983 tokens at 0.0856\n",
      "Borrowed 38.4694 tokens at 0.0856\n",
      "Borrowed 132.3529 tokens at 0.0451\n",
      "Borrowed 93.4256 tokens at 0.0451\n",
      "HODLing 117 tokens at rate 0.0402\n",
      "Withdrew 115.8357 tokens at 0.0738\n",
      "Withdrew 1.1584 tokens at 0.0973\n",
      "unBorrowed 214.4901 tokens at 0.0502\n",
      "Withdrew 214.4802 tokens at 0.0451\n",
      "Withdrew 2.1448 tokens at 0.0738\n",
      "unBorrowed 10.7245 tokens at 0.0502\n",
      "Withdrew 10.7449 tokens at 0.0451\n",
      "HODLing 468 tokens at rate 0.0453\n",
      "Withdrew 89.6063 tokens at 0.0738\n",
      "Withdrew 374.7172 tokens at 0.0973\n",
      "Withdrew 3.7472 tokens at 0.0811\n",
      "unBorrowed 0.5362 tokens at 0.0502\n",
      "Withdrew 0.5736 tokens at 0.0451\n",
      "HODLing 1000 tokens at rate 0.0461\n",
      "Withdrew 0.8961 tokens at 0.0738\n",
      "Withdrew 811.2328 tokens at 0.0973\n",
      "Withdrew 185.9981 tokens at 0.0811\n",
      "Withdrew 1.8600 tokens at 0.0856\n",
      "HODLing 228 tokens at rate 0.0487\n",
      "Withdrew 8.1123 tokens at 0.0973\n",
      "Withdrew 217.7074 tokens at 0.0811\n",
      "Withdrew 2.1771 tokens at 0.0856\n",
      "HODLing 187 tokens at rate 0.0499\n",
      "Withdrew 185.1518 tokens at 0.0811\n",
      "Withdrew 1.8515 tokens at 0.0856\n",
      "HODLing 65 tokens at rate 0.0357\n",
      "Withdrew 64.3685 tokens at 0.0811\n",
      "Withdrew 0.6437 tokens at 0.0856\n",
      "HODLing 858 tokens at rate 0.0508\n",
      "Withdrew 375.1670 tokens at 0.0811\n",
      "Withdrew 85.5714 tokens at 0.0856\n",
      "Withdrew 2.2154 tokens at 0.0451\n",
      "Withdrew 3.7517 tokens at 0.0811\n",
      "Withdrew 0.8557 tokens at 0.0856\n",
      "HODLing 293 tokens at rate 0.0560\n",
      "Borrowed 54.4983 tokens at 0.0566\n",
      "Withdrew 53.9533 tokens at 0.0566\n",
      "Borrowed 38.4694 tokens at 0.0566\n",
      "Withdrew 38.6242 tokens at 0.0566\n",
      "Withdrew 0.3862 tokens at 0.0566\n",
      "Borrowed 140.1384 tokens at 0.0759\n",
      "Withdrew 138.7370 tokens at 0.0759\n",
      "Borrowed 98.9212 tokens at 0.0759\n",
      "Withdrew 99.3194 tokens at 0.0759\n",
      "Withdrew 0.9932 tokens at 0.0759\n",
      "HODLing 869 tokens at rate 0.0468\n",
      "Borrowed 140.1384 tokens at 0.0541\n",
      "Withdrew 138.7370 tokens at 0.0541\n",
      "Borrowed 98.9212 tokens at 0.0541\n",
      "Withdrew 99.3194 tokens at 0.0541\n",
      "Withdrew 0.9932 tokens at 0.0541\n",
      "Borrowed 132.3446 tokens at 0.0451\n",
      "Withdrew 131.0433 tokens at 0.0451\n",
      "Borrowed 93.4197 tokens at 0.0451\n",
      "Withdrew 93.7960 tokens at 0.0451\n",
      "Withdrew 0.9380 tokens at 0.0451\n",
      "Borrowed 210.2076 tokens at 0.0267\n",
      "Withdrew 208.1055 tokens at 0.0267\n",
      "Withdrew 2.0811 tokens at 0.0267\n",
      "Borrowed 202.4221 tokens at 0.0980\n",
      "Withdrew 200.3979 tokens at 0.0980\n",
      "Borrowed 142.8862 tokens at 0.0980\n",
      "Withdrew 143.4613 tokens at 0.0980\n",
      "Withdrew 1.4346 tokens at 0.0980\n",
      "HODLing 150 tokens at rate 0.0326\n",
      "Borrowed 373.7024 tokens at 0.0836\n",
      "Withdrew 346.6234 tokens at 0.0836\n",
      "Borrowed 263.7899 tokens at 0.0836\n",
      "Withdrew 3.4662 tokens at 0.0836\n",
      "unBorrowed 214.5045 tokens at 0.0502\n",
      "Withdrew 2.1330 tokens at 0.0451\n",
      "Withdrew 212.4061 tokens at 0.0836\n",
      "unBorrowed 10.7252 tokens at 0.0502\n",
      "Withdrew 0.1275 tokens at 0.0451\n",
      "Withdrew 12.7220 tokens at 0.0836\n",
      "unBorrowed 0.5363 tokens at 0.0502\n",
      "Withdrew 0.6634 tokens at 0.0836\n",
      "unBorrowed 199.6983 tokens at 0.0561\n",
      "Withdrew 1.9978 tokens at 0.0267\n",
      "Withdrew 60.9954 tokens at 0.0836\n",
      "unBorrowed 9.9849 tokens at 0.0561\n",
      "Withdrew 0.1188 tokens at 0.0267\n",
      "Withdrew 0.6100 tokens at 0.0836\n",
      "unBorrowed 0.4992 tokens at 0.0561\n",
      "Borrowed 778.5467 tokens at 0.0614\n",
      "Withdrew 147.0833 tokens at 0.0614\n",
      "Borrowed 549.5624 tokens at 0.0614\n",
      "Withdrew 1.4708 tokens at 0.0614\n",
      "unBorrowed 1261.7053 tokens at 0.0617\n",
      "Withdrew 1180.2517 tokens at 0.0614\n",
      "unBorrowed 63.0853 tokens at 0.0617\n",
      "Withdrew 12.4271 tokens at 0.0614\n",
      "unBorrowed 3.1543 tokens at 0.0617\n",
      "Withdrew 0.1555 tokens at 0.0614\n",
      "unBorrowed 0.1577 tokens at 0.0617\n",
      "unBorrowed 227.1097 tokens at 0.0682\n",
      "Withdrew 2.2583 tokens at 0.0541\n",
      "unBorrowed 11.3555 tokens at 0.0682\n",
      "Withdrew 0.1350 tokens at 0.0541\n",
      "unBorrowed 0.5678 tokens at 0.0682\n",
      "unBorrowed 88.3219 tokens at 0.0698\n",
      "Withdrew 0.8836 tokens at 0.0688\n",
      "unBorrowed 4.4161 tokens at 0.0698\n",
      "unBorrowed 0.2208 tokens at 0.0698\n",
      "HODLing 425 tokens at rate 0.0596\n",
      "Borrowed 54.4949 tokens at 0.0688\n",
      "Withdrew 54.0046 tokens at 0.0688\n",
      "Borrowed 38.4670 tokens at 0.0688\n",
      "Withdrew 38.6223 tokens at 0.0688\n",
      "Withdrew 0.3862 tokens at 0.0688\n",
      "Borrowed 778.5443 tokens at 0.0614\n",
      "Withdrew 770.7619 tokens at 0.0614\n",
      "Withdrew 7.7076 tokens at 0.0614\n",
      "unBorrowed 739.6250 tokens at 0.0617\n",
      "Withdrew 7.3994 tokens at 0.0614\n",
      "Borrowed 767.0974 tokens at 0.0614\n",
      "Withdrew 759.5004 tokens at 0.0614\n",
      "Withdrew 7.5950 tokens at 0.0614\n",
      "unBorrowed 765.7239 tokens at 0.0617\n",
      "Withdrew 7.6566 tokens at 0.0614\n",
      "Borrowed 766.6934 tokens at 0.0614\n",
      "Withdrew 759.1030 tokens at 0.0614\n",
      "Withdrew 7.5910 tokens at 0.0614\n",
      "unBorrowed 766.6450 tokens at 0.0617\n",
      "Withdrew 7.6657 tokens at 0.0614\n",
      "Borrowed 766.6791 tokens at 0.0614\n",
      "Withdrew 759.0890 tokens at 0.0614\n",
      "Withdrew 7.5909 tokens at 0.0614\n",
      "unBorrowed 766.6775 tokens at 0.0617\n",
      "Withdrew 7.6660 tokens at 0.0614\n",
      "Borrowed 766.6786 tokens at 0.0614\n",
      "Withdrew 759.0885 tokens at 0.0614\n",
      "Withdrew 7.5909 tokens at 0.0614\n",
      "unBorrowed 766.6787 tokens at 0.0617\n",
      "Withdrew 7.6660 tokens at 0.0614\n",
      "Borrowed 766.6786 tokens at 0.0614\n",
      "Withdrew 759.0885 tokens at 0.0614\n",
      "Withdrew 7.5909 tokens at 0.0614\n",
      "unBorrowed 766.6787 tokens at 0.0617\n",
      "Withdrew 7.6660 tokens at 0.0614\n",
      "Borrowed 766.6786 tokens at 0.0614\n",
      "Withdrew 759.0885 tokens at 0.0614\n",
      "Withdrew 7.5909 tokens at 0.0614\n",
      "unBorrowed 766.6787 tokens at 0.0617\n",
      "Withdrew 7.6660 tokens at 0.0614\n",
      "Borrowed 766.6786 tokens at 0.0614\n",
      "Withdrew 759.0885 tokens at 0.0614\n",
      "Withdrew 7.5909 tokens at 0.0614\n",
      "unBorrowed 766.6787 tokens at 0.0617\n",
      "Withdrew 7.6660 tokens at 0.0614\n",
      "Borrowed 766.6786 tokens at 0.0614\n",
      "Withdrew 759.0885 tokens at 0.0614\n",
      "Withdrew 7.5909 tokens at 0.0614\n",
      "unBorrowed 766.6787 tokens at 0.0617\n",
      "Withdrew 7.6660 tokens at 0.0614\n",
      "Borrowed 766.6786 tokens at 0.0614\n",
      "Withdrew 759.0885 tokens at 0.0614\n",
      "Withdrew 7.5909 tokens at 0.0614\n",
      "unBorrowed 766.6787 tokens at 0.0617\n",
      "Withdrew 7.6660 tokens at 0.0614\n",
      "Borrowed 766.6786 tokens at 0.0614\n",
      "Withdrew 759.0885 tokens at 0.0614\n",
      "Withdrew 7.5909 tokens at 0.0614\n",
      "unBorrowed 766.6787 tokens at 0.0617\n",
      "Withdrew 7.6660 tokens at 0.0614\n",
      "Borrowed 766.6786 tokens at 0.0614\n",
      "Withdrew 759.0885 tokens at 0.0614\n",
      "Withdrew 7.5909 tokens at 0.0614\n",
      "HODLing 1000 tokens at rate 0.0538\n",
      "Borrowed 541.1849 tokens at 0.0614\n",
      "Withdrew 535.8490 tokens at 0.0614\n",
      "Withdrew 5.3585 tokens at 0.0614\n",
      "unBorrowed 1280.8045 tokens at 0.0617\n",
      "Withdrew 12.7335 tokens at 0.0614\n",
      "Withdrew 0.1273 tokens at 0.0614\n",
      "Borrowed 758.7200 tokens at 0.0614\n",
      "Withdrew 751.1341 tokens at 0.0614\n",
      "Borrowed 535.5671 tokens at 0.0614\n",
      "Withdrew 537.7227 tokens at 0.0614\n",
      "Withdrew 5.3772 tokens at 0.0614\n",
      "unBorrowed 1293.6132 tokens at 0.0617\n",
      "Withdrew 12.8605 tokens at 0.0614\n",
      "Withdrew 0.1286 tokens at 0.0614\n",
      "Borrowed 758.5217 tokens at 0.0614\n",
      "Withdrew 750.9378 tokens at 0.0614\n",
      "Borrowed 535.4271 tokens at 0.0614\n",
      "Withdrew 537.5822 tokens at 0.0614\n",
      "Withdrew 5.3758 tokens at 0.0614\n",
      "unBorrowed 1293.9323 tokens at 0.0617\n",
      "Withdrew 12.8637 tokens at 0.0614\n",
      "Withdrew 0.1286 tokens at 0.0614\n",
      "Borrowed 758.5168 tokens at 0.0614\n",
      "Withdrew 750.9329 tokens at 0.0614\n",
      "Borrowed 535.4236 tokens at 0.0614\n",
      "Withdrew 537.5787 tokens at 0.0614\n",
      "Withdrew 5.3758 tokens at 0.0614\n",
      "unBorrowed 1293.9402 tokens at 0.0617\n",
      "Withdrew 12.8638 tokens at 0.0614\n",
      "Withdrew 0.1286 tokens at 0.0614\n",
      "Borrowed 758.5167 tokens at 0.0614\n",
      "Withdrew 750.9328 tokens at 0.0614\n",
      "Borrowed 535.4235 tokens at 0.0614\n",
      "Withdrew 537.5786 tokens at 0.0614\n",
      "Withdrew 5.3758 tokens at 0.0614\n",
      "unBorrowed 1293.9404 tokens at 0.0617\n",
      "Withdrew 12.8638 tokens at 0.0614\n",
      "Withdrew 0.1286 tokens at 0.0614\n",
      "Borrowed 758.5167 tokens at 0.0614\n",
      "Withdrew 750.9328 tokens at 0.0614\n",
      "Borrowed 535.4235 tokens at 0.0614\n",
      "Withdrew 537.5786 tokens at 0.0614\n",
      "Withdrew 5.3758 tokens at 0.0614\n",
      "unBorrowed 1293.9404 tokens at 0.0617\n",
      "Withdrew 12.8638 tokens at 0.0614\n",
      "Withdrew 0.1286 tokens at 0.0614\n",
      "Borrowed 758.5167 tokens at 0.0614\n",
      "Withdrew 750.9328 tokens at 0.0614\n",
      "Borrowed 535.4235 tokens at 0.0614\n",
      "Withdrew 537.5786 tokens at 0.0614\n",
      "Withdrew 5.3758 tokens at 0.0614\n",
      "unBorrowed 1293.9404 tokens at 0.0617\n",
      "Withdrew 12.8638 tokens at 0.0614\n",
      "Withdrew 0.1286 tokens at 0.0614\n",
      "Borrowed 758.5167 tokens at 0.0614\n",
      "Withdrew 750.9328 tokens at 0.0614\n",
      "Borrowed 535.4235 tokens at 0.0614\n",
      "Withdrew 537.5786 tokens at 0.0614\n",
      "Withdrew 5.3758 tokens at 0.0614\n",
      "unBorrowed 1293.9404 tokens at 0.0617\n",
      "Withdrew 12.8638 tokens at 0.0614\n",
      "HODLing 402 tokens at rate 0.0467\n",
      "Withdrew 0.1286 tokens at 0.0614\n",
      "Borrowed 758.5167 tokens at 0.0614\n",
      "Withdrew 750.9328 tokens at 0.0614\n",
      "Borrowed 535.4235 tokens at 0.0614\n",
      "Withdrew 537.5786 tokens at 0.0614\n",
      "Withdrew 5.3758 tokens at 0.0614\n",
      "Borrowed 140.1296 tokens at 0.0541\n",
      "Withdrew 138.7353 tokens at 0.0541\n",
      "Withdrew 1.3874 tokens at 0.0541\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unBorrowed 1293.9451 tokens at 0.0617\n",
      "Withdrew 12.8638 tokens at 0.0614\n",
      "Withdrew 0.1286 tokens at 0.0614\n",
      "Borrowed 758.5166 tokens at 0.0614\n",
      "Withdrew 750.9327 tokens at 0.0614\n",
      "Borrowed 535.4235 tokens at 0.0614\n",
      "Withdrew 537.5786 tokens at 0.0614\n",
      "Withdrew 5.3758 tokens at 0.0614\n",
      "unBorrowed 1293.9405 tokens at 0.0617\n",
      "Withdrew 12.8638 tokens at 0.0614\n",
      "Withdrew 0.1286 tokens at 0.0614\n",
      "HODLing 390 tokens at rate 0.0346\n",
      "Borrowed 758.5167 tokens at 0.0614\n",
      "Withdrew 750.9328 tokens at 0.0614\n",
      "Borrowed 535.4235 tokens at 0.0614\n",
      "Withdrew 537.5786 tokens at 0.0614\n",
      "Withdrew 5.3758 tokens at 0.0614\n",
      "Borrowed 98.9149 tokens at 0.0541\n",
      "Withdrew 97.9396 tokens at 0.0541\n",
      "Withdrew 0.9794 tokens at 0.0541\n",
      "HODLing 1000 tokens at rate 0.0349\n",
      "Borrowed 132.3446 tokens at 0.0451\n",
      "Withdrew 131.0278 tokens at 0.0451\n",
      "Borrowed 93.4197 tokens at 0.0451\n",
      "Withdrew 93.7958 tokens at 0.0451\n",
      "Withdrew 0.9380 tokens at 0.0451\n",
      "Borrowed 225.7785 tokens at 0.0446\n",
      "Withdrew 223.5208 tokens at 0.0446\n",
      "Borrowed 159.3731 tokens at 0.0446\n",
      "Withdrew 160.0146 tokens at 0.0446\n",
      "Withdrew 1.6001 tokens at 0.0446\n",
      "HODLing 101 tokens at rate 0.0281\n",
      "Borrowed 295.8478 tokens at 0.0993\n",
      "Withdrew 292.8893 tokens at 0.0993\n",
      "Borrowed 208.8337 tokens at 0.0993\n",
      "Withdrew 209.6743 tokens at 0.0993\n",
      "Withdrew 2.0967 tokens at 0.0993\n",
      "Borrowed 93.4256 tokens at 0.0511\n",
      "Withdrew 92.4913 tokens at 0.0511\n",
      "Borrowed 65.9475 tokens at 0.0511\n",
      "Withdrew 66.2129 tokens at 0.0511\n",
      "Withdrew 0.6621 tokens at 0.0511\n",
      "Borrowed 31.1419 tokens at 0.0970\n",
      "Withdrew 30.8304 tokens at 0.0970\n",
      "Borrowed 21.9825 tokens at 0.0970\n",
      "Withdrew 22.0710 tokens at 0.0970\n",
      "Withdrew 0.2207 tokens at 0.0970\n",
      "Borrowed 350.3460 tokens at 0.0867\n",
      "Withdrew 346.8426 tokens at 0.0867\n",
      "HODLing 884 tokens at rate 0.0332\n",
      "Borrowed 247.3031 tokens at 0.0867\n",
      "Withdrew 248.2985 tokens at 0.0867\n",
      "Withdrew 2.4830 tokens at 0.0867\n",
      "HODLing 882 tokens at rate 0.0208\n",
      "Borrowed 210.1999 tokens at 0.0267\n",
      "Withdrew 208.1040 tokens at 0.0267\n",
      "Borrowed 148.3764 tokens at 0.0267\n",
      "Withdrew 148.9737 tokens at 0.0267\n",
      "Withdrew 1.4897 tokens at 0.0267\n",
      "Borrowed 77.8547 tokens at 0.0255\n",
      "Withdrew 77.0761 tokens at 0.0255\n",
      "Borrowed 54.9562 tokens at 0.0255\n",
      "Withdrew 55.1774 tokens at 0.0255\n",
      "Withdrew 0.5518 tokens at 0.0255\n",
      "Borrowed 124.5675 tokens at 0.0243\n",
      "Withdrew 123.3218 tokens at 0.0243\n",
      "Borrowed 87.9300 tokens at 0.0243\n",
      "Withdrew 88.2839 tokens at 0.0243\n",
      "Withdrew 0.8828 tokens at 0.0243\n",
      "Borrowed 46.7128 tokens at 0.0183\n",
      "Withdrew 46.2457 tokens at 0.0183\n",
      "Borrowed 32.9737 tokens at 0.0183\n",
      "Withdrew 33.1065 tokens at 0.0183\n",
      "Withdrew 0.3311 tokens at 0.0183\n",
      "Borrowed 179.0657 tokens at 0.0179\n",
      "Withdrew 177.2751 tokens at 0.0179\n",
      "Borrowed 126.3993 tokens at 0.0179\n",
      "Withdrew 126.9081 tokens at 0.0179\n",
      "Withdrew 1.2691 tokens at 0.0179\n",
      "Borrowed 132.3529 tokens at 0.0287\n",
      "Withdrew 131.0294 tokens at 0.0287\n",
      "Borrowed 93.4256 tokens at 0.0287\n",
      "Withdrew 93.8016 tokens at 0.0287\n",
      "Withdrew 0.9380 tokens at 0.0287\n",
      "Borrowed 264.7059 tokens at 0.0887\n",
      "Withdrew 262.0588 tokens at 0.0887\n",
      "Borrowed 186.8512 tokens at 0.0887\n",
      "Withdrew 187.6033 tokens at 0.0887\n",
      "Withdrew 1.8760 tokens at 0.0887\n",
      "unBorrowed 126.1707 tokens at 0.0356\n",
      "Withdrew 1.2546 tokens at 0.0255\n",
      "unBorrowed 6.3085 tokens at 0.0356\n",
      "unBorrowed 0.3154 tokens at 0.0356\n",
      "HODLing 396 tokens at rate 0.0365\n",
      "Borrowed 155.7093 tokens at 0.0960\n",
      "Withdrew 154.1522 tokens at 0.0960\n",
      "HODLing 376 tokens at rate 0.0372\n",
      "Borrowed 109.9125 tokens at 0.0960\n",
      "Withdrew 110.3549 tokens at 0.0960\n",
      "Withdrew 1.1035 tokens at 0.0960\n",
      "HODLing 467 tokens at rate 0.0126\n",
      "HODLing 149 tokens at rate 0.0156\n",
      "Borrowed 77.8547 tokens at 0.0406\n",
      "Withdrew 77.0761 tokens at 0.0406\n",
      "Borrowed 54.9562 tokens at 0.0406\n",
      "Withdrew 55.1774 tokens at 0.0406\n",
      "Withdrew 0.5518 tokens at 0.0406\n",
      "Borrowed 77.8498 tokens at 0.0255\n",
      "Withdrew 77.1494 tokens at 0.0255\n",
      "Borrowed 54.9528 tokens at 0.0255\n",
      "Withdrew 55.1748 tokens at 0.0255\n",
      "Withdrew 0.5517 tokens at 0.0255\n",
      "Borrowed 778.5467 tokens at 0.0156\n",
      "Withdrew 770.7612 tokens at 0.0156\n",
      "Withdrew 7.7076 tokens at 0.0156\n",
      "unBorrowed 739.6199 tokens at 0.0235\n",
      "Withdrew 7.3993 tokens at 0.0156\n",
      "Borrowed 778.5467 tokens at 0.0626\n",
      "Withdrew 770.7612 tokens at 0.0626\n",
      "Borrowed 549.5624 tokens at 0.0626\n",
      "Withdrew 354.9729 tokens at 0.0626\n",
      "Withdrew 3.5497 tokens at 0.0626\n",
      "Borrowed 233.5640 tokens at 0.0750\n",
      "Borrowed 164.8687 tokens at 0.0750\n",
      "Borrowed 529.4118 tokens at 0.0624\n",
      "Borrowed 373.7024 tokens at 0.0624\n",
      "unBorrowed 36.9810 tokens at 0.0235\n",
      "Withdrew 0.4401 tokens at 0.0156\n",
      "Withdrew 36.5771 tokens at 0.0626\n",
      "Withdrew 0.3658 tokens at 0.0750\n",
      "unBorrowed 1.8491 tokens at 0.0235\n",
      "Withdrew 1.8525 tokens at 0.0626\n",
      "HODLing 549 tokens at rate 0.0216\n",
      "Withdrew 158.7918 tokens at 0.0626\n",
      "Withdrew 386.3247 tokens at 0.0750\n",
      "Withdrew 3.8632 tokens at 0.0624\n",
      "HODLing 1000 tokens at rate 0.0258\n",
      "Withdrew 1.5879 tokens at 0.0626\n",
      "Withdrew 11.6251 tokens at 0.0750\n",
      "Withdrew 890.2595 tokens at 0.0624\n",
      "Withdrew 0.1163 tokens at 0.0750\n",
      "Withdrew 8.9026 tokens at 0.0624\n",
      "HODLing 542 tokens at rate 0.0316\n",
      "unBorrowed 126.1788 tokens at 0.0356\n",
      "Withdrew 1.2547 tokens at 0.0255\n",
      "unBorrowed 6.3089 tokens at 0.0356\n",
      "unBorrowed 0.3154 tokens at 0.0356\n",
      "HODLing 306 tokens at rate 0.0324\n",
      "Borrowed 62.2837 tokens at 0.0561\n",
      "Withdrew 61.6609 tokens at 0.0561\n",
      "Borrowed 43.9650 tokens at 0.0561\n",
      "Withdrew 44.1419 tokens at 0.0561\n",
      "Withdrew 0.4414 tokens at 0.0561\n",
      "HODLing 347 tokens at rate 0.0329\n",
      "HODLing 142 tokens at rate 0.0268\n",
      "Borrowed 762.9758 tokens at 0.0667\n",
      "Withdrew 755.3460 tokens at 0.0667\n",
      "Borrowed 538.5711 tokens at 0.0667\n",
      "Withdrew 540.7389 tokens at 0.0667\n",
      "Withdrew 5.4074 tokens at 0.0667\n",
      "Borrowed 31.1419 tokens at 0.0689\n",
      "Withdrew 30.8304 tokens at 0.0689\n",
      "Borrowed 21.9825 tokens at 0.0689\n",
      "Withdrew 22.0710 tokens at 0.0689\n",
      "Withdrew 0.2207 tokens at 0.0689\n",
      "HODLing 938 tokens at rate 0.0337\n",
      "HODLing 414 tokens at rate 0.0279\n",
      "Borrowed 116.7820 tokens at 0.0819\n",
      "Withdrew 115.6142 tokens at 0.0819\n",
      "Borrowed 82.4344 tokens at 0.0819\n",
      "Withdrew 82.7662 tokens at 0.0819\n",
      "Withdrew 0.8277 tokens at 0.0819\n",
      "HODLing 465 tokens at rate 0.0220\n",
      "Borrowed 77.8498 tokens at 0.0255\n",
      "Withdrew 77.1494 tokens at 0.0255\n",
      "Borrowed 54.9528 tokens at 0.0255\n",
      "Withdrew 55.1748 tokens at 0.0255\n",
      "Withdrew 0.5517 tokens at 0.0255\n",
      "Borrowed 513.8408 tokens at 0.0950\n",
      "Withdrew 508.7024 tokens at 0.0950\n",
      "Borrowed 362.7112 tokens at 0.0950\n",
      "Withdrew 364.1711 tokens at 0.0950\n",
      "Withdrew 3.6417 tokens at 0.0950\n",
      "unBorrowed 126.1785 tokens at 0.0356\n",
      "Withdrew 1.2547 tokens at 0.0255\n",
      "unBorrowed 6.3089 tokens at 0.0356\n",
      "unBorrowed 0.3154 tokens at 0.0356\n",
      "HODLing 329 tokens at rate 0.0352\n",
      "HODLing 435 tokens at rate 0.0286\n"
     ]
    }
   ],
   "source": [
    "import numpy\n",
    "\n",
    "chain = blockchain(1,1)\n",
    "oracle.setPrice(Decimal('2'))\n",
    "tokenA = Token(\"TokenA\")\n",
    "tokenB = Token(\"TokenB\")\n",
    "oracle = Oracle(tokenA, tokenB)\n",
    "oracle.setPrice(Decimal('1'))\n",
    "interface = Interface(chain, tokenA, tokenB, oracle)\n",
    "\n",
    "interface.pyA.interestUpdateAmount = Decimal('0.0001')\n",
    "interface.pyA.reservesTarget = Decimal('0.25')\n",
    "interface.pyA.minimumSpread = Decimal('0.0')\n",
    "interface.pyA.adjustmentFreeWindow = Decimal('0.01')\n",
    "\n",
    "# tracking variables\n",
    "pyAinterest = []\n",
    "pyBinterest = []\n",
    "qxA = []\n",
    "qxB = []\n",
    "pyratesA = []\n",
    "debtratesA = []\n",
    "tSupply = []\n",
    "ulying = []\n",
    "debtOutstanding = []\n",
    "debtSupplyRatio = []\n",
    "feeIncome = []\n",
    "rrMean = []\n",
    "rrvar  = []\n",
    "reservesTarget = []\n",
    "candidateReserves = []\n",
    "\n",
    "\n",
    "\n",
    "# Initial positions\n",
    "interface.A.mint(\"HODL\", Decimal(100000))\n",
    "interface.B.mint(\"HODL\", Decimal(100000))\n",
    "interface.eth.mint(\"BU\", Decimal(1000))\n",
    "interface.pyA.addCollateral(\"ETH\", \"BU\", Decimal(100) )\n",
    "interface.pyB.addCollateral(\"ETH\", \"BU\", Decimal(100) )\n",
    "\n",
    "price = interface.ethAOracle.getPriceInToken(interface.A)\n",
    "#borrow = (Decimal(25) * price) / (interface.pyA.collateralizationRatio )\n",
    "#borrow = Decimal(100)\n",
    "#interface.pyA.borrow(\"ETH\", \"BU\", borrow)\n",
    "#interface.qxA.mint(\"HODL\", Decimal(1500 * 0.15))\n",
    "\n",
    "#Create users\n",
    "\n",
    "#user1 = YieldAgent(interface)\n",
    "#user2 = BorrowAgent(interface)\n",
    "#users = [user1, user2]\n",
    "\n",
    "users = []\n",
    "for x in range(5):\n",
    "    users.append(YieldAgent(interface))\n",
    "    \n",
    "for y in range(2):\n",
    "    users.append(BorrowAgent(interface))\n",
    "\n",
    "# Setup\n",
    "for user in users:\n",
    "    user.setup()\n",
    "\n",
    "    \n",
    "mean_estimate = Decimal(0)  \n",
    "measurements = Decimal(0) \n",
    "var_estimate = Decimal(0)\n",
    "lastReserves = Decimal(0)\n",
    "measurementsLimit = Decimal(15)\n",
    "\n",
    "# Run\n",
    "for block in range(4000):\n",
    "    \n",
    "    # skip some blocks\n",
    "    #if numpy.random.uniform(0,100) < 80:\n",
    "    #    continue\n",
    "    \n",
    "    # perform interest rate update at beginning of block\n",
    "    interface.eachBlock()\n",
    "    \n",
    "    # add users\n",
    "    if numpy.random.uniform(0,100) < 4:\n",
    "        if numpy.random.uniform(0,100) < 33:\n",
    "            newAgent = BorrowAgent(interface)\n",
    "            newAgent.setup()\n",
    "            users.append(newAgent)\n",
    "        else:\n",
    "            newAgent = YieldAgent(interface)\n",
    "            newAgent.setup()\n",
    "            users.append(newAgent)\n",
    "    \n",
    "    for user in users:\n",
    "        user.performActions()\n",
    "    \n",
    "    #calculate an estimate of the reserves\n",
    "    reserves = interface.qxA.underlying\n",
    "    RTarget = (interface.pyA.totalDebt+interface.qxA.underlying)*interface.pyA.reservesTarget\n",
    "    \n",
    "    if reserves != lastReserves:\n",
    "        if measurements !=0:\n",
    "            mean_estimate = (1/measurements) * (reserves) + ((measurements - 1)/measurements) * mean_estimate\n",
    "            var_estimate = (1/measurements) * math.ln((RTarget - reserves)**2) + ((measurements - 1)/measurements) * mean_estimate\n",
    "        else:\n",
    "            mean_estimate = Decimal(reserves)\n",
    "            var_estimate = 0\n",
    "\n",
    "        measurements += 1\n",
    "        if measurements > measurementsLimit:\n",
    "            measurements = Decimal(measurementsLimit)\n",
    "        \n",
    "    lastReserves = reserves \n",
    "    #Candidate new reserves Target\n",
    "    candidateR = Decimal(10)*math.sqrt(interface.pyA.totalDebt)/(interface.pyA.totalDebt+interface.qxA.underlying)\n",
    "    interface.pyA.reservesTarget = candidateR\n",
    "    \n",
    "    candidateReserves.append(float(candidateR))\n",
    "    reservesTarget.append(float(RTarget))\n",
    "    rrMean.append(float(mean_estimate))\n",
    "    rrvar.append(float(var_estimate))\n",
    "    pyAinterest.append(float(interface.pyA.yearlyRate()))\n",
    "    debtSupplyRatio.append(float(interface.pyA.totalDebt/interface.pyA.totalSupply))\n",
    "    qxA.append(float(interface.qxA.measureRatio()))\n",
    "    debtOutstanding.append(float(interface.pyA.getTotalDebt()))\n",
    "    pyratesA.append(float(interface.pyA.yearlyRate()))\n",
    "    debtratesA.append(float(interface.pyA.yearlyDebtRate()))\n",
    "    tSupply.append(float(interface.pyA.totalSupply))\n",
    "    ulying.append(float(interface.qxA.underlying))\n",
    "    feeIncome.append(float(interface.pyA.totalFeeIncome))\n",
    "# Shutdown\n",
    "for user in users:\n",
    "    user.shutdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12c5fefd0>"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pandas.Series(debtOutstanding, name=\"debt\").plot(legend=True)\n",
    "pandas.Series(tSupply, name=\"pyTokens\").plot(legend=True)\n",
    "pandas.Series(ulying, name=\"underlying\").plot(legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12c705fd0>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pandas.Series(debtSupplyRatio, name=\"Debt/Supply Ratio\").plot(legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12c5e4550>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pandas.Series(qxA, name=\"Reserve Ratio\").plot(legend=True)\n",
    "pandas.Series(candidateReserves, name=\"Candidate Reserve Ratio Target\").plot(legend=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12c9ca0d0>"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pandas.Series(rrMean, name=\"Reserve Ratio Mean Estimate\").plot(legend=True)\n",
    "pandas.Series(reservesTarget, name=\"Reserves Target\").plot(legend=True)\n",
    "pandas.Series(ulying, name=\"underlying\").plot(legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12cb0f9d0>"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pandas.Series(rrvar, name=\"variance\").plot(legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12ccc6910>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pandas.Series(pyratesA, name=\"pyToken yield\").plot(legend=True)\n",
    "pandas.Series(debtratesA,name=\"borrow rate\").plot(legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2647326092547395e-05"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's look at variance\n",
    "pandas.Series(pyratesA[1000:], name=\"pyToken yield\").var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12c037dd0>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pandas.Series(feeIncome,name=\"fee income\").plot(legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scratchpad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0xReferenceExchange': Decimal('1000000000'),\n",
       " 'HODL': Decimal('100000'),\n",
       " '0xYA_HodlPY255': Decimal('239'),\n",
       " '0xYA_HodlPY312': Decimal('0'),\n",
       " '0xYA_HodlPY536': Decimal('530'),\n",
       " '0xYA_HodlPY753': Decimal('1000'),\n",
       " '0xYA_HodlPY539': Decimal('1000'),\n",
       " '0xBA_longETH202': Decimal('145588.1662439447303715612890'),\n",
       " '0xBA_longETH95': Decimal('1324857.494250996192092001421'),\n",
       " '0xYA_HodlPY342': Decimal('239'),\n",
       " '0xYA_HodlPY692': Decimal('0'),\n",
       " '0xYA_HodlPY187': Decimal('0'),\n",
       " '0xYA_HodlPY495': Decimal('0'),\n",
       " '0xQX_TokenA': Decimal('2180.551225007945549087069973'),\n",
       " '0xBA_longETH591': Decimal('145588.1628584547339975374692'),\n",
       " '0xYA_HodlPY403': Decimal('559'),\n",
       " '0xYA_HodlPY994': Decimal('572'),\n",
       " '0xYA_HodlPY506': Decimal('797'),\n",
       " '0xBA_longETH5': Decimal('39705.88235294117647058823530'),\n",
       " '0xBA_longETH215': Decimal('304717.2033041883467945107796'),\n",
       " '0xBA_longETH408': Decimal('119237.1649376913980946244718'),\n",
       " '0xBA_longETH633': Decimal('304411.7647058823529411764706'),\n",
       " '0xYA_HodlPY488': Decimal('322'),\n",
       " '0xYA_HodlPY636': Decimal('972'),\n",
       " '0xBA_longETH678': Decimal('516176.4705882352941176470590'),\n",
       " '0xBA_longETH192': Decimal('502941.1764705882352941176470'),\n",
       " '0xBA_longETH363': Decimal('211764.7058823529411764705882'),\n",
       " '0xBA_longETH820': Decimal('105988.5972098196846836827671'),\n",
       " '0xYA_HodlPY760': Decimal('347'),\n",
       " '0xYA_HodlPY251': Decimal('0'),\n",
       " '0xBA_longETH566': Decimal('185294.1176470588235294117647'),\n",
       " '0xYA_HodlPY617': Decimal('240'),\n",
       " '0xYA_HodlPY796': Decimal('1000'),\n",
       " '0xYA_HodlPY92': Decimal('0'),\n",
       " '0xYA_HodlPY35': Decimal('0'),\n",
       " '0xBA_longETH637': Decimal('225000.0202825860814662472016'),\n",
       " '0xBA_longETH751': Decimal('741920.1804688873380691923825'),\n",
       " '0xBA_longETH636': Decimal('225225.7690708625912835440920'),\n",
       " '0xYA_HodlPY512': Decimal('0'),\n",
       " '0xYA_HodlPY347': Decimal('0'),\n",
       " '0xYA_HodlPY426': Decimal('324'),\n",
       " '0xYA_HodlPY178': Decimal('765'),\n",
       " '0xYA_HodlPY61': Decimal('0'),\n",
       " '0xBA_longETH162': Decimal('502941.1764705882352941176470'),\n",
       " '0xYA_HodlPY394': Decimal('676'),\n",
       " '0xYA_HodlPY546': Decimal('247'),\n",
       " '0xYA_HodlPY888': Decimal('87'),\n",
       " '0xBA_longETH561': Decimal('238235.2941176470588235294118'),\n",
       " '0xYA_HodlPY293': Decimal('0'),\n",
       " '0xBA_longETH364': Decimal('158982.8958147458761622084469'),\n",
       " '0xYA_HodlPY304': Decimal('828'),\n",
       " '0xYA_HodlPY773': Decimal('0'),\n",
       " '0xBA_longETH31': Decimal('821411.6283755110173061508587'),\n",
       " '0xYA_HodlPY644': Decimal('369'),\n",
       " '0xBA_longETH672': Decimal('39745.72395365298377968497692'),\n",
       " '0xYA_HodlPY565': Decimal('239'),\n",
       " '0xYA_HodlPY102': Decimal('662'),\n",
       " '0xYA_HodlPY415': Decimal('0'),\n",
       " '0xYA_HodlPY714': Decimal('134'),\n",
       " '0xBA_longETH39': Decimal('278220.0676756624173246007861'),\n",
       " '0xBA_longETH748': Decimal('238474.3437220271774604761189'),\n",
       " '0xYA_HodlPY282': Decimal('0'),\n",
       " '0xYA_HodlPY931': Decimal('0'),\n",
       " '0xYA_HodlPY649': Decimal('418'),\n",
       " '0xBA_longETH864': Decimal('251722.9183732718393935163562'),\n",
       " '0xYA_HodlPY513': Decimal('779'),\n",
       " '0xBA_longETH297': Decimal('185480.0451171855861355730917'),\n",
       " '0xBA_longETH45': Decimal('66176.47058823529411764705885'),\n",
       " '0xYA_HodlPY824': Decimal('409'),\n",
       " '0xYA_HodlPY390': Decimal('0'),\n",
       " '0xBA_longETH348': Decimal('158982.8958147514046302058296'),\n",
       " '0xYA_HodlPY248': Decimal('0'),\n",
       " '0xBA_longETH492': Decimal('317965.7916294477460017367270'),\n",
       " '0xYA_HodlPY554': Decimal('380'),\n",
       " '0xYA_HodlPY516': Decimal('0'),\n",
       " '0xYA_HodlPY319': Decimal('227'),\n",
       " '0xBA_longETH756': Decimal('291176.4705882352941176470588'),\n",
       " '0xYA_HodlPY942': Decimal('356'),\n",
       " '0xBA_longETH185': Decimal('251618.4973183637329332696286'),\n",
       " '0xYA_HodlPY810': Decimal('1000'),\n",
       " '0xYA_HodlPY253': Decimal('320'),\n",
       " '0xYA_HodlPY112': Decimal('624'),\n",
       " '0xYA_HodlPY142': Decimal('0')}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interface.A.balances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0xBA_longETH202': Decimal('0.0868882457381374672547674163'),\n",
       " '0xBA_longETH95': Decimal('0.028326767708364589296749430'),\n",
       " '0xQX_TokenA': Decimal('0E-25'),\n",
       " '0xYA_HodlPY312': Decimal('999.9996584443820805767014771'),\n",
       " '0xBA_longETH591': Decimal('0.0868430408615332689461752574'),\n",
       " '0xBA_longETH215': Decimal('0.026593795272738221944147451'),\n",
       " '0xBA_longETH408': Decimal('0.011952957666731207748354779'),\n",
       " '0xYA_HodlPY495': Decimal('611.9986997883756845181540008'),\n",
       " '0xYA_HodlPY692': Decimal('327.9989597152214036079218273'),\n",
       " '0xYA_HodlPY187': Decimal('1095.987359143417690977534448'),\n",
       " '0xBA_longETH820': Decimal('0.0044587566004282398784045651'),\n",
       " '0xYA_HodlPY251': Decimal('455.9972719665310129213045212'),\n",
       " '0xYA_HodlPY92': Decimal('153.9987284446132739195070753'),\n",
       " '0xYA_HodlPY35': Decimal('200.9982688959080735510379511'),\n",
       " '0xBA_longETH636': Decimal('0.0094748115885531628893651249'),\n",
       " '0xYA_HodlPY512': Decimal('128.9986138534116180282285082'),\n",
       " '0xYA_HodlPY347': Decimal('77.99914501485735235933130797'),\n",
       " '0xBA_longETH637': Decimal('0.0066502214998684390838411140'),\n",
       " '0xBA_longETH751': Decimal('0.031211086323511456458240464'),\n",
       " '0xBA_longETH364': Decimal('0.0066880644809495045870540993'),\n",
       " '0xBA_longETH31': Decimal('0.034554930249076754510082170'),\n",
       " '0xYA_HodlPY61': Decimal('163.9969805689022198882537977'),\n",
       " '0xYA_HodlPY293': Decimal('754.9860995702510732660464469'),\n",
       " '0xYA_HodlPY773': Decimal('270.9950105742225706690047511'),\n",
       " '0xBA_longETH672': Decimal('0.0016720117518543589028213792'),\n",
       " '0xYA_HodlPY415': Decimal('384.9916776156267871445744626'),\n",
       " '0xBA_longETH39': Decimal('0.011704047948126206946575402'),\n",
       " '0xBA_longETH748': Decimal('0.0100320382320963453161508665'),\n",
       " '0xYA_HodlPY282': Decimal('652.9850832208619964450261496'),\n",
       " '0xYA_HodlPY931': Decimal('368.9914490025312736948948615'),\n",
       " '0xBA_longETH864': Decimal('0.010589359343679289356573436'),\n",
       " '0xBA_longETH297': Decimal('0.0078026827270165653876324211'),\n",
       " '0xBA_longETH348': Decimal('0.0066880075520860660087387525'),\n",
       " '0xYA_HodlPY248': Decimal('89.99771067394264275367636826'),\n",
       " '0xBA_longETH492': Decimal('0.013376003476697150353381129'),\n",
       " '0xYA_HodlPY516': Decimal('93.99752844923343721354860691'),\n",
       " '0xYA_HodlPY390': Decimal('287.9921026522529373762876952'),\n",
       " '0xBA_longETH185': Decimal('0.014791954853192329270288413'),\n",
       " '0xYA_HodlPY142': Decimal('490.9840909492395091068744909')}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interface.pyA.balances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "192.8080100478424"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.random.lognormal(5, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lnmean(mean, sigma):\n",
    "    sum = 0\n",
    "    for x in range(1000):\n",
    "        sum += numpy.random.lognormal(mean, sigma)\n",
    "    return sum/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.910013808703923"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lnmean(.1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "650.4594621904588"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lnmean(6,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def threshold(mean, sigma, limit ):\n",
    "    result = int(numpy.random.exponential(mean))\n",
    "    print(result)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tfreq(mean, sigma, limit):\n",
    "    sum = 0\n",
    "    for x in range(100):\n",
    "        if threshold(mean, sigma, limit ):\n",
    "            sum += 1\n",
    "    return sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3624386683879848"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.random.exponential(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 1, 1, 1]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[1]*5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interest Tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# self.rate = Decimal(1) + ((self.debtRate-1) * self.totalDebt * (1 - self.minimumSpread))/(1 + self.totalSupply)\n",
    "# self.rate = math.power(1 + Decimal(rate), 1/Decimal(31622400)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from decimal import Decimal\n",
    "import decimal\n",
    "debtRate = Decimal('1.04')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Decimal('1.0816')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math = decimal.Context()\n",
    "accumulate = math.power(debtRate, 2)\n",
    "accumulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "minSpread = Decimal('0.00')\n",
    "totalSupply = 1000\n",
    "totalDebt = totalSupply * Decimal('0.7')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "rate = Decimal(1) + ((debtRate-1) * totalDebt * (1 - minSpread))/(totalSupply)\n",
    "#rate = ((debtRate) * totalDebt * (1 - minSpread))/(1 + totalSupply)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Decimal('1.02800')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Decimal('1.0567840000')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.power(rate, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Decimal('57.12000')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accumulate*totalDebt - totalDebt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Decimal('56.7840000000')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totalSupply*math.power(rate, 2) - totalSupply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1036.170999442840748032000\n",
      "1218.99441999475713024000\n"
     ]
    }
   ],
   "source": [
    "print(totalDebt*math.power(debtRate, 10))\n",
    "print(totalSupply*math.power(Decimal('1.02'), 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Decimal('1.000000000003162157215564487')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interestUpdateAmount = Decimal('0.0001')\n",
    "newRate = math.power(1 + Decimal(interestUpdateAmount), 1/Decimal(31622400))\n",
    "newRate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0000000000031621"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1000000000003162157215564487 / 10**27"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000000284625770972959474870"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(3162157215564487 * 90010) + 10**27\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000000000000000000000000000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2*10**27"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.999999999999999"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4.999999999999998706\n",
    "4.999999999999999000"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}