Uniswap vs Sushiswap [Volume / Liquidity]

uni_vs_sushi.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Sushi vs Uni.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "l_QWhshthaOE"
      },
      "source": [
        "import asyncio\r\n",
        "import nest_asyncio\r\n",
        "\r\n",
        "from gql import gql, Client\r\n",
        "from gql.transport.aiohttp import AIOHTTPTransport\r\n",
        "\r\n",
        "from datetime import datetime\r\n",
        "\r\n",
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "plt.style.use(\"fivethirtyeight\")\r\n",
        "\r\n",
        "nest_asyncio.apply()"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "45eSwnnbhnGY"
      },
      "source": [
        "async def main():\r\n",
        "  sushi_transport = AIOHTTPTransport(url=\"https://api.thegraph.com/subgraphs/name/jiro-ono/sushiswap-v1-exchange\")\r\n",
        "  sushi_client = Client(transport=sushi_transport, fetch_schema_from_transport=True)\r\n",
        "\r\n",
        "  uni_transport = AIOHTTPTransport(url=\"https://api.thegraph.com/subgraphs/name/uniswap/uniswap-v2\")\r\n",
        "  uni_client = Client(transport=uni_transport, fetch_schema_from_transport=True)\r\n",
        "\r\n",
        "  query = gql(\r\n",
        "      \"\"\"\r\n",
        "        query {\r\n",
        "          uniswapDayDatas (where: { date_gt: 1606731200 }) {\r\n",
        "            date\r\n",
        "            dailyVolumeUSD\r\n",
        "            totalLiquidityUSD\r\n",
        "          }\r\n",
        "        }\r\n",
        "      \"\"\"\r\n",
        "  )\r\n",
        "\r\n",
        "  async with sushi_client as session:\r\n",
        "    sushi_result = await session.execute(query)\r\n",
        "  async with uni_client as session:\r\n",
        "    uni_result = await session.execute(query)\r\n",
        "  return [ uni_result[\"uniswapDayDatas\"], sushi_result[\"uniswapDayDatas\"] ]\r\n",
        "\r\n",
        "futures = [main]\r\n",
        "loop = asyncio.get_event_loop()\r\n",
        "result = loop.run_until_complete(main())"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sDi_rZaYiH-H"
      },
      "source": [
        "uni = result[0]\r\n",
        "sushi = result[1]\r\n",
        "\r\n",
        "dates = []\r\n",
        "uni_v = []\r\n",
        "sushi_v = []\r\n",
        "uni_l = []\r\n",
        "sushi_l = []\r\n",
        "uni_r = []\r\n",
        "sushi_r = []\r\n",
        "\r\n",
        "for (u, s) in zip(uni, sushi):\r\n",
        "  dates.append(datetime.utcfromtimestamp(u['date']).strftime(\"%b %d '%y\"))\r\n",
        "  u_v = float(u[\"dailyVolumeUSD\"])\r\n",
        "  s_v = float(s[\"dailyVolumeUSD\"])\r\n",
        "  u_l = float(u[\"totalLiquidityUSD\"])\r\n",
        "  s_l = float(s[\"totalLiquidityUSD\"])\r\n",
        "  uni_v.append(u_v)\r\n",
        "  sushi_v.append(s_v)\r\n",
        "  uni_l.append(u_l)\r\n",
        "  sushi_l.append(s_l)\r\n",
        "  uni_r.append(u_v*100/u_l)\r\n",
        "  sushi_r.append(s_v*100/s_l)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 762
        },
        "id": "DaQw348_waGq",
        "outputId": "009dd324-9233-45d0-a0ce-e43c4c5f45a3"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(32, 18))\r\n",
        "\r\n",
        "ind = np.arange(len(uni_r))\r\n",
        "width = 0.4\r\n",
        "ax.bar(ind, uni_r, width, label=\"Uniswap\", color=\"#ff007a\")\r\n",
        "ax.bar(ind + width, sushi_r, width, label=\"Sushiswap\", color=\"#2baee4\")\r\n",
        "\r\n",
        "ax.xaxis.set_tick_params(rotation=45)\r\n",
        "plt.title(\"Volume / Liquidity (%)\")\r\n",
        "\r\n",
        "plt.xticks((ind + width / 2) - width, dates)\r\n",
        "ax.legend(loc=\"best\", fontsize=18)\r\n",
        "plt.tick_params(labelsize=16)\r\n",
        "\r\n",
        "vals = ax.get_yticks()\r\n",
        "ax.set_yticklabels([\"{:.0f}%\".format(x) for x in vals])\r\n",
        "\r\n",
        "plt.show()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 2304x1296 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2EuMtdtrxiT-"
      },
      "source": [
        ""
      ],
      "execution_count": 4,
      "outputs": []
    }
  ]
}