defi/Uniswap-V3/whitepaperGuide/oracleCompare.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "source": [
    "'''\r\n",
    "compare two implementations of price oracle:\r\n",
    "    arithmetic mean used by uniswap v2\r\n",
    "    geometric mean used by uniswap v3\r\n",
    "'''\r\n",
    "\r\n",
    "from functools import reduce\r\n",
    "import math\r\n",
    "import pandas as pd\r\n",
    "import numpy as np\r\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "source": [
    "# tick index is log(price, 1.0001)\r\n",
    "LOG_BASE = 1.0001\r\n",
    "time_index = [0,1,3,6,9,11,12,13,16,20,22,24,26,28,32,36]\r\n",
    "# 5th and 11th prices are abnormal\r\n",
    "origin_prices = [100,130,110,120,30,110,100,120,110,120,200,90,80,100,120,150]\r\n",
    "\r\n",
    "# delta time of every besides price\r\n",
    "# first element is 0s\r\n",
    "delta_times = [0]\r\n",
    "for i in range(1, len(time_index)):\r\n",
    "    delta_times.append(time_index[i] - time_index[i-1])\r\n",
    "    "
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "source": [
    "# uniswap V2 use arithmetic sum\r\n",
    "def arithmetic_sums_calc(begin, end):\r\n",
    "    sums = []\r\n",
    "    for i in range(begin, end):\r\n",
    "        if i == 0:\r\n",
    "            sums.append(origin_prices[0])\r\n",
    "        else:\r\n",
    "            # sum = current_price * delta time\r\n",
    "            sums.append(origin_prices[begin+i] * delta_times[begin+i])\r\n",
    "    return sums\r\n",
    "\r\n",
    "# uniswap V3 use geometric sum\r\n",
    "def geometric_sums_calc(begin, end):\r\n",
    "    sums = []\r\n",
    "    for i in range(begin, end):\r\n",
    "        if i == 0:\r\n",
    "            sums.append(math.log(origin_prices[0], LOG_BASE))\r\n",
    "        else:\r\n",
    "            # sum = current_price * delta time\r\n",
    "            sums.append(math.log(origin_prices[begin+i], LOG_BASE) * delta_times[begin+i])\r\n",
    "    return sums\r\n",
    "\r\n",
    "arithmetic_sums = arithmetic_sums_calc(0, len(origin_prices))\r\n",
    "geometric_sums = geometric_sums_calc(0, len(origin_prices))\r\n",
    "\r\n",
    "print(arithmetic_sums)\r\n",
    "print(geometric_sums)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[100, 130, 220, 360, 90, 220, 100, 120, 330, 480, 400, 180, 160, 200, 480, 600]\n",
      "[46054.00440660449, 48677.77823122565, 94014.30771788706, 143631.93340141006, 102041.0231609227, 94014.30771788706, 46054.00440660449, 47877.311133803356, 141021.4615768306, 191509.24453521342, 105971.64556003807, 90000.69314129246, 87644.91464709182, 92108.00881320897, 191509.24453521342, 200435.43286744767]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "source": [
    "# the cumulative price store in oracle\r\n",
    "cumulative_arithmetic = []\r\n",
    "cumulative_geometric = []\r\n",
    "\r\n",
    "for i in range(1,len(origin_prices)+1):\r\n",
    "    cumulative_arithmetic.append(reduce(lambda x,y: x+y, arithmetic_sums[0:i]))\r\n",
    "    cumulative_geometric.append(reduce(lambda x,y: x+y, geometric_sums[0:i]))\r\n",
    "\r\n",
    "print(cumulative_arithmetic)\r\n",
    "print(cumulative_geometric)\r\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[100, 230, 450, 810, 900, 1120, 1220, 1340, 1670, 2150, 2550, 2730, 2890, 3090, 3570, 4170]\n",
      "[46054.00440660449, 94731.78263783014, 188746.0903557172, 332378.02375712723, 434419.0469180499, 528433.354635937, 574487.3590425415, 622364.6701763448, 763386.1317531754, 954895.3762883889, 1060867.021848427, 1150867.7149897194, 1238512.6296368113, 1330620.6384500202, 1522129.8829852336, 1722565.3158526812]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "source": [
    "# calc price with oracle's cumulative price\r\n",
    "def calc_arithmetic_oracleprice(begin,end):\r\n",
    "    return (cumulative_arithmetic[end] - cumulative_arithmetic[begin]) / (time_index[end] - time_index[begin])\r\n",
    "\r\n",
    "def calc_geometric_oracleprice(begin,end):\r\n",
    "    return math.pow(LOG_BASE, (cumulative_geometric[end] - cumulative_geometric[begin]) / (time_index[end] - time_index[begin]))\r\n",
    "\r\n",
    "WINDOW_SIZE = 4\r\n",
    "\r\n",
    "np_mean_prices = []\r\n",
    "oracle_arithmetic_prices = []\r\n",
    "oracle_geometric_prices = []\r\n",
    "\r\n",
    "for i in range(0,len(origin_prices)):\r\n",
    "    if i < WINDOW_SIZE:\r\n",
    "        np_mean_prices.append(None)\r\n",
    "        oracle_arithmetic_prices.append(None)\r\n",
    "        oracle_geometric_prices.append(None)\r\n",
    "    else:\r\n",
    "        np_mean_prices.append(np.mean(origin_prices[i-WINDOW_SIZE:i]))\r\n",
    "        oracle_arithmetic_prices.append(calc_arithmetic_oracleprice(i-WINDOW_SIZE,i))\r\n",
    "        oracle_geometric_prices.append(calc_geometric_oracleprice(i-WINDOW_SIZE,i))\r\n",
    "\r\n",
    "df = pd.DataFrame({\r\n",
    "  'time_index': time_index,\r\n",
    "  'price': origin_prices,\r\n",
    "  'delta_times': delta_times,\r\n",
    "  'cumulative_arithmetic': cumulative_arithmetic,\r\n",
    "  'cumulative_geometric': cumulative_geometric,\r\n",
    "  'oracle_arithmetic_prices': oracle_arithmetic_prices,\r\n",
    "  'oracle_geometric_prices': oracle_geometric_prices,\r\n",
    "})\r\n",
    "\r\n",
    "# the result showing arithmetic mean giving higher price more weight\r\n",
    "# and geometric mean giving lower price more weight\r\n",
    "print(df)\r\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "    time_index  price  delta_times  cumulative_arithmetic  \\\n",
      "0            0    100            0                    100   \n",
      "1            1    130            1                    230   \n",
      "2            3    110            2                    450   \n",
      "3            6    120            3                    810   \n",
      "4            9     30            3                    900   \n",
      "5           11    110            2                   1120   \n",
      "6           12    100            1                   1220   \n",
      "7           13    120            1                   1340   \n",
      "8           16    110            3                   1670   \n",
      "9           20    120            4                   2150   \n",
      "10          22    200            2                   2550   \n",
      "11          24     90            2                   2730   \n",
      "12          26     80            2                   2890   \n",
      "13          28    100            2                   3090   \n",
      "14          32    120            4                   3570   \n",
      "15          36    150            4                   4170   \n",
      "\n",
      "    cumulative_geometric  oracle_arithmetic_prices  oracle_geometric_prices  \n",
      "0           4.605400e+04                       NaN                      NaN  \n",
      "1           9.473178e+04                       NaN                      NaN  \n",
      "2           1.887461e+05                       NaN                      NaN  \n",
      "3           3.323780e+05                       NaN                      NaN  \n",
      "4           4.344190e+05                 88.888889                74.809987  \n",
      "5           5.284334e+05                 89.000000                76.462382  \n",
      "6           5.744874e+05                 85.555556                72.660638  \n",
      "7           6.223647e+05                 75.714286                62.957460  \n",
      "8           7.633861e+05                110.000000               109.869668  \n",
      "9           9.548954e+05                114.444444               114.231832  \n",
      "10          1.060867e+06                133.000000               129.483499  \n",
      "11          1.150868e+06                126.363636               122.038055  \n",
      "12          1.238513e+06                122.000000               115.703100  \n",
      "13          1.330621e+06                117.500000               109.544512  \n",
      "14          1.522130e+06                102.000000               100.725399  \n",
      "15          1.722565e+06                120.000000               117.203118  \n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "source": [
    "'''\r\n",
    "arithmetic mean is more stable than geometric mean.\r\n",
    "time 12s \r\n",
    "'''\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "\r\n",
    "plt.figure(figsize=(16,8))\r\n",
    "plt.plot(time_index, origin_prices, label=\"origin price\")\r\n",
    "plt.plot(time_index, oracle_arithmetic_prices, label=\"arithmetic price\")\r\n",
    "plt.plot(time_index, oracle_geometric_prices, label=\"geometric price\")\r\n",
    "plt.legend()\r\n",
    "plt.xlabel('time/s')\r\n",
    "plt.ylabel('price/$')\r\n",
    "plt.title('compare oracle price')\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  }
 ],
 "metadata": {
  "orig_nbformat": 4,
  "language_info": {
   "name": "python",
   "version": "3.7.4",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.7.4 64-bit"
  },
  "interpreter": {
   "hash": "4b0c0561b02f92c4f016080a613bf5b1e5d4d0bd86ff103aff55fd51031da5da"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}