CarbonCurves.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ed525c67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Carbon v2.1-BETA1 (14/Dec/2022)\n",
      "CarbonOrderUI v1.4 (15/Dec/2022)\n"
     ]
    }
   ],
   "source": [
    "from carbon import CarbonSimulatorUI, CarbonOrderUI, P, __version__, __date__\n",
    "from math import sqrt\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "print(f\"Carbon v{__version__} ({__date__})\")\n",
    "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(CarbonOrderUI))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "795b0a19",
   "metadata": {},
   "source": [
    "# Carbon Curves"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2938262",
   "metadata": {},
   "source": [
    "## The CarbonOrderUI object"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "605fecbc",
   "metadata": {},
   "source": [
    "This curve `order1` is selling ETH for USDC, starting at 2000 USDC per ETH and ending at 3000. The curve has a capacity of 10 ETH (5 ETH for `order1b`), and it also is currently loaded with 10 ETH. The curve `order2` is selling USDC, starting at 1000 USDC per ETH and ending at 750. Capacity and loading is 20,000 USDC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "12e631b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "order1 = CarbonOrderUI.from_prices(\"ETH/USDC\", \"ETH\", 2000, 3000, 10, 10)\n",
    "order1b = CarbonOrderUI.from_prices(\"ETH/USDC\", \"ETH\", 2000, 3000, 10, 5)\n",
    "order2 = CarbonOrderUI.from_prices(\"ETH/USDC\", \"USDC\", 1000, 750, 20000, 20000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18bdca85",
   "metadata": {},
   "source": [
    "## Simple numeric properties"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "257299ec",
   "metadata": {},
   "source": [
    "The prices are retrievable under a number of names: `p_start` and `p_end` to indicate the beginning and the end of the range, `px` and `py` to indicate the axis intersect to which they corresponds, and `pa` and `pb` simply in alphabetical order. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d50945b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000.0, 3000.0, 2000.0, 3000.0, 1000.0, 750.0000000000001)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order1.p_start, order1.p_end, order1b.p_start, order1b.p_end, order2.p_start, order2.p_end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8566f648",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000.0, 3000.0, 2000.0, 3000.0, 1000.0, 750.0000000000001)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order1.py, order1.px, order1b.py, order1b.px, order2.py, order2.px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "29227434",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000.0, 3000.0, 2000.0, 3000.0, 1000.0, 750.0000000000001)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order1.pa, order1.pb, order1b.pa, order1b.pb, order2.pa, order2.pb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dbc1968",
   "metadata": {},
   "source": [
    "The y-intercept `yint` correspond to the capacity of the curves, the y-value `y` to the current loading of the curve; those are expressed in `tkn`, also known as `tkny`. The x-intercept is the corresponding capacity in the other token `tknx`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "04dc81d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 10, 20000)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order1.yint, order1b.yint, order2.yint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "68d34ebb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 5, 20000)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order1.y, order1b.y, order2.y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "39e0b029",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('ETH', 'ETH', 'USDC')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order1.tkny, order1b.tkny, order2.tkny"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "093da1e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24494.897427831784, 24494.897427831784, 23.094010767585033)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order1.xint, order1b.xint, order2.xint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ba78ae66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('USDC', 'USDC', 'ETH')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order1.tknx, order1b.tknx, order2.tknx"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44794f55",
   "metadata": {},
   "source": [
    "The ratio `yint/xint` or `yint/xint` equals `p0` which is also the geometric average of the prices of the range; `p0` is always extressed in the quuote conventions of the pair."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8861b712",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2449.489742783178, 2449.489742783178, 866.0254037844387)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order1.p0, order1b.p0, order2.p0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3c0758aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2449.489742783178, 2449.489742783178, 866.0254037844387)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sqrt(order1.px*order1.py), sqrt(order1b.px*order1b.py), sqrt(order2.px*order2.py)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "61853e55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2449.4897427831784, 2449.4897427831784, 866.0254037844386)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order1.xint/order1.yint, order1b.xint/order1b.yint, order2.yint/order2.xint"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20355667",
   "metadata": {},
   "source": [
    "The current marginal price of the order is indicated by `p_marg`. If `y==yint` then `p_marg==p_start`, if `y==0` then `p_marg==p_end` and for `y` in between (as in `order1b`) it is in between."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "02c3065c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000.0, 2424.4923464074514, 1000.0)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order1.p_marg, order1b.p_marg, order2.p_marg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9965b6ac",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6b888339",
   "metadata": {},
   "outputs": [],
   "source": [
    "ETHr = np.linspace(0,order1.yint)\n",
    "ETHr2 = np.linspace(0,order1b.y)\n",
    "USDCr = np.linspace(0,order2.yint)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f88f8d3",
   "metadata": {},
   "source": [
    "We first plot the invariant curves. We see that `order1` and `order1b` have the same curve, but `order2` is different, and the axis are reversed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c5243db2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f91aafd94f0>"
      ]
     },
     "execution_count": 16,
     "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": [
    "plt.plot([order1.xfromy_f(y) for y in ETHr], ETHr, label=\"order1 (y=10)\", color=\"red\")\n",
    "plt.plot([order1b.xfromy_f(y) for y in ETHr], ETHr, label=\"order1b (y=5)\", color=\"blue\", linestyle=\"dotted\")\n",
    "plt.title(\"Full invariant curve\")\n",
    "plt.ylabel(\"y [ETH]\")\n",
    "plt.xlabel(\"x [USDC]\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "276ad9f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9198133a00>"
      ]
     },
     "execution_count": 17,
     "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": [
    "plt.plot([order2.xfromy_f(y) for y in USDCr], USDCr, label=\"order2 (y=20k)\", color=\"lawngreen\")\n",
    "plt.title(\"Full invariant curve\")\n",
    "plt.ylabel(\"y [USDC]\")\n",
    "plt.xlabel(\"x [ETH]\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d35d3c8b",
   "metadata": {},
   "source": [
    "The _active_ curve is the part of the curve that is reachable, ie below the current value of `y`. Initially we have `y==yint` so the full curve is active. But as we see in `order1b`, if `y<yint` the active part of the curve is smaller than the full curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dbcaa9a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9188507d60>"
      ]
     },
     "execution_count": 18,
     "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": [
    "plt.plot([order1b.xfromy_f(y) for y in ETHr], ETHr, label=\"full curve\", color=\"blue\", linestyle=\"dotted\")\n",
    "plt.plot([order1b.xfromy_f(y) for y in ETHr2], ETHr2, label=\"active curve\", color=\"blue\")\n",
    "plt.title(\"Active vs full invariant curve, order 1b\")\n",
    "plt.ylabel(\"y [ETH]\")\n",
    "plt.xlabel(\"x [USDC]\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f829a018",
   "metadata": {},
   "source": [
    "Here we see the swap curves, ie the change in `x`, `dx` for a given change in `y`, `dy` (the chart should be read therefore in reverse: the dependent variable is on the x-axis). The first chart is for the orders selling `ETH` and we recall that the curve in `order1b` is not entirely filled, which is why it stops in the middle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "036d1aff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f91ab12e700>"
      ]
     },
     "execution_count": 19,
     "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": [
    "plt.plot([order1.dxfromdy_f(y, raiseonerror=False) for y in ETHr], -ETHr, label=\"order1 (y=10)\", color=\"red\")\n",
    "plt.plot([order1b.dxfromdy_f(y, raiseonerror=False) for y in ETHr], -ETHr, label=\"order1b (y=5)\", color=\"blue\")\n",
    "plt.title(\"Swap curve\")\n",
    "plt.ylabel(\"dy [ETH]\")\n",
    "plt.xlabel(\"dx [USDC]\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e4697712",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f91ab09a880>"
      ]
     },
     "execution_count": 20,
     "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": [
    "plt.plot([order2.dxfromdy_f(y, raiseonerror=False) for y in USDCr], -USDCr, label=\"order2 (y=20k)\", color=\"lawngreen\")\n",
    "plt.title(\"Swap curve\")\n",
    "plt.xlabel(\"dx [ETH]\")\n",
    "plt.ylabel(\"dy [USDC]\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "293f2520",
   "metadata": {},
   "source": [
    "In the below chart we show the marginal and effective prices for a given trade size. We have chosen `dx` as the independent variable here to keep the chart visually in line with the ones previously presented. The **marginal price** is the price (in USDC per ETH) at which the marginal unit of USDC is traded, ie _one dollar more_. The **effective price** is the average price of the entire trade, which corresponds to the average marginal price up to this point. Therefore, the effective prices curves are always flatter than the marginal price curves.\n",
    "\n",
    "The curves for selling ETH are upwards sloping, corresponding to the fact that the first units are sold for fewer dollars than the later ones. The ones for buying ETH are downwards sloping, because the first units are bought more expensively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f72d65ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f91ab222d30>"
      ]
     },
     "execution_count": 21,
     "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": [
    "plt.plot(\n",
    "    [order1.dxfromdy_f(dy, raiseonerror=False) for dy in ETHr], \n",
    "    [order1.p_marg_f(dy, raiseonerror=False) for dy in ETHr], \n",
    "    label=\"marg (1; y=10 ETH)\", color=\"red\", linestyle=\"solid\")\n",
    "plt.plot(\n",
    "    [order1b.dxfromdy_f(dy, raiseonerror=False) for dy in ETHr], \n",
    "    [order1b.p_marg_f(dy, raiseonerror=False) for dy in ETHr], \n",
    "    label=\"marg (1b; y=5 ETH)\", color=\"blue\", linestyle=\"solid\")\n",
    "plt.plot(\n",
    "    USDCr, \n",
    "    [order2.p_marg_f(dy, raiseonerror=False) for dy in USDCr], \n",
    "    label=\"marg(2; y=20k USDC)\", color=\"lawngreen\", linestyle=\"solid\")\n",
    "plt.plot(\n",
    "    [order1.dxfromdy_f(dy, raiseonerror=False) for dy in ETHr], \n",
    "    [order1.p_eff_f(dy, raiseonerror=False) for dy in ETHr], \n",
    "    label=\"eff (1; y=10 ETH)\", color=\"red\", linestyle=\"dotted\")\n",
    "plt.plot(\n",
    "    [order1b.dxfromdy_f(dy, raiseonerror=False) for dy in ETHr], \n",
    "    [order1b.p_eff_f(dy, raiseonerror=False) for dy in ETHr], \n",
    "    label=\"eff (1b; y=5 ETH)\", color=\"blue\", linestyle=\"dotted\")\n",
    "plt.plot(\n",
    "    USDCr, \n",
    "    [order2.p_eff_f(dy, raiseonerror=False) for dy in USDCr], \n",
    "    label=\"eff (2; y=20k USDC)\", color=\"lawngreen\", linestyle=\"dotted\")\n",
    "plt.ylabel(\"Price [USDC per ETH]\")\n",
    "plt.xlabel(\"dx [USDC]\")\n",
    "plt.legend(loc=\"center right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "697d78cc",
   "metadata": {},
   "source": [
    "## Trading\n",
    "\n",
    "You can simulate trading (against a single curve) directly here. With `execute = False` the curve object itself is not changed. The command `selly` (when called with a positive `dy`) sells the y token of the curve. When called with a negative number, it buys this token. The outputs of this command are\n",
    "\n",
    "      'y_old': 10                             # the previous amount of liquidity on curve         \n",
    "      'y': 9                                  # ditto current\n",
    "      'dy': 1                                 # the liquidity change in token y (positive=SELL)\n",
    "      'yint_old': None                        # the old curve capacity\n",
    "      'y_int': 10                             # the current curve capacity\n",
    "      'expanded': False                       # whether the curve capacity has been expanded\n",
    "      'x': 2037.3867433374953                 # the implied virtual x liquidity on curve\n",
    "      'dx': 2037.3867433374958                # the liquidity change in token x (positive=BUY)\n",
    "      'tkny': 'ETH'                           # name of token y\n",
    "      'tknx': 'USDC'                          # name of token x\n",
    "      'tx': 'Sell 1 ETH buy USDC'             # the transaction as text\n",
    "      'dx/dy': 2037.3867433374958             # the ratio dx/dy (effective price)\n",
    "      'dy/dx': 0.0004908248290463863          # the ratio dy/dx (effective price; inverse quotattion)\n",
    "      'pmarg_old': 2000.0                     # the marginal price before the trade, in canonic quote direction\n",
    "      'pmarg': 2075.472370963683              # ditto after the trade\n",
    "      'p': 2037.3867433374958                 # the effective price of the trade, in canonic quote direction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "780692a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'y_old': 10,\n",
       " 'y': 9,\n",
       " 'dy': 1,\n",
       " 'yint_old': None,\n",
       " 'y_int': 10,\n",
       " 'expanded': False,\n",
       " 'x': 2037.3867433374953,\n",
       " 'dx': 2037.3867433374958,\n",
       " 'tkny': 'ETH',\n",
       " 'tknx': 'USDC',\n",
       " 'tx': 'Sell 1 ETH buy USDC',\n",
       " 'dx/dy': 2037.3867433374958,\n",
       " 'dy/dx': 0.0004908248290463863,\n",
       " 'pmarg_old': 2000.0,\n",
       " 'pmarg': 2075.472370963683,\n",
       " 'p': 2037.3867433374958}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order1.selly(1, execute = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1fe4a724",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'y_old': 10,\n",
       " 'y': 8,\n",
       " 'dy': 2,\n",
       " 'yint_old': None,\n",
       " 'y_int': 10,\n",
       " 'expanded': False,\n",
       " 'x': 4152.395764007314,\n",
       " 'dx': 4152.395764007314,\n",
       " 'tkny': 'ETH',\n",
       " 'tknx': 'USDC',\n",
       " 'tx': 'Sell 2 ETH buy USDC',\n",
       " 'dx/dy': 2076.197882003657,\n",
       " 'dy/dx': 0.00048164965809277256,\n",
       " 'pmarg_old': 2000.0,\n",
       " 'pmarg': 2155.2988226182356,\n",
       " 'p': 2076.197882003657}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order1.selly(2, execute = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aed980a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'y_old': 10,\n",
       " 'y': 9.50454587628476,\n",
       " 'dy': 0.4954541237152398,\n",
       " 'yint_old': None,\n",
       " 'y_int': 10,\n",
       " 'expanded': False,\n",
       " 'x': 999.9999999999992,\n",
       " 'dx': 1000.0,\n",
       " 'tkny': 'ETH',\n",
       " 'tknx': 'USDC',\n",
       " 'tx': 'Sell 0.4954541237152398 ETH buy USDC',\n",
       " 'dx/dy': 2018.3503419072274,\n",
       " 'dy/dx': 0.0004954541237152398,\n",
       " 'pmarg_old': 2000.0,\n",
       " 'pmarg': 2036.869051338511,\n",
       " 'p': 2018.3503419072274}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order1.buyx(1000, execute = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "beceb9a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'y_old': 10,\n",
       " 'y': 9.018019674715147,\n",
       " 'dy': 0.9819803252848526,\n",
       " 'yint_old': None,\n",
       " 'y_int': 10,\n",
       " 'expanded': False,\n",
       " 'x': 2000.0000000000016,\n",
       " 'dx': 2000.0,\n",
       " 'tkny': 'ETH',\n",
       " 'tknx': 'USDC',\n",
       " 'tx': 'Sell 0.9819803252848526 ETH buy USDC',\n",
       " 'dx/dy': 2036.7006838144548,\n",
       " 'dy/dx': 0.0004909901626424263,\n",
       " 'pmarg_old': 2000.0,\n",
       " 'pmarg': 2074.0748377251334,\n",
       " 'p': 2036.7006838144548}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order1.buyx(2000, execute = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06838de6",
   "metadata": {},
   "source": [
    "With `execute = True` (default), the order object is changed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "83135da2",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y=8.5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'y_old': 7.0,\n",
       " 'y': 8.5,\n",
       " 'dy': -1.5,\n",
       " 'yint_old': None,\n",
       " 'y_int': 10.0,\n",
       " 'expanded': False,\n",
       " 'x': 3084.9138354523297,\n",
       " 'dx': -3264.635360664166,\n",
       " 'tkny': 'ETH',\n",
       " 'tknx': 'USDC',\n",
       " 'tx': 'Buy 1.5 ETH sell USDC',\n",
       " 'dx/dy': 2176.4235737761105,\n",
       " 'dy/dx': 0.00045946938456699067,\n",
       " 'pmarg_old': 2239.8208329946465,\n",
       " 'pmarg': 2114.8207493700456,\n",
       " 'p': 2176.4235737761105}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_ = CarbonOrderUI.from_order(order1)\n",
    "print(f\"y={10-1-2+1.5}\")\n",
    "order_.selly(1)\n",
    "order_.selly(2)\n",
    "order_.selly(-1.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11e61267",
   "metadata": {},
   "source": [
    "## Order book calculations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cd3abd3",
   "metadata": {},
   "source": [
    "First we create a staggered list of 10 orders. Note that all those orders are only half-filled. The corresponding marginal prices are shown in the printout."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c2bfc1e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y=5 pa=2000.0 pb=2500.0  pmarg=2229.1\n",
      "y=5 pa=2050.0 pb=2550.0  pmarg=2279.6\n",
      "y=5 pa=2100.0 pb=2600.0  pmarg=2330.0\n",
      "y=5 pa=2150.0 pb=2650.0  pmarg=2380.4\n",
      "y=5 pa=2200.0 pb=2700.0  pmarg=2430.8\n",
      "y=5 pa=2250.0 pb=2750.0  pmarg=2481.2\n",
      "y=5 pa=2300.0 pb=2800.0  pmarg=2531.6\n",
      "y=5 pa=2350.0 pb=2850.0  pmarg=2581.9\n",
      "y=5 pa=2400.0 pb=2900.0  pmarg=2632.3\n",
      "y=5 pa=2450.0 pb=2950.0  pmarg=2682.6\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2229.1236000336485, 2682.6140015319056)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders = [\n",
    "    CarbonOrderUI.from_prices(\"ETH/USDC\", \"ETH\", 2000+50*i, 2500+50*i, 10, 5)\n",
    "    for i in range(10)\n",
    "]\n",
    "for o in orders:\n",
    "    print(f\"y={o.y} pa={round(o.pa,0)} pb={round(o.pb,0)}  pmarg={round(o.p_marg,1)}\")\n",
    "margp = [o.p_marg for o in orders]\n",
    "min(margp), max(margp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e13df660",
   "metadata": {},
   "source": [
    "We are now looking at the dy released if the market moves to 2100. Those are all 0, because all positions are already beyond 2100 (as we can see above, the lowest marginal prices is about 2229)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "785a27d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[o.dyfromp_f(2100) for o in orders]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "799f4448",
   "metadata": {},
   "source": [
    "If markets go all the way to 3000 -- which is above the upper end of the highest range which is at 2950 -- we see that all positions release their 5 ETH."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "789712cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 5, 5, 5, 5, 5, 5, 5, 5, 5]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[o.dyfromp_f(3000) for o in orders]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d281b70d",
   "metadata": {},
   "source": [
    "For 2300 we see that only the first two positions contribute -- they release 1.39 and 0.40 ETH respectively. The current marginal price of the 3rd position is about 2330 which is already to high, and the other ones are even higher. If the market goes to 2500, the 1st position is fully used up, and the the next 4 position partially."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "96b73244",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.39, 0.41, 0, 0, 0, 0, 0, 0, 0, 0]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[round(o.dyfromp_f(2300),2) for o in orders]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7138b6c6",
   "metadata": {},
   "source": [
    "For 2500, the first 6 positions release ETH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c33112d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5.0, 4.14, 3.24, 2.32, 1.36, 0.38, 0, 0, 0, 0]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[round(o.dyfromp_f(2500),2) for o in orders]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ee29d2a",
   "metadata": {},
   "source": [
    "We now define `dy_f` as the aggregate ETH released from all positions defined above, and we compute the corresponding USDC inflow `dx_f` from this. In case you are not familiar with the `lamba` syntax, this is simply a short form for saying that `dy_f(p)` and `dx_f(p)` are functions of p.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "347f53f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dy_f = lambda p: sum(o.dyfromp_f(p) for o in orders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "81d2ffb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "dx_f = lambda p: sum(o.dxfromdy_f(o.dyfromp_f(p)) for o in orders)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72f374da",
   "metadata": {},
   "source": [
    "We can then plot the ETH release (first chart) and USDC inflow (second chart) as a function of marginal price of the AMM, keeping in mind that in reasonably liquid markets the marginal price of the AMM will correspond to the market price of ETH/USDC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d8b5f4cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "pvals = np.linspace(2000, 3000)\n",
    "dyvals = [dy_f(p) for p in pvals]\n",
    "dxvals = [dx_f(p) for p in pvals]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3411062a",
   "metadata": {},
   "source": [
    "The chart below looks at (ETH) outflows vs (USDC) inflows. Unsurprisingly it is monotonically increasing. It also looks mostly linear but this is an illusion as the prices change along the way as we can see in the marginal and effective price charts below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "11f100e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Aggregate ETH release')"
      ]
     },
     "execution_count": 35,
     "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": [
    "plt.plot(dyvals, dxvals)\n",
    "plt.title(\"Inflows vs outflows\")\n",
    "plt.ylabel(\"Aggregate USDC inflow\")\n",
    "plt.xlabel(\"Aggregate ETH release\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "143e9f9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Aggregate ETH release')"
      ]
     },
     "execution_count": 36,
     "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": [
    "plt.plot(dyvals, pvals)\n",
    "plt.title(\"Marginal price chart\")\n",
    "plt.ylabel(\"Marginal price (USDC per ETH)\")\n",
    "plt.xlabel(\"Aggregate ETH release\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5379b53b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Effective price (USDC per ETH)')"
      ]
     },
     "execution_count": 37,
     "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": [
    "plt.plot(dyvals, [dx/dy if dy>0 else None for dx,dy in zip(dxvals, dyvals)])\n",
    "plt.title(\"Effective price chart\")\n",
    "plt.xlabel(\"Aggregate ETH release\")\n",
    "plt.ylabel(\"Effective price (USDC per ETH)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3e57b88d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Aggregate ETH release')"
      ]
     },
     "execution_count": 38,
     "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": [
    "plt.plot(pvals, dyvals)\n",
    "plt.title(\"Price reaction chart\")\n",
    "plt.xlabel(\"Marginal price (USDC per ETH)\")\n",
    "plt.ylabel(\"Aggregate ETH release\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "76cc87d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Aggregate USDC inflow')"
      ]
     },
     "execution_count": 39,
     "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": [
    "plt.plot(pvals, dxvals)\n",
    "plt.title(\"USDC inflow vs market price\")\n",
    "plt.xlabel(\"Marginal price (USDC per ETH)\")\n",
    "plt.ylabel(\"Aggregate USDC inflow\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b35348d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Effective price (USDC per ETH)')"
      ]
     },
     "execution_count": 40,
     "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": [
    "plt.plot(pvals, [dx/dy if dy>0 else None for dx,dy in zip(dxvals, dyvals)])\n",
    "plt.xlabel(\"Marginal price (USDC per ETH)\")\n",
    "plt.ylabel(\"Effective price (USDC per ETH)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bcfd7f41",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bead01f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,py:light"
  },
  "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}