Liquidity Pnl.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "fd45424c-08cb-4540-9be1-a4cee9d10695",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a transaction function\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch.nn as nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "4211f6c5-a30f-484e-b40f-2f31c0138db8",
   "metadata": {},
   "outputs": [],
   "source": [
    "class OptionsOptimizer(nn.Module): # setting up neural network\n",
    "    def __init__(self, derivatives): # takes in derivatives as parmeters\n",
    "        super(OptionsOptimizer, self).__init__() \n",
    "        self.derivatives = derivatives # derivatives inputed\n",
    "        self.theta = torch.rand(len(derivatives), requires_grad=True) # target\n",
    "\n",
    "    def forward(self, x): # when we run forward we need to provide input prices for neural network to run\n",
    "        derivatives_payoff = [\n",
    "            [d.payoff_fun(sample) for d in self.derivatives] for sample in x \n",
    "        ] # for each sample in x we are evaluating each payoff for d in self.derivatives \n",
    "        derivatives_payoff_tensor = torch.tensor(derivatives_payoff) # create a tensor of all the derivative payoffs\n",
    "        out = torch.sum(self.theta * derivatives_payoff_tensor, dim=1) # multiply the derivative payoff by the theta\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "5876ce80-aabe-4a8d-a9bd-99e5ed40bd97",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Liquidity_Pnl:\n",
    "    def __init__(self, token0, token1, price0, price1, ticklower, tickupper):\n",
    "        self.token0 = token0 #  number of btc\n",
    "        self.token1 = token1 # number of usdc\n",
    "        self.price0 = price0 # price of btc\n",
    "        self.price1 = price1 # price of usdc\n",
    "        self.lower = (1.0001**ticklower) * 10**2 # lower bound\n",
    "        self.upper = (1.0001**tickupper) * 10**2 # upper bound \n",
    "        self.balance = (token0 * token1) ** 0.5 # sqrt k \n",
    "        self.check = token0 * token1\n",
    "        self.price_list = []\n",
    "    def notional(self):\n",
    "        notional = None\n",
    "        if self.price0 < self.lower:\n",
    "            notional = self.token0\n",
    "        elif self.lower < self.price0 < self.upper:\n",
    "            notional = (self.token0\n",
    "                        * (self.price0 / self.lower) ** 0.5 \n",
    "                        * (self.upper**0.5 - self.lower**0.5) \n",
    "                        / (self.upper ** 0.5 \n",
    "                           - self.price0 ** 0.5\n",
    "                          )\n",
    "                       ) \n",
    "        else: \n",
    "            notional = self.token1 / (\n",
    "                (self.lower * self.upper)**0.5\n",
    "            )\n",
    "        return notional  \n",
    "        \n",
    "    def token0_amount(self, final_price):\n",
    "        if final_price <= self.lower:\n",
    "            amount_a = self.notional()\n",
    "            amount_b = 0\n",
    "        elif self.lower < final_price < self.upper:\n",
    "            amount_a = (self.notional() * (self.lower / final_price)**0.5 \n",
    "            * ((self.upper) ** 0.5 - (final_price) ** 0.5)\n",
    "            / ( \n",
    "                (self.upper) ** 0.5\n",
    "                - (self.lower) ** 0.5\n",
    "            )\n",
    "                       )\n",
    "            amount_b = (\n",
    "                self.notional()\n",
    "                * ((self.lower * self.upper) ** 0.5)\n",
    "                * (((final_price) ** 0.5) - ((self.lower) ** 0.5))\n",
    "                / (\n",
    "                    (self.upper) ** 0.5\n",
    "                    - (self.lower) ** 0.5\n",
    "                )\n",
    "            )\n",
    "        else:\n",
    "            amount_a = 0\n",
    "            amount_b = (\n",
    "                self.notional() * (self.lower * self.upper) ** 0.5 \n",
    "            )\n",
    "        return (amount_a * final_price) + amount_b \n",
    "    \n",
    "    def calculate_pnl(self):\n",
    "        self.price_list = []\n",
    "        adam_list = []\n",
    "        for i in range(0, 100000):\n",
    "            final_price = i \n",
    "            pnl = (self.token0_amount(final_price) \n",
    "                   - (self.token0 * self.price0 + self.token1))\n",
    "            adam_list.append(pnl)\n",
    "            self.price_list.append(final_price)\n",
    "        return adam_list\n",
    "\n",
    "    def above(self):\n",
    "        high_values = [pnl if pnl > 0 else 0 for pnl in self.calculate_pnl()]\n",
    "        return high_values\n",
    "\n",
    "    def beneath(self):\n",
    "        low_values = [pnl if pnl < 0 else 0 for pnl in self.calculate_pnl()]\n",
    "        return low_values\n",
    "        \n",
    "    def plot(self):\n",
    "        plt.figure(figsize=(12,8))\n",
    "        plot = plt.scatter(self.price_list, self.calculate_pnl(), c='b', s=4)\n",
    "        plt.xlabel('Final Price')\n",
    "        plt.ylabel('Pnl ($)')\n",
    "        plt.title('Liquidity Position Pnl')\n",
    "        # Adding a horizontal dotted line at y=0\n",
    "        plt.axhline(y=0, color='black', linestyle='--', xmax= 1)\n",
    "        plt.fill_between(self.price_list, self.above(), color='Limegreen', alpha=0.4)\n",
    "        plt.fill_between(self.price_list, self.beneath(), color='LightCoral', alpha=0.4)\n",
    "        return plot"
   ]
  },
  {
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   "id": "7b59d352-eadb-452d-bbda-664e79f2db73",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       " -710224.257646441,\n",
       " -710191.5027924409,\n",
       " -710158.7479384409,\n",
       " -710125.9930844408,\n",
       " -710093.2382304408,\n",
       " -710060.4833764408,\n",
       " -710027.7285224408,\n",
       " -709994.9736684407,\n",
       " -709962.2188144407,\n",
       " -709929.4639604406,\n",
       " -709896.7091064405,\n",
       " -709863.9542524405,\n",
       " -709831.1993984404,\n",
       " -709798.4445444404,\n",
       " -709765.6896904404,\n",
       " -709732.9348364404,\n",
       " -709700.1799824403,\n",
       " -709667.4251284403,\n",
       " -709634.6702744402,\n",
       " -709601.9154204401,\n",
       " -709569.1605664401,\n",
       " -709536.40571244,\n",
       " -709503.65085844,\n",
       " -709470.89600444,\n",
       " -709438.14115044,\n",
       " -709405.3862964399,\n",
       " -709372.6314424399,\n",
       " -709339.8765884398,\n",
       " -709307.1217344397,\n",
       " -709274.3668804397,\n",
       " -709241.6120264396,\n",
       " -709208.8571724396,\n",
       " -709176.1023184396,\n",
       " -709143.3474644396,\n",
       " -709110.5926104395,\n",
       " -709077.8377564395,\n",
       " -709045.0829024394,\n",
       " -709012.3280484393,\n",
       " -708979.5731944393,\n",
       " -708946.8183404392,\n",
       " -708914.0634864392,\n",
       " -708881.3086324392,\n",
       " -708848.5537784392,\n",
       " -708815.7989244391,\n",
       " -708783.0440704391,\n",
       " -708750.289216439,\n",
       " -708717.534362439,\n",
       " -708684.7795084389,\n",
       " -708652.0246544388,\n",
       " -708619.2698004388,\n",
       " -708586.5149464388,\n",
       " -708553.7600924388,\n",
       " -708521.0052384387,\n",
       " -708488.2503844387,\n",
       " -708455.4955304386,\n",
       " -708422.7406764386,\n",
       " -708389.9858224385,\n",
       " -708357.2309684384,\n",
       " -708324.4761144384,\n",
       " -708291.7212604384,\n",
       " -708258.9664064384,\n",
       " -708226.2115524383,\n",
       " -708193.4566984383,\n",
       " -708160.7018444382,\n",
       " -708127.9469904382,\n",
       " -708095.1921364381,\n",
       " -708062.437282438,\n",
       " -708029.682428438,\n",
       " -707996.927574438,\n",
       " -707964.172720438,\n",
       " -707931.4178664379,\n",
       " -707898.6630124379,\n",
       " -707865.9081584378,\n",
       " -707833.1533044378,\n",
       " -707800.3984504377,\n",
       " -707767.6435964376,\n",
       " -707734.8887424376,\n",
       " -707702.1338884376,\n",
       " -707669.3790344376,\n",
       " -707636.6241804375,\n",
       " -707603.8693264375,\n",
       " -707571.1144724374,\n",
       " -707538.3596184374,\n",
       " -707505.6047644373,\n",
       " -707472.8499104372,\n",
       " -707440.0950564373,\n",
       " -707407.3402024372,\n",
       " -707374.5853484372,\n",
       " -707341.8304944371,\n",
       " -707309.0756404371,\n",
       " -707276.320786437,\n",
       " -707243.565932437,\n",
       " -707210.8110784369,\n",
       " -707178.0562244368,\n",
       " -707145.3013704369,\n",
       " -707112.5465164369,\n",
       " -707079.7916624368,\n",
       " -707047.0368084367,\n",
       " -707014.2819544367,\n",
       " -706981.5271004366,\n",
       " -706948.7722464366,\n",
       " -706916.0173924365,\n",
       " -706883.2625384365,\n",
       " -706850.5076844365,\n",
       " -706817.7528304365,\n",
       " -706784.9979764364,\n",
       " -706752.2431224363,\n",
       " -706719.4882684363,\n",
       " -706686.7334144362,\n",
       " -706653.9785604362,\n",
       " -706621.2237064361,\n",
       " -706588.468852436,\n",
       " -706555.7139984361,\n",
       " ...]"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = Liquidity_Pnl(19.94218148, 265132.506276, 23776, 1, 51960, 59940)\n",
    "x.calculate_pnl()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "c5c5aac3-1263-4b1c-8196-6eabb69737ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fffbe5446d0>"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b577ad7-0444-4c98-b85a-4c47cd14d663",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Train:\n",
    "    def __init__(self, "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}