code/EIP1559_Analysis.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SciEcon/SoK_Blockchain_Decentralization/blob/main/code/EIP1559_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***All data is from token genesis to TODAY***\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "ornmgQIPONew"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Decentralization Index Import"
      ],
      "metadata": {
        "id": "gFU9Ifc1OrWZ"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BVuYSvSF1Ucm"
      },
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import decimal\n",
        "from datetime import datetime, date, timedelta, timezone\n",
        "from dateutil.relativedelta import relativedelta\n",
        "import math\n",
        "import plotly.offline as py\n",
        "import plotly.graph_objects as go\n",
        "from plotly.subplots import make_subplots\n",
        "import plotly.figure_factory as ff\n",
        "from scipy import stats\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn import datasets, linear_model\n",
        "import statsmodels.api as sm\n",
        "import plotly.colors as pc\n",
        "import statsmodels.api as sm\n",
        "import matplotlib.pyplot as plt\n",
        "import re\n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Bnt_ent = pd.read_csv('https://raw.githubusercontent.com/SciEcon/SoK_Blockchain_Decentralization/main/Data_TokenIndex/Bnt_ent.csv',parse_dates=['date'])\n",
        "#%%\n",
        "Uni_ent = pd.read_csv('https://raw.githubusercontent.com/SciEcon/SoK_Blockchain_Decentralization/main/Data_TokenIndex/Uni_ent.csv',parse_dates=['date'])\n",
        "\n",
        "#%%\n",
        "Aave_ent = pd.read_csv('https://raw.githubusercontent.com/SciEcon/SoK_Blockchain_Decentralization/main/Data_TokenIndex/Aave_ent.csv',parse_dates=['date'])\n",
        "\n",
        "#%%\n",
        "Comp_ent = pd.read_csv('https://raw.githubusercontent.com/SciEcon/SoK_Blockchain_Decentralization/main/Data_TokenIndex/Comp_ent.csv', parse_dates=['date'])\n",
        "\n",
        "Maker_ent = pd.read_csv('https://raw.githubusercontent.com/SciEcon/SoK_Blockchain_Decentralization/main/Data_TokenIndex/Maker_ent.csv', parse_dates=['date'])\n",
        "\n",
        "#%%\n",
        "Dai_ent= pd.read_csv('https://raw.githubusercontent.com/SciEcon/SoK_Blockchain_Decentralization/main/Data_TokenIndex/Dai_ent.csv', parse_dates=['date'],)\n",
        "\n",
        "#%%\n",
        "Lqty_ent = pd.read_csv('https://raw.githubusercontent.com/SciEcon/SoK_Blockchain_Decentralization/main/Data_TokenIndex/Lqty_ent.csv',parse_dates=['date'])\n",
        "\n",
        "#%%\n",
        "Sushi_ent = pd.read_csv('https://raw.githubusercontent.com/SciEcon/SoK_Blockchain_Decentralization/main/Data_TokenIndex/Sushi_ent.csv', parse_dates=['date'])\n",
        "\n",
        "#%%\n",
        "Amp_ent = pd.read_csv('https://raw.githubusercontent.com/SciEcon/SoK_Blockchain_Decentralization/main/Data_TokenIndex/Amp_ent.csv', parse_dates=['date'])\n",
        "\n",
        "#%%\n",
        "Sai_ent= pd.read_csv('https://raw.githubusercontent.com/SciEcon/SoK_Blockchain_Decentralization/main/Data_TokenIndex/Sai_ent.csv', parse_dates=['date'],)\n",
        "\n",
        "#%%\n",
        "Bal_ent= pd.read_csv('https://raw.githubusercontent.com/SciEcon/SoK_Blockchain_Decentralization/main/Data_TokenIndex/Bal_ent.csv', parse_dates=['date'],)\n",
        "#%%\n",
        "Torn_ent= pd.read_csv('https://raw.githubusercontent.com/SciEcon/SoK_Blockchain_Decentralization/main/Data_TokenIndex/Torn_ent.csv', parse_dates=['date'],)\n",
        "#%%\n",
        "Tribe_ent= pd.read_csv('https://raw.githubusercontent.com/SciEcon/SoK_Blockchain_Decentralization/main/Data_TokenIndex/Tribe_ent.csv', parse_dates=['date'],)\n",
        "#%%\n",
        "Ren_ent= pd.read_csv('https://raw.githubusercontent.com/SciEcon/SoK_Blockchain_Decentralization/main/Data_TokenIndex/Ren_ent.csv', parse_dates=['date'],)\n",
        "#%%\n",
        "CVX_ent= pd.read_csv('https://raw.githubusercontent.com/SciEcon/SoK_Blockchain_Decentralization/main/Data_TokenIndex/CVX_ent.csv', parse_dates=['date'],)\n",
        "#%%\n",
        "CVXCRV_ent= pd.read_csv('https://raw.githubusercontent.com/SciEcon/SoK_Blockchain_Decentralization/main/Data_TokenIndex/CVXCRV_ent.csv', parse_dates=['date'],)\n",
        "#%%\n",
        "Nxm_ent= pd.read_csv('https://raw.githubusercontent.com/SciEcon/SoK_Blockchain_Decentralization/main/Data_TokenIndex/Nxm_ent.csv', parse_dates=['date'],)\n",
        "#%%\n",
        "Snx_ent= pd.read_csv('https://raw.githubusercontent.com/SciEcon/SoK_Blockchain_Decentralization/main/Data_TokenIndex/Snx_ent.csv', parse_dates=['date'],)\n",
        "#\n",
        "Lusd_ent= pd.read_csv('https://raw.githubusercontent.com/SciEcon/SoK_Blockchain_Decentralization/main/Data_TokenIndex/Lusd_ent.csv', parse_dates=['date'],)\n",
        "#\n",
        "Usdt_ent = pd.read_csv('https://raw.githubusercontent.com/SciEcon/SoK_Blockchain_Decentralization/main/Data_TokenIndex/Usdt_ent.csv', parse_dates=['date'],)"
      ],
      "metadata": {
        "id": "YvoXYoa7mQ_6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Other Data Sources"
      ],
      "metadata": {
        "id": "ovro3qvUVZhV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "CoinMetrics: Market Data"
      ],
      "metadata": {
        "id": "AtNQulJcVx59"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Dai\n",
        "url_dai = \"https://github.com/coinmetrics-io/data/raw/master/csv/dai.csv\"\n",
        "dai_met = pd.read_csv(url_dai,header='infer')\n",
        "# dai_met.to_csv('/content/drive/MyDrive/Decentralization Research/CoinMetrics/dai.csv')\n",
        "#Sai\n",
        "url_sai = \"https://github.com/coinmetrics-io/data/raw/master/csv/sai.csv\"\n",
        "sai_met = pd.read_csv(url_sai,header='infer')\n",
        "# sai_met.to_csv('/content/drive/MyDrive/Decentralization Research/CoinMetrics/sai.csv')\n",
        "#BTC\n",
        "url_btc = \"https://github.com/coinmetrics-io/data/raw/master/csv/btc.csv\"\n",
        "btc_met = pd.read_csv(url_btc,header='infer')\n",
        "# btc_met.to_csv('/content/drive/MyDrive/Decentralization Research/CoinMetrics/btc.csv')\n",
        "#BTC\n",
        "url_eth = \"https://github.com/coinmetrics-io/data/raw/master/csv/eth.csv\"\n",
        "eth_met = pd.read_csv(url_eth,header='infer')\n",
        "# eth_met.to_csv('/content/drive/MyDrive/Decentralization Research/CoinMetrics/eth.csv')\n",
        "#Dai\n",
        "url_aave = \"https://github.com/coinmetrics-io/data/raw/master/csv/aave.csv\"\n",
        "aave_met = pd.read_csv(url_aave,header='infer')\n",
        "\n",
        "url_aave = \"https://github.com/coinmetrics-io/data/raw/master/csv/comp.csv\"\n",
        "comp_met = pd.read_csv(url_aave,header='infer')\n",
        "\n",
        "# url_aave = \"https://github.com/coinmetrics-io/data/raw/master/csv/lusd.csv\"\n",
        "# lusd_met = pd.read_csv(url_aave,header='infer')\n",
        "\n",
        "url_aave = \"https://github.com/coinmetrics-io/data/raw/master/csv/usdt.csv\"\n",
        "usdt_met = pd.read_csv(url_aave,header='infer')\n"
      ],
      "metadata": {
        "id": "WJCmaqqmVYev",
        "outputId": "414871fb-5cc8-4981-e76f-6fad8fc32d42",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-22-6b3b8cd280fa>:11: DtypeWarning: Columns (147) have mixed types. Specify dtype option on import or set low_memory=False.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "EIP-1559 Adoption Data"
      ],
      "metadata": {
        "id": "wR9FihJNibC_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Timestamp\n",
        "timestamps = pd.read_csv('https://raw.githubusercontent.com/SciEcon/SoK_Blockchain_Decentralization/main/Data_EIP1559/timestamp.csv',header='infer')"
      ],
      "metadata": {
        "id": "seCxPe-eiaRl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Adoption\n",
        "adoption = pd.read_csv('https://raw.githubusercontent.com/SciEcon/SoK_Blockchain_Decentralization/main/Data_EIP1559/adoption.csv',header='infer')"
      ],
      "metadata": {
        "id": "EQC9UVEtjW99"
      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "adoption"
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              "0           12865000          221            0            0             221\n",
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              "272026      13134996           34           27           27               9\n",
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              "272028      13134998           53           47           47              50\n",
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              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9eb9aeee-c411-4fc0-917c-1eaf52b094e9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_31d228fa-5eba-496b-b66d-49907e16a834\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('adoption')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_31d228fa-5eba-496b-b66d-49907e16a834 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('adoption');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "adoption"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# EIP1559 Econometrics\n"
      ],
      "metadata": {
        "id": "QFu27aECnLlD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## RDD on Time"
      ],
      "metadata": {
        "id": "s0wyEsNQdOME"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ! pip install stargazer\n",
        "# from stargazer.stargazer import Stargazer\n",
        "\n",
        "\n",
        "import statsmodels.formula.api as smf\n",
        "import statsmodels.api as sm\n",
        "\n",
        "from IPython.core.display import HTML\n",
        "from datetime import datetime,date\n",
        "import time\n",
        "# import kaleido\n",
        "\n"
      ],
      "metadata": {
        "id": "Q_zsJgxhwDjM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Decentralization_Analysis:\n",
        "\n",
        "\n",
        "  def __init__(self, token, metrics):\n",
        "    self.reset()\n",
        "    self.token = token.copy()\n",
        "    self.metrics = metrics.copy()\n",
        "    self.result = None\n",
        "    self.token['date'].replace(to_replace= r'\\s00:00:00', value='', regex=True, inplace=True)\n",
        "\n",
        "    ent_exp = pd.DataFrame()\n",
        "    ent_exp['val'] = self.token['val']\n",
        "    ent_exp['date'] = self.token['date']\n",
        "    ent_exp['n'] = range(1,ent_exp.shape[0]+1)\n",
        "    ent_exp['SMA5'] =  ent_exp.val.rolling(5, min_periods=1).mean()\n",
        "    ent_exp['SMA30'] =  ent_exp.val.rolling(30, min_periods=1).mean()\n",
        "    # ent_exp['SMA60'] =  ent_exp.val.rolling(60, min_periods=1).mean()\n",
        "    # ent_exp['SMA90'] =  ent_exp.val.rolling(90, min_periods=1).mean()\n",
        "    # ent_exp['SMA180'] =  ent_exp.val.rolling(180, min_periods=1).mean()\n",
        "    ent_exp['EMA'] = ent_exp['val'].ewm(alpha=0.1, adjust=False).mean()\n",
        "    # ent_exp['EMA0.3'] = ent_exp['val'].ewm(alpha=0.3, adjust=False).mean()\n",
        "\n",
        "\n",
        "\n",
        "    columns =['time','CapMrktCurUSD', 'PriceUSD', 'VtyDayRet30d', 'TxTfrValAdjUSD',\n",
        "                            'TxTfrCnt']\n",
        "    metrics1 = self.metrics[columns]\n",
        "    metrics1.time=pd.to_datetime(metrics1.time)\n",
        "    ent_exp.date=pd.to_datetime(ent_exp.date)\n",
        "    metrics1.rename(columns={'time':'date'},inplace=True)\n",
        "    metrics1.dropna(inplace=True)\n",
        "    df = ent_exp.merge(metrics1,on='date')\n",
        "\n",
        "\n",
        "\n",
        "    df.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
        "    df.dropna(inplace=True)\n",
        "    # subset=['date','val','CapMrktCurUSD', 'PriceUSD', 'VtyDayRet30d', 'TxTfrValAdjUSD',\n",
        "    #                         'TxTfrCnt', 'BTC_PriceUSD', 'BTC_VtyDayRet30d', 'BTC_TxTfrValAdjUSD',\n",
        "    #                         'BTC_TxTfrCnt', 'ETH_PriceUSD', 'ETH_VtyDayRet30d']\n",
        "    df['ROI'] = df['PriceUSD'].pct_change(1)\n",
        "\n",
        "    df['EIP'] = (df['date'] > '2021-08-05') + 0\n",
        "    df['date'].apply(pd.to_datetime)\n",
        "    df['Day'] = (df['date'] - datetime(2021,8,5)).dt.days\n",
        "    df['EIP_Day'] = df['EIP']*df['Day']\n",
        "\n",
        "    df = df[df['date'] != '2021-08-05']\n",
        "\n",
        "\n",
        "    df2 = df[df['date']>='2021-07-15']\n",
        "    df2 = df2[df2['date']<='2021-09-18']\n",
        "\n",
        "    self.df2 = df2.copy()\n",
        "\n",
        "    df12 = df[df['date']>='2021-07-31']\n",
        "    df12 = df12[df12['date']<='2021-08-16']\n",
        "\n",
        "    self.df12 = df12.copy()\n",
        "\n",
        "    df = df[df['date']>='2021-07-25']\n",
        "    df = df[df['date']<='2021-08-27']\n",
        "\n",
        "\n",
        "\n",
        "    self.viz = df.copy()\n",
        "\n",
        "    # df = df[(df['date']<='2021-08-05') | (df['date']>='2021-08-16')]\n",
        "    self.df = df.copy()\n",
        "\n",
        "\n",
        "  def regression(self):\n",
        "      results1 = smf.ols('SMA30 ~ 1+ EIP + Day + EIP_Day', data=self.df).fit()\n",
        "      results2 = smf.ols('SMA30 ~ 1+ EIP + Day + EIP_Day + TxTfrValAdjUSD + TxTfrCnt', data=self.df).fit()\n",
        "      results3 = smf.ols('SMA30 ~ 1+ EIP + Day + EIP_Day +ROI + VtyDayRet30d + TxTfrValAdjUSD + TxTfrCnt', data=self.df).fit()\n",
        "      stargazer = Stargazer([results1,results2,results3])\n",
        "      stargazer.title('EIP1559 Regression Discontinuity')\n",
        "      stargazer.significant_digits(3)\n",
        "      stargazer.covariate_order(['EIP','Day','EIP_Day','TxTfrValAdjUSD','TxTfrCnt','ROI','VtyDayRet30d','Intercept'])\n",
        "      display(HTML(stargazer.render_html()))\n",
        "      print(stargazer.render_latex())\n",
        "      results1 = smf.ols('EMA ~ 1+ EIP + Day + EIP_Day', data=self.df).fit()\n",
        "      results2 = smf.ols('EMA ~ 1+ EIP + Day + EIP_Day + TxTfrValAdjUSD + TxTfrCnt', data=self.df).fit()\n",
        "      results3 = smf.ols('EMA ~ 1+ EIP + Day + EIP_Day +ROI + VtyDayRet30d + TxTfrValAdjUSD + TxTfrCnt', data=self.df).fit()\n",
        "      stargazer = Stargazer([results1,results2,results3])\n",
        "      stargazer.title('EIP1559 Regression Discontinuity')\n",
        "      stargazer.significant_digits(3)\n",
        "      stargazer.covariate_order(['EIP','Day','EIP_Day','TxTfrValAdjUSD','TxTfrCnt','ROI','VtyDayRet30d','Intercept'])\n",
        "      display(HTML(stargazer.render_html()))\n",
        "      print(stargazer.render_latex())\n",
        "\n",
        "\n",
        "  def sensitvity(self):\n",
        "    results1 = smf.ols('SMA30 ~ 1+ EIP + Day + EIP_Day', data=self.df2).fit()\n",
        "    results2 = smf.ols('SMA30 ~ 1+ EIP + Day + EIP_Day + TxTfrValAdjUSD + TxTfrCnt', data=self.df2).fit()\n",
        "    results3 = smf.ols('SMA30 ~ 1+ EIP + Day + EIP_Day +ROI + VtyDayRet30d + TxTfrValAdjUSD + TxTfrCnt', data=self.df2).fit()\n",
        "    stargazer = Stargazer([results1,results2,results3])\n",
        "    stargazer.title('EIP1559 Regression Discontinuity')\n",
        "    stargazer.significant_digits(3)\n",
        "    stargazer.covariate_order(['EIP','Day','EIP_Day','TxTfrValAdjUSD','TxTfrCnt','ROI','VtyDayRet30d','Intercept'])\n",
        "    display(HTML(stargazer.render_html()))\n",
        "    print(stargazer.render_latex())\n",
        "    results1 = smf.ols('SMA30 ~ 1+ EIP + Day + EIP_Day', data=self.df12).fit()\n",
        "    results2 = smf.ols('SMA30 ~ 1+ EIP + Day + EIP_Day + TxTfrValAdjUSD + TxTfrCnt', data=self.df12).fit()\n",
        "    results3 = smf.ols('SMA30 ~ 1+ EIP + Day + EIP_Day +ROI + VtyDayRet30d + TxTfrValAdjUSD + TxTfrCnt', data=self.df12).fit()\n",
        "    stargazer = Stargazer([results1,results2,results3])\n",
        "    stargazer.title('EIP1559 Regression Discontinuity')\n",
        "    stargazer.significant_digits(3)\n",
        "    stargazer.covariate_order(['EIP','Day','EIP_Day','TxTfrValAdjUSD','TxTfrCnt','ROI','VtyDayRet30d','Intercept'])\n",
        "    display(HTML(stargazer.render_html()))\n",
        "    print(stargazer.render_latex())\n",
        "\n",
        "  def reset(self):\n",
        "      self.token = None\n",
        "      self.metrics = None\n",
        "      self.df = None\n",
        "      self.tvl = None\n"
      ],
      "metadata": {
        "id": "djcIs5WvnOqG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Dai = Decentralization_Analysis(Dai_ent,dai_met)"
      ],
      "metadata": {
        "id": "RTyvRMCIpsAv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = Dai.df\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9Px16pXTp4rg",
        "outputId": "d6d5a0b6-92dd-438d-c30f-4d30708db2b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             val       date    n        SMA5       SMA30         EMA  \\\n",
              "583   263.919633 2021-07-25  621  512.549400  556.707983  493.415478   \n",
              "584  1234.694750 2021-07-26  622  574.312834  540.473542  567.543405   \n",
              "585   912.870553 2021-07-27  623  644.790031  544.266912  602.076120   \n",
              "586   138.537347 2021-07-28  624  558.593675  510.113386  555.722243   \n",
              "587    67.027245 2021-07-29  625  523.409905  502.839147  506.852743   \n",
              "588   973.908703 2021-07-30  626  665.407719  523.395382  553.558339   \n",
              "589   267.094338 2021-07-31  627  471.887637  487.128556  524.911939   \n",
              "590  1033.923589 2021-08-01  628  496.098244  500.120842  575.813104   \n",
              "591   952.977654 2021-08-02  629  658.986306  502.669161  613.529559   \n",
              "592   185.009025 2021-08-03  630  682.582662  502.111057  570.677505   \n",
              "593  1156.670281 2021-08-04  631  719.134978  534.456968  629.276783   \n",
              "595   531.584877 2021-08-06  633  743.171429  568.617451  642.938060   \n",
              "596  1078.851719 2021-08-07  634  768.346242  573.832273  686.529426   \n",
              "597   796.056496 2021-08-08  635  890.555736  580.137067  697.482133   \n",
              "598   501.034906 2021-08-09  636  759.428661  584.487971  677.837410   \n",
              "599   410.809648 2021-08-10  637  663.667529  588.024816  651.134634   \n",
              "600   902.996699 2021-08-11  638  737.949894  603.230303  676.320840   \n",
              "601   203.342435 2021-08-12  639  562.848037  594.254757  629.023000   \n",
              "602   801.352568 2021-08-13  640  563.907251  605.161530  646.255957   \n",
              "603   603.155029 2021-08-14  641  584.331276  615.237915  641.945864   \n",
              "604   581.598488 2021-08-15  642  618.489044  625.906359  635.911126   \n",
              "605   503.744471 2021-08-16  643  538.638598  625.407400  622.694461   \n",
              "606  1221.285273 2021-08-17  644  742.227166  652.597253  682.553542   \n",
              "607  1162.450201 2021-08-18  645  814.446693  675.705582  730.543208   \n",
              "608   509.197165 2021-08-19  646  795.655120  672.751192  708.408604   \n",
              "609   789.589854 2021-08-20  647  837.253393  668.208268  716.526729   \n",
              "610   480.666336 2021-08-21  648  832.637766  665.547660  692.940689   \n",
              "611   473.312471 2021-08-22  649  683.043205  662.340772  670.977868   \n",
              "612   649.492443 2021-08-23  650  580.451654  675.892317  668.829325   \n",
              "613   507.287860 2021-08-24  651  580.069793  684.004591  652.675179   \n",
              "614   602.592957 2021-08-25  652  542.670413  662.934531  647.666956   \n",
              "615   553.327586 2021-08-26  653  557.202663  650.949766  638.233019   \n",
              "616   509.445841 2021-08-27  654  564.429337  663.313382  625.354301   \n",
              "\n",
              "     CapMrktCurUSD  PriceUSD  VtyDayRet30d  TxTfrValAdjUSD  TxTfrCnt  \\\n",
              "583   5.308923e+09  1.001319      0.000314    5.046705e+08   12748.0   \n",
              "584   5.314280e+09  1.000943      0.000319    9.177476e+08   22342.0   \n",
              "585   5.323567e+09  1.001337      0.000322    7.710389e+08   16751.0   \n",
              "586   5.358583e+09  1.001122      0.000298    2.857868e+09   14350.0   \n",
              "587   5.352118e+09  1.000930      0.000294    4.021327e+09   14543.0   \n",
              "588   5.368017e+09  1.000941      0.000284    6.173390e+08   16100.0   \n",
              "589   5.388795e+09  1.001094      0.000273    1.082540e+09   14776.0   \n",
              "590   5.411745e+09  1.001226      0.000263    6.880336e+08   15512.0   \n",
              "591   5.428476e+09  1.001440      0.000265    6.249126e+08   16218.0   \n",
              "592   5.435133e+09  1.000483      0.000319    1.999513e+09   14346.0   \n",
              "593   5.443340e+09  1.000389      0.000318    6.848119e+08   17015.0   \n",
              "595   5.464014e+09  1.000738      0.000323    6.883365e+08   13933.0   \n",
              "596   5.509692e+09  1.001180      0.000332    6.626645e+08   15579.0   \n",
              "597   5.547599e+09  1.001162      0.000331    6.948149e+08   14282.0   \n",
              "598   5.535275e+09  1.000380      0.000362    9.105665e+08   15299.0   \n",
              "599   5.577314e+09  1.000786      0.000369    7.919636e+08   13591.0   \n",
              "600   5.614677e+09  1.000559      0.000372    5.128071e+08   14623.0   \n",
              "601   5.647036e+09  1.000753      0.000342    1.212189e+09   14246.0   \n",
              "602   5.684582e+09  1.001885      0.000394    4.363608e+08   14741.0   \n",
              "603   5.696500e+09  1.001098      0.000419    5.877929e+08   14542.0   \n",
              "604   5.726022e+09  1.000955      0.000419    4.197429e+08   12178.0   \n",
              "605   5.730731e+09  1.000298      0.000436    6.786509e+08   13812.0   \n",
              "606   5.712987e+09  1.000591      0.000438    5.677394e+08   16182.0   \n",
              "607   5.722496e+09  1.000243      0.000442    6.367904e+08   16419.0   \n",
              "608   5.737403e+09  1.000881      0.000458    7.282589e+08   15207.0   \n",
              "609   5.758780e+09  1.000742      0.000459    6.304919e+08   14607.0   \n",
              "610   5.766067e+09  1.000365      0.000450    5.689566e+08   14456.0   \n",
              "611   5.773414e+09  1.000689      0.000450    5.679549e+08   18065.0   \n",
              "612   5.787862e+09  1.000657      0.000449    9.076590e+08   13360.0   \n",
              "613   5.777064e+09  1.000903      0.000449    8.650995e+08   11717.0   \n",
              "614   5.777759e+09  1.000756      0.000445    4.856078e+08   10807.0   \n",
              "615   5.781618e+09  1.000851      0.000439    4.802745e+08   10909.0   \n",
              "616   5.816988e+09  1.000913      0.000437    4.808408e+08    9360.0   \n",
              "\n",
              "          ROI  EIP  Day  EIP_Day  \n",
              "583  0.000226    0  -11        0  \n",
              "584 -0.000375    0  -10        0  \n",
              "585  0.000394    0   -9        0  \n",
              "586 -0.000215    0   -8        0  \n",
              "587 -0.000192    0   -7        0  \n",
              "588  0.000011    0   -6        0  \n",
              "589  0.000153    0   -5        0  \n",
              "590  0.000132    0   -4        0  \n",
              "591  0.000214    0   -3        0  \n",
              "592 -0.000956    0   -2        0  \n",
              "593 -0.000094    0   -1        0  \n",
              "595  0.000005    1    1        1  \n",
              "596  0.000442    1    2        2  \n",
              "597 -0.000018    1    3        3  \n",
              "598 -0.000781    1    4        4  \n",
              "599  0.000406    1    5        5  \n",
              "600 -0.000227    1    6        6  \n",
              "601  0.000194    1    7        7  \n",
              "602  0.001131    1    8        8  \n",
              "603 -0.000786    1    9        9  \n",
              "604 -0.000142    1   10       10  \n",
              "605 -0.000657    1   11       11  \n",
              "606  0.000293    1   12       12  \n",
              "607 -0.000347    1   13       13  \n",
              "608  0.000638    1   14       14  \n",
              "609 -0.000139    1   15       15  \n",
              "610 -0.000377    1   16       16  \n",
              "611  0.000324    1   17       17  \n",
              "612 -0.000032    1   18       18  \n",
              "613  0.000245    1   19       19  \n",
              "614 -0.000146    1   20       20  \n",
              "615  0.000095    1   21       21  \n",
              "616  0.000062    1   22       22  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a7c88b21-a6b6-43c0-a178-5d6cc82e51bb\" class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>val</th>\n",
              "      <th>date</th>\n",
              "      <th>n</th>\n",
              "      <th>SMA5</th>\n",
              "      <th>SMA30</th>\n",
              "      <th>EMA</th>\n",
              "      <th>CapMrktCurUSD</th>\n",
              "      <th>PriceUSD</th>\n",
              "      <th>VtyDayRet30d</th>\n",
              "      <th>TxTfrValAdjUSD</th>\n",
              "      <th>TxTfrCnt</th>\n",
              "      <th>ROI</th>\n",
              "      <th>EIP</th>\n",
              "      <th>Day</th>\n",
              "      <th>EIP_Day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>583</th>\n",
              "      <td>263.919633</td>\n",
              "      <td>2021-07-25</td>\n",
              "      <td>621</td>\n",
              "      <td>512.549400</td>\n",
              "      <td>556.707983</td>\n",
              "      <td>493.415478</td>\n",
              "      <td>5.308923e+09</td>\n",
              "      <td>1.001319</td>\n",
              "      <td>0.000314</td>\n",
              "      <td>5.046705e+08</td>\n",
              "      <td>12748.0</td>\n",
              "      <td>0.000226</td>\n",
              "      <td>0</td>\n",
              "      <td>-11</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>584</th>\n",
              "      <td>1234.694750</td>\n",
              "      <td>2021-07-26</td>\n",
              "      <td>622</td>\n",
              "      <td>574.312834</td>\n",
              "      <td>540.473542</td>\n",
              "      <td>567.543405</td>\n",
              "      <td>5.314280e+09</td>\n",
              "      <td>1.000943</td>\n",
              "      <td>0.000319</td>\n",
              "      <td>9.177476e+08</td>\n",
              "      <td>22342.0</td>\n",
              "      <td>-0.000375</td>\n",
              "      <td>0</td>\n",
              "      <td>-10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>585</th>\n",
              "      <td>912.870553</td>\n",
              "      <td>2021-07-27</td>\n",
              "      <td>623</td>\n",
              "      <td>644.790031</td>\n",
              "      <td>544.266912</td>\n",
              "      <td>602.076120</td>\n",
              "      <td>5.323567e+09</td>\n",
              "      <td>1.001337</td>\n",
              "      <td>0.000322</td>\n",
              "      <td>7.710389e+08</td>\n",
              "      <td>16751.0</td>\n",
              "      <td>0.000394</td>\n",
              "      <td>0</td>\n",
              "      <td>-9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>586</th>\n",
              "      <td>138.537347</td>\n",
              "      <td>2021-07-28</td>\n",
              "      <td>624</td>\n",
              "      <td>558.593675</td>\n",
              "      <td>510.113386</td>\n",
              "      <td>555.722243</td>\n",
              "      <td>5.358583e+09</td>\n",
              "      <td>1.001122</td>\n",
              "      <td>0.000298</td>\n",
              "      <td>2.857868e+09</td>\n",
              "      <td>14350.0</td>\n",
              "      <td>-0.000215</td>\n",
              "      <td>0</td>\n",
              "      <td>-8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>587</th>\n",
              "      <td>67.027245</td>\n",
              "      <td>2021-07-29</td>\n",
              "      <td>625</td>\n",
              "      <td>523.409905</td>\n",
              "      <td>502.839147</td>\n",
              "      <td>506.852743</td>\n",
              "      <td>5.352118e+09</td>\n",
              "      <td>1.000930</td>\n",
              "      <td>0.000294</td>\n",
              "      <td>4.021327e+09</td>\n",
              "      <td>14543.0</td>\n",
              "      <td>-0.000192</td>\n",
              "      <td>0</td>\n",
              "      <td>-7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>588</th>\n",
              "      <td>973.908703</td>\n",
              "      <td>2021-07-30</td>\n",
              "      <td>626</td>\n",
              "      <td>665.407719</td>\n",
              "      <td>523.395382</td>\n",
              "      <td>553.558339</td>\n",
              "      <td>5.368017e+09</td>\n",
              "      <td>1.000941</td>\n",
              "      <td>0.000284</td>\n",
              "      <td>6.173390e+08</td>\n",
              "      <td>16100.0</td>\n",
              "      <td>0.000011</td>\n",
              "      <td>0</td>\n",
              "      <td>-6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>589</th>\n",
              "      <td>267.094338</td>\n",
              "      <td>2021-07-31</td>\n",
              "      <td>627</td>\n",
              "      <td>471.887637</td>\n",
              "      <td>487.128556</td>\n",
              "      <td>524.911939</td>\n",
              "      <td>5.388795e+09</td>\n",
              "      <td>1.001094</td>\n",
              "      <td>0.000273</td>\n",
              "      <td>1.082540e+09</td>\n",
              "      <td>14776.0</td>\n",
              "      <td>0.000153</td>\n",
              "      <td>0</td>\n",
              "      <td>-5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>590</th>\n",
              "      <td>1033.923589</td>\n",
              "      <td>2021-08-01</td>\n",
              "      <td>628</td>\n",
              "      <td>496.098244</td>\n",
              "      <td>500.120842</td>\n",
              "      <td>575.813104</td>\n",
              "      <td>5.411745e+09</td>\n",
              "      <td>1.001226</td>\n",
              "      <td>0.000263</td>\n",
              "      <td>6.880336e+08</td>\n",
              "      <td>15512.0</td>\n",
              "      <td>0.000132</td>\n",
              "      <td>0</td>\n",
              "      <td>-4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>591</th>\n",
              "      <td>952.977654</td>\n",
              "      <td>2021-08-02</td>\n",
              "      <td>629</td>\n",
              "      <td>658.986306</td>\n",
              "      <td>502.669161</td>\n",
              "      <td>613.529559</td>\n",
              "      <td>5.428476e+09</td>\n",
              "      <td>1.001440</td>\n",
              "      <td>0.000265</td>\n",
              "      <td>6.249126e+08</td>\n",
              "      <td>16218.0</td>\n",
              "      <td>0.000214</td>\n",
              "      <td>0</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>592</th>\n",
              "      <td>185.009025</td>\n",
              "      <td>2021-08-03</td>\n",
              "      <td>630</td>\n",
              "      <td>682.582662</td>\n",
              "      <td>502.111057</td>\n",
              "      <td>570.677505</td>\n",
              "      <td>5.435133e+09</td>\n",
              "      <td>1.000483</td>\n",
              "      <td>0.000319</td>\n",
              "      <td>1.999513e+09</td>\n",
              "      <td>14346.0</td>\n",
              "      <td>-0.000956</td>\n",
              "      <td>0</td>\n",
              "      <td>-2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>593</th>\n",
              "      <td>1156.670281</td>\n",
              "      <td>2021-08-04</td>\n",
              "      <td>631</td>\n",
              "      <td>719.134978</td>\n",
              "      <td>534.456968</td>\n",
              "      <td>629.276783</td>\n",
              "      <td>5.443340e+09</td>\n",
              "      <td>1.000389</td>\n",
              "      <td>0.000318</td>\n",
              "      <td>6.848119e+08</td>\n",
              "      <td>17015.0</td>\n",
              "      <td>-0.000094</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>531.584877</td>\n",
              "      <td>2021-08-06</td>\n",
              "      <td>633</td>\n",
              "      <td>743.171429</td>\n",
              "      <td>568.617451</td>\n",
              "      <td>642.938060</td>\n",
              "      <td>5.464014e+09</td>\n",
              "      <td>1.000738</td>\n",
              "      <td>0.000323</td>\n",
              "      <td>6.883365e+08</td>\n",
              "      <td>13933.0</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>1078.851719</td>\n",
              "      <td>2021-08-07</td>\n",
              "      <td>634</td>\n",
              "      <td>768.346242</td>\n",
              "      <td>573.832273</td>\n",
              "      <td>686.529426</td>\n",
              "      <td>5.509692e+09</td>\n",
              "      <td>1.001180</td>\n",
              "      <td>0.000332</td>\n",
              "      <td>6.626645e+08</td>\n",
              "      <td>15579.0</td>\n",
              "      <td>0.000442</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>796.056496</td>\n",
              "      <td>2021-08-08</td>\n",
              "      <td>635</td>\n",
              "      <td>890.555736</td>\n",
              "      <td>580.137067</td>\n",
              "      <td>697.482133</td>\n",
              "      <td>5.547599e+09</td>\n",
              "      <td>1.001162</td>\n",
              "      <td>0.000331</td>\n",
              "      <td>6.948149e+08</td>\n",
              "      <td>14282.0</td>\n",
              "      <td>-0.000018</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>501.034906</td>\n",
              "      <td>2021-08-09</td>\n",
              "      <td>636</td>\n",
              "      <td>759.428661</td>\n",
              "      <td>584.487971</td>\n",
              "      <td>677.837410</td>\n",
              "      <td>5.535275e+09</td>\n",
              "      <td>1.000380</td>\n",
              "      <td>0.000362</td>\n",
              "      <td>9.105665e+08</td>\n",
              "      <td>15299.0</td>\n",
              "      <td>-0.000781</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>599</th>\n",
              "      <td>410.809648</td>\n",
              "      <td>2021-08-10</td>\n",
              "      <td>637</td>\n",
              "      <td>663.667529</td>\n",
              "      <td>588.024816</td>\n",
              "      <td>651.134634</td>\n",
              "      <td>5.577314e+09</td>\n",
              "      <td>1.000786</td>\n",
              "      <td>0.000369</td>\n",
              "      <td>7.919636e+08</td>\n",
              "      <td>13591.0</td>\n",
              "      <td>0.000406</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>600</th>\n",
              "      <td>902.996699</td>\n",
              "      <td>2021-08-11</td>\n",
              "      <td>638</td>\n",
              "      <td>737.949894</td>\n",
              "      <td>603.230303</td>\n",
              "      <td>676.320840</td>\n",
              "      <td>5.614677e+09</td>\n",
              "      <td>1.000559</td>\n",
              "      <td>0.000372</td>\n",
              "      <td>5.128071e+08</td>\n",
              "      <td>14623.0</td>\n",
              "      <td>-0.000227</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>601</th>\n",
              "      <td>203.342435</td>\n",
              "      <td>2021-08-12</td>\n",
              "      <td>639</td>\n",
              "      <td>562.848037</td>\n",
              "      <td>594.254757</td>\n",
              "      <td>629.023000</td>\n",
              "      <td>5.647036e+09</td>\n",
              "      <td>1.000753</td>\n",
              "      <td>0.000342</td>\n",
              "      <td>1.212189e+09</td>\n",
              "      <td>14246.0</td>\n",
              "      <td>0.000194</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>602</th>\n",
              "      <td>801.352568</td>\n",
              "      <td>2021-08-13</td>\n",
              "      <td>640</td>\n",
              "      <td>563.907251</td>\n",
              "      <td>605.161530</td>\n",
              "      <td>646.255957</td>\n",
              "      <td>5.684582e+09</td>\n",
              "      <td>1.001885</td>\n",
              "      <td>0.000394</td>\n",
              "      <td>4.363608e+08</td>\n",
              "      <td>14741.0</td>\n",
              "      <td>0.001131</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>603</th>\n",
              "      <td>603.155029</td>\n",
              "      <td>2021-08-14</td>\n",
              "      <td>641</td>\n",
              "      <td>584.331276</td>\n",
              "      <td>615.237915</td>\n",
              "      <td>641.945864</td>\n",
              "      <td>5.696500e+09</td>\n",
              "      <td>1.001098</td>\n",
              "      <td>0.000419</td>\n",
              "      <td>5.877929e+08</td>\n",
              "      <td>14542.0</td>\n",
              "      <td>-0.000786</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>604</th>\n",
              "      <td>581.598488</td>\n",
              "      <td>2021-08-15</td>\n",
              "      <td>642</td>\n",
              "      <td>618.489044</td>\n",
              "      <td>625.906359</td>\n",
              "      <td>635.911126</td>\n",
              "      <td>5.726022e+09</td>\n",
              "      <td>1.000955</td>\n",
              "      <td>0.000419</td>\n",
              "      <td>4.197429e+08</td>\n",
              "      <td>12178.0</td>\n",
              "      <td>-0.000142</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>605</th>\n",
              "      <td>503.744471</td>\n",
              "      <td>2021-08-16</td>\n",
              "      <td>643</td>\n",
              "      <td>538.638598</td>\n",
              "      <td>625.407400</td>\n",
              "      <td>622.694461</td>\n",
              "      <td>5.730731e+09</td>\n",
              "      <td>1.000298</td>\n",
              "      <td>0.000436</td>\n",
              "      <td>6.786509e+08</td>\n",
              "      <td>13812.0</td>\n",
              "      <td>-0.000657</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>606</th>\n",
              "      <td>1221.285273</td>\n",
              "      <td>2021-08-17</td>\n",
              "      <td>644</td>\n",
              "      <td>742.227166</td>\n",
              "      <td>652.597253</td>\n",
              "      <td>682.553542</td>\n",
              "      <td>5.712987e+09</td>\n",
              "      <td>1.000591</td>\n",
              "      <td>0.000438</td>\n",
              "      <td>5.677394e+08</td>\n",
              "      <td>16182.0</td>\n",
              "      <td>0.000293</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>607</th>\n",
              "      <td>1162.450201</td>\n",
              "      <td>2021-08-18</td>\n",
              "      <td>645</td>\n",
              "      <td>814.446693</td>\n",
              "      <td>675.705582</td>\n",
              "      <td>730.543208</td>\n",
              "      <td>5.722496e+09</td>\n",
              "      <td>1.000243</td>\n",
              "      <td>0.000442</td>\n",
              "      <td>6.367904e+08</td>\n",
              "      <td>16419.0</td>\n",
              "      <td>-0.000347</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>608</th>\n",
              "      <td>509.197165</td>\n",
              "      <td>2021-08-19</td>\n",
              "      <td>646</td>\n",
              "      <td>795.655120</td>\n",
              "      <td>672.751192</td>\n",
              "      <td>708.408604</td>\n",
              "      <td>5.737403e+09</td>\n",
              "      <td>1.000881</td>\n",
              "      <td>0.000458</td>\n",
              "      <td>7.282589e+08</td>\n",
              "      <td>15207.0</td>\n",
              "      <td>0.000638</td>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>609</th>\n",
              "      <td>789.589854</td>\n",
              "      <td>2021-08-20</td>\n",
              "      <td>647</td>\n",
              "      <td>837.253393</td>\n",
              "      <td>668.208268</td>\n",
              "      <td>716.526729</td>\n",
              "      <td>5.758780e+09</td>\n",
              "      <td>1.000742</td>\n",
              "      <td>0.000459</td>\n",
              "      <td>6.304919e+08</td>\n",
              "      <td>14607.0</td>\n",
              "      <td>-0.000139</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>610</th>\n",
              "      <td>480.666336</td>\n",
              "      <td>2021-08-21</td>\n",
              "      <td>648</td>\n",
              "      <td>832.637766</td>\n",
              "      <td>665.547660</td>\n",
              "      <td>692.940689</td>\n",
              "      <td>5.766067e+09</td>\n",
              "      <td>1.000365</td>\n",
              "      <td>0.000450</td>\n",
              "      <td>5.689566e+08</td>\n",
              "      <td>14456.0</td>\n",
              "      <td>-0.000377</td>\n",
              "      <td>1</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>611</th>\n",
              "      <td>473.312471</td>\n",
              "      <td>2021-08-22</td>\n",
              "      <td>649</td>\n",
              "      <td>683.043205</td>\n",
              "      <td>662.340772</td>\n",
              "      <td>670.977868</td>\n",
              "      <td>5.773414e+09</td>\n",
              "      <td>1.000689</td>\n",
              "      <td>0.000450</td>\n",
              "      <td>5.679549e+08</td>\n",
              "      <td>18065.0</td>\n",
              "      <td>0.000324</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>612</th>\n",
              "      <td>649.492443</td>\n",
              "      <td>2021-08-23</td>\n",
              "      <td>650</td>\n",
              "      <td>580.451654</td>\n",
              "      <td>675.892317</td>\n",
              "      <td>668.829325</td>\n",
              "      <td>5.787862e+09</td>\n",
              "      <td>1.000657</td>\n",
              "      <td>0.000449</td>\n",
              "      <td>9.076590e+08</td>\n",
              "      <td>13360.0</td>\n",
              "      <td>-0.000032</td>\n",
              "      <td>1</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>613</th>\n",
              "      <td>507.287860</td>\n",
              "      <td>2021-08-24</td>\n",
              "      <td>651</td>\n",
              "      <td>580.069793</td>\n",
              "      <td>684.004591</td>\n",
              "      <td>652.675179</td>\n",
              "      <td>5.777064e+09</td>\n",
              "      <td>1.000903</td>\n",
              "      <td>0.000449</td>\n",
              "      <td>8.650995e+08</td>\n",
              "      <td>11717.0</td>\n",
              "      <td>0.000245</td>\n",
              "      <td>1</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>614</th>\n",
              "      <td>602.592957</td>\n",
              "      <td>2021-08-25</td>\n",
              "      <td>652</td>\n",
              "      <td>542.670413</td>\n",
              "      <td>662.934531</td>\n",
              "      <td>647.666956</td>\n",
              "      <td>5.777759e+09</td>\n",
              "      <td>1.000756</td>\n",
              "      <td>0.000445</td>\n",
              "      <td>4.856078e+08</td>\n",
              "      <td>10807.0</td>\n",
              "      <td>-0.000146</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>615</th>\n",
              "      <td>553.327586</td>\n",
              "      <td>2021-08-26</td>\n",
              "      <td>653</td>\n",
              "      <td>557.202663</td>\n",
              "      <td>650.949766</td>\n",
              "      <td>638.233019</td>\n",
              "      <td>5.781618e+09</td>\n",
              "      <td>1.000851</td>\n",
              "      <td>0.000439</td>\n",
              "      <td>4.802745e+08</td>\n",
              "      <td>10909.0</td>\n",
              "      <td>0.000095</td>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>616</th>\n",
              "      <td>509.445841</td>\n",
              "      <td>2021-08-27</td>\n",
              "      <td>654</td>\n",
              "      <td>564.429337</td>\n",
              "      <td>663.313382</td>\n",
              "      <td>625.354301</td>\n",
              "      <td>5.816988e+09</td>\n",
              "      <td>1.000913</td>\n",
              "      <td>0.000437</td>\n",
              "      <td>4.808408e+08</td>\n",
              "      <td>9360.0</td>\n",
              "      <td>0.000062</td>\n",
              "      <td>1</td>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 33,\n  \"fields\": [\n    {\n      \"column\": \"val\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 329.6785422411158,\n        \"min\": 67.02724483215341,\n        \"max\": 1234.6947501152902,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          553.3275859911728,\n          410.8096478329677,\n          480.6663357819809\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2021-07-25 00:00:00\",\n        \"max\": \"2021-08-27 00:00:00\",\n        \"num_unique_values\": 33,\n        \"samples\": [\n          \"2021-08-26 00:00:00\",\n          \"2021-08-10 00:00:00\",\n          \"2021-08-21 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"n\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 621,\n        \"max\": 654,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          653,\n          637,\n          648\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SMA5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 112.80623079333301,\n        \"min\": 471.88763704066895,\n        \"max\": 890.5557361602266,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          557.2026632236576,\n          663.6675291639784,\n          832.637765778366\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SMA30\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 63.952020170115894,\n        \"min\": 487.12855557169394,\n        \"max\": 684.0045910995597,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          650.9497657553217,\n          588.0248163568166,\n          665.5476604534944\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EMA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 60.06017664417494,\n        \"min\": 493.4154779165357,\n        \"max\": 730.5432079545681,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          638.2330193209784,\n          651.1346337547133,\n          692.9406894047506\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CapMrktCurUSD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 173481948.0193615,\n        \"min\": 5308922950.482219,\n        \"max\": 5816988475.91417,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          5781618219.627314,\n          5577313759.768757,\n          5766067056.219728\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PriceUSD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0003617980875569573,\n        \"min\": 1.00024349900643,\n        \"max\": 1.00188470701344,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          1.00085127960257,\n          1.00078579959088,\n          1.00036495406195\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"VtyDayRet30d\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.73056064659712e-05,\n        \"min\": 0.000262603436275,\n        \"max\": 0.0004587248104385,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          0.0004386423811428,\n          0.0003692454640868,\n          0.0004498778176399\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TxTfrValAdjUSD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 734293639.5716162,\n        \"min\": 419742862.1818888,\n        \"max\": 4021327141.306469,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          480274530.538559,\n          791963566.616695,\n          568956576.446152\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TxTfrCnt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2324.0565461161773,\n        \"min\": 9360.0,\n        \"max\": 22342.0,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          10909.0,\n          13591.0,\n          14456.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ROI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00042615091364380734,\n        \"min\": -0.0009559507975973602,\n        \"max\": 0.0011306837020099714,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          9.47861041744602e-05,\n          0.00040578326330265035,\n          -0.00037689317877742656\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EIP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": -11,\n        \"max\": 22,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          21,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EIP_Day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 0,\n        \"max\": 22,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          15,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# mask = np.triu(np.ones_like(Dai.df.corr(), dtype=bool))\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.heatmap(Dai.df.corr(),annot=True, cmap='YlGnBu',vmin=-1, vmax=1)"
      ],
      "metadata": {
        "id": "XcGit4fhrAfg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Dai.regression()"
      ],
      "metadata": {
        "id": "dZGjVv9INGo_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Dai.sensitvity()"
      ],
      "metadata": {
        "id": "NMNo0t4wnQva"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results1 = smf.ols('SMA5 ~ 1+ EIP + Day + EIP_Day', data=df).fit()\n",
        "results2 = smf.ols('SMA5 ~ 1+ EIP + Day + EIP_Day + TxTfrValAdjUSD + TxTfrCnt', data=df).fit()\n",
        "results3 = smf.ols('SMA5 ~ 1+ EIP + Day + EIP_Day +ROI + VtyDayRet30d + TxTfrValAdjUSD + TxTfrCnt', data=df).fit()\n",
        "stargazer = Stargazer([results1,results2,results3])\n",
        "stargazer.title('Decentralization Index')\n",
        "stargazer.significant_digits(3)\n",
        "stargazer.covariate_order(['EIP','Day','EIP_Day','TxTfrValAdjUSD','TxTfrCnt','ROI','VtyDayRet30d','Intercept'])\n",
        "HTML(stargazer.render_html())\n",
        "# print(stargazer.render_latex())"
      ],
      "metadata": {
        "id": "t5feN4z-Cnmj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results1 = smf.ols('SMA30 ~ 1+ EIP + Day + EIP_Day', data=df).fit()\n",
        "results2 = smf.ols('SMA30 ~ 1+ EIP + Day + EIP_Day + TxTfrValAdjUSD + TxTfrCnt', data=df).fit()\n",
        "results3 = smf.ols('SMA30 ~ 1+ EIP + Day + EIP_Day +ROI + VtyDayRet30d + TxTfrValAdjUSD + TxTfrCnt', data=df).fit()\n",
        "stargazer = Stargazer([results1,results2,results3])\n",
        "stargazer.title('Decentralization Index')\n",
        "stargazer.significant_digits(3)\n",
        "stargazer.covariate_order(['EIP','Day','EIP_Day','TxTfrValAdjUSD','TxTfrCnt','ROI','VtyDayRet30d','Intercept'])\n",
        "HTML(stargazer.render_html())\n",
        "# print(stargazer.render_latex())"
      ],
      "metadata": {
        "id": "gnnedzTnx8rP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results1 = smf.ols('EMA ~ 1+ EIP + Day + EIP_Day', data=df).fit()\n",
        "results2 = smf.ols('EMA ~ 1+ EIP + Day + EIP_Day + TxTfrValAdjUSD + TxTfrCnt', data=df).fit()\n",
        "results3 = smf.ols('EMA ~ 1+ EIP + Day + EIP_Day +ROI + VtyDayRet30d + TxTfrValAdjUSD + TxTfrCnt', data=df).fit()\n",
        "stargazer = Stargazer([results1,results2,results3])\n",
        "stargazer.title('Decentralization Index')\n",
        "stargazer.significant_digits(3)\n",
        "stargazer.covariate_order(['EIP','Day','EIP_Day','TxTfrValAdjUSD','TxTfrCnt','ROI','VtyDayRet30d','Intercept'])\n",
        "HTML(stargazer.render_html())\n",
        "# print(stargazer.render_latex())"
      ],
      "metadata": {
        "id": "Ck27LkANyHaL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Aave = Decentralization_Analysis(Aave_ent,aave_met)"
      ],
      "metadata": {
        "id": "_U-qApi-rEMW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Aave.regression()"
      ],
      "metadata": {
        "id": "o8cJbG7sOQMb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Comp = Decentralization_Analysis(Comp_ent,comp_met)\n",
        "Comp.regression()"
      ],
      "metadata": {
        "id": "8hZgKRiqOUpN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Usdt = Decentralization_Analysis(Usdt_ent,usdt_met)\n",
        "Usdt.regression()"
      ],
      "metadata": {
        "id": "_cUOGiIhPErx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## RDD on Adoption Rate (Robustness Check)"
      ],
      "metadata": {
        "id": "MFLaP-r-dSH1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datetime import date, timedelta, datetime\n",
        "import pytz"
      ],
      "metadata": {
        "id": "JVkK2hyJl7dc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class EIP_Analysis:\n",
        "\n",
        "\n",
        "  def __init__(self, token, metrics, adoption, timestamps):\n",
        "    self.reset()\n",
        "    self.token = token.copy()\n",
        "    self.metrics = metrics.copy()\n",
        "    self.adoption = adoption.copy()\n",
        "    self.timestamps = timestamps.copy()\n",
        "    self.result = None\n",
        "    self.token['date'].replace(to_replace= r'\\s00:00:00', value='', regex=True, inplace=True)\n",
        "\n",
        "    ent_exp = pd.DataFrame()\n",
        "    ent_exp['val'] = self.token['val']\n",
        "    ent_exp['date'] = self.token['date']\n",
        "    ent_exp['n'] = range(1,ent_exp.shape[0]+1)\n",
        "    ent_exp['SMA5'] =  ent_exp.val.rolling(5, min_periods=1).mean()\n",
        "    ent_exp['SMA30'] =  ent_exp.val.rolling(30, min_periods=1).mean()\n",
        "    # ent_exp['SMA60'] =  ent_exp.val.rolling(60, min_periods=1).mean()\n",
        "    # ent_exp['SMA90'] =  ent_exp.val.rolling(90, min_periods=1).mean()\n",
        "    # ent_exp['SMA180'] =  ent_exp.val.rolling(180, min_periods=1).mean()\n",
        "    ent_exp['EMA'] = ent_exp['val'].ewm(alpha=0.1, adjust=False).mean()\n",
        "    # ent_exp['EMA0.3'] = ent_exp['val'].ewm(alpha=0.3, adjust=False).mean()\n",
        "\n",
        "\n",
        "\n",
        "    columns =['time','CapMrktCurUSD', 'PriceUSD', 'VtyDayRet30d', 'TxTfrValAdjUSD',\n",
        "                            'TxTfrCnt']\n",
        "    metrics1 = self.metrics[columns]\n",
        "    metrics1.time=pd.to_datetime(metrics1.time)\n",
        "    ent_exp.date=pd.to_datetime(ent_exp.date)\n",
        "    metrics1.rename(columns={'time':'date'},inplace=True)\n",
        "    metrics1.dropna(inplace=True)\n",
        "    df = ent_exp.merge(metrics1,on='date')\n",
        "\n",
        "\n",
        "\n",
        "    df.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
        "    df.dropna(inplace=True)\n",
        "    # subset=['date','val','CapMrktCurUSD', 'PriceUSD', 'VtyDayRet30d', 'TxTfrValAdjUSD',\n",
        "    #                         'TxTfrCnt', 'BTC_PriceUSD', 'BTC_VtyDayRet30d', 'BTC_TxTfrValAdjUSD',\n",
        "    #                         'BTC_TxTfrCnt', 'ETH_PriceUSD', 'ETH_VtyDayRet30d']\n",
        "    df['ROI'] = df['PriceUSD'].pct_change(1)\n",
        "\n",
        "    df['EIP'] = (df['date'] > '2021-08-05') + 0\n",
        "    df['date'].apply(pd.to_datetime)\n",
        "    df['Day'] = (df['date'] - datetime(2021,8,5)).dt.days\n",
        "    df['EIP_Day'] = df['EIP']*df['Day']\n",
        "    df = df[df['date'] != '2021-08-05']\n",
        "\n",
        "    df = df[df['date']>='2021-07-25']\n",
        "    df = df[df['date']<='2021-08-27']\n",
        "\n",
        "    adoption_all = pd.merge(self.timestamps,self.adoption, on='block_number')\n",
        "    adoption_all.drop(columns = ['Unnamed: 0','block_number','lag_timestamp','block_time','all_prcount','legacy_gpcount'],inplace=True)\n",
        "    tz = pytz.timezone('UTC')\n",
        "    adoption_all['datetime'] = adoption_all['timestamp'].apply(lambda x: datetime.fromtimestamp(x,tz))\n",
        "    adoption_r = adoption_all.resample('D', on='datetime').sum().reset_index()\n",
        "    adoption_r['AdoptionRate'] = adoption_r['all_mfcount']/adoption_r['all_gpcount']\n",
        "    adoption_rate = adoption_r[['AdoptionRate']]\n",
        "    df1 = pd.concat([df.reset_index(),adoption_rate],axis=1)\n",
        "\n",
        "    df1.set_index('index',inplace=True)\n",
        "\n",
        "    self.viz = df1.copy()\n",
        "\n",
        "\n",
        "    # df = df[(df['date']<='2021-08-05') | (df['date']>='2021-08-16')]\n",
        "    self.df = df1.copy()\n",
        "\n",
        "\n",
        "  def regression(self):\n",
        "      results1 = smf.ols('SMA30 ~ 1+ EIP + AdoptionRate', data=self.df).fit()\n",
        "      results2 = smf.ols('SMA30 ~ 1+ EIP + AdoptionRate + TxTfrValAdjUSD + TxTfrCnt', data=self.df).fit()\n",
        "      results3 = smf.ols('SMA30 ~ 1+ EIP + AdoptionRate + ROI + VtyDayRet30d + TxTfrValAdjUSD + TxTfrCnt', data=self.df).fit()\n",
        "      stargazer = Stargazer([results1,results2,results3])\n",
        "      stargazer.title('EIP1559 Regression Discontinuity')\n",
        "      stargazer.significant_digits(3)\n",
        "      stargazer.covariate_order(['EIP','AdoptionRate','TxTfrValAdjUSD','TxTfrCnt','ROI','VtyDayRet30d','Intercept'])\n",
        "      display(HTML(stargazer.render_html()))\n",
        "      print(stargazer.render_latex())\n",
        "      results1 = smf.ols('EMA ~ 1+ EIP + AdoptionRate', data=self.df).fit()\n",
        "      results2 = smf.ols('EMA ~ 1+ EIP + AdoptionRate + TxTfrValAdjUSD + TxTfrCnt', data=self.df).fit()\n",
        "      results3 = smf.ols('EMA ~ 1+ EIP + AdoptionRate + ROI + VtyDayRet30d + TxTfrValAdjUSD + TxTfrCnt', data=self.df).fit()\n",
        "      stargazer = Stargazer([results1,results2,results3])\n",
        "      stargazer.title('EIP1559 Regression Discontinuity')\n",
        "      stargazer.significant_digits(3)\n",
        "      stargazer.covariate_order(['EIP','AdoptionRate','TxTfrValAdjUSD','TxTfrCnt','ROI','VtyDayRet30d','Intercept'])\n",
        "      display(HTML(stargazer.render_html()))\n",
        "      print(stargazer.render_latex())\n",
        "\n",
        "  def reset(self):\n",
        "      self.token = None\n",
        "      self.metrics = None\n",
        "      self.df = None\n",
        "      self.tvl = None\n"
      ],
      "metadata": {
        "id": "MINpwXkenKDW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Dai_adoption = EIP_Analysis(Dai_ent,dai_met,adoption,timestamps)"
      ],
      "metadata": {
        "id": "phIuFTvBqAfP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Dai_adoption.regression()"
      ],
      "metadata": {
        "id": "hmbQLORtqlEM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualizations\n"
      ],
      "metadata": {
        "id": "pxAGzA4yC3gC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://github.com/plotly/orca/releases/download/v1.2.1/orca-1.2.1-x86_64.AppImage -O /usr/local/bin/orca\n",
        "!chmod +x /usr/local/bin/orca\n",
        "!apt-get install xvfb libgtk2.0-0 libgconf-2-4"
      ],
      "metadata": {
        "id": "Mq7FKANKwJ_N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig1 = make_subplots()\n",
        "fig1.add_trace(go.Scatter(x=Dai.viz.date, y=Dai.viz['EMA'],\n",
        "              mode='lines', name='Dai'))\n",
        "\n",
        "fig1.add_shape(\n",
        "              go.layout.Shape(\n",
        "                    type=\"line\",\n",
        "                    xref=\"x\",\n",
        "                    yref=\"y\",\n",
        "                    x0='2021-8-5',\n",
        "                    y0=450,\n",
        "                    x1='2021-8-5',\n",
        "                    y1=800,\n",
        "                    name='London Fork',\n",
        "                    line=dict(color='black', width=3, dash='dot')))\n",
        "fig1.add_annotation(\n",
        "                x='2021-8-5',\n",
        "                y=1.06,\n",
        "                yref='paper',\n",
        "                showarrow=False,\n",
        "                text='London Fork')\n",
        "fig1.write_image('DaiEIP.pdf')"
      ],
      "metadata": {
        "id": "DFdtiMSdC2u7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# RDD Visualizations RDPlot"
      ],
      "metadata": {
        "id": "dnkPRYQclTV7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install rdrobust"
      ],
      "metadata": {
        "id": "ih2YoL-8lXDY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a8d3518b-f18c-4a1d-92dd-3da44b3d1f2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: rdrobust in /usr/local/lib/python3.10/dist-packages (1.2.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from rdrobust) (1.25.2)\n",
            "Requirement already satisfied: pandas>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from rdrobust) (2.2.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from rdrobust) (1.11.4)\n",
            "Requirement already satisfied: scikit-learn>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from rdrobust) (1.2.2)\n",
            "Requirement already satisfied: plotnine in /usr/local/lib/python3.10/dist-packages (from rdrobust) (0.12.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.0.0->rdrobust) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.0.0->rdrobust) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas>=2.0.0->rdrobust) (2024.1)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.2.0->rdrobust) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.2.0->rdrobust) (3.3.0)\n",
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            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6.0->plotnine->rdrobust) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6.0->plotnine->rdrobust) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6.0->plotnine->rdrobust) (4.50.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6.0->plotnine->rdrobust) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6.0->plotnine->rdrobust) (24.0)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6.0->plotnine->rdrobust) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.6.0->plotnine->rdrobust) (3.1.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from patsy>=0.5.1->plotnine->rdrobust) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from rdrobust import rdbwselect, rdrobust, rdplot\n",
        "from plotnine import *"
      ],
      "metadata": {
        "id": "xp3Mv8ZjlhaI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "import plotly.io as pio\n",
        "# ! pip install -U kaleido\n",
        "# ! pip install pingouin\n"
      ],
      "metadata": {
        "id": "oPNjxiVAt4Ht"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://github.com/plotly/orca/releases/download/v1.2.1/orca-1.2.1-x86_64.AppImage -O /usr/local/bin/orca\n",
        "!chmod +x /usr/local/bin/orca\n",
        "!apt-get install xvfb libgtk2.0-0 libgconf-2-4"
      ],
      "metadata": {
        "id": "XEhZ5gBDwb6f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c7760441-8e49-4f70-9bd2-7acd9f1c2851"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Connecting to github.com (github.com)|192.30.255.112|:443... connected.\n",
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            "Length: 51607939 (49M) [application/octet-stream]\n",
            "Saving to: ‘/usr/local/bin/orca’\n",
            "\n",
            "/usr/local/bin/orca 100%[===================>]  49.22M   257MB/s    in 0.2s    \n",
            "\n",
            "2023-07-14 22:43:32 (257 MB/s) - ‘/usr/local/bin/orca’ saved [51607939/51607939]\n",
            "\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
            "  gconf-service gconf-service-backend gconf2-common libdbus-glib-1-2\n",
            "  libfontenc1 libgail-common libgail18 libgtk2.0-bin libgtk2.0-common\n",
            "  libxfont2 libxkbfile1 x11-xkb-utils xfonts-base xfonts-encodings\n",
            "  xfonts-utils xserver-common\n",
            "Suggested packages:\n",
            "  gvfs\n",
            "The following NEW packages will be installed:\n",
            "  gconf-service gconf-service-backend gconf2-common libdbus-glib-1-2\n",
            "  libfontenc1 libgail-common libgail18 libgconf-2-4 libgtk2.0-0 libgtk2.0-bin\n",
            "  libgtk2.0-common libxfont2 libxkbfile1 x11-xkb-utils xfonts-base\n",
            "  xfonts-encodings xfonts-utils xserver-common xvfb\n",
            "0 upgraded, 19 newly installed, 0 to remove and 15 not upgraded.\n",
            "Need to get 10.7 MB of archives.\n",
            "After this operation, 27.2 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu focal/main amd64 libdbus-glib-1-2 amd64 0.110-5fakssync1 [59.1 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu focal/universe amd64 gconf2-common all 3.2.6-6ubuntu1 [698 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu focal/universe amd64 libgconf-2-4 amd64 3.2.6-6ubuntu1 [84.8 kB]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu focal/universe amd64 gconf-service-backend amd64 3.2.6-6ubuntu1 [58.6 kB]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu focal/universe amd64 gconf-service amd64 3.2.6-6ubuntu1 [17.4 kB]\n",
            "Get:6 http://archive.ubuntu.com/ubuntu focal/main amd64 libfontenc1 amd64 1:1.1.4-0ubuntu1 [14.0 kB]\n",
            "Get:7 http://archive.ubuntu.com/ubuntu focal/main amd64 libgtk2.0-common all 2.24.32-4ubuntu4 [126 kB]\n",
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            "Get:9 http://archive.ubuntu.com/ubuntu focal/main amd64 libgail18 amd64 2.24.32-4ubuntu4 [14.7 kB]\n",
            "Get:10 http://archive.ubuntu.com/ubuntu focal/main amd64 libgail-common amd64 2.24.32-4ubuntu4 [116 kB]\n",
            "Get:11 http://archive.ubuntu.com/ubuntu focal/main amd64 libgtk2.0-bin amd64 2.24.32-4ubuntu4 [7,728 B]\n",
            "Get:12 http://archive.ubuntu.com/ubuntu focal/main amd64 libxfont2 amd64 1:2.0.3-1 [91.7 kB]\n",
            "Get:13 http://archive.ubuntu.com/ubuntu focal/main amd64 libxkbfile1 amd64 1:1.1.0-1 [65.3 kB]\n",
            "Get:14 http://archive.ubuntu.com/ubuntu focal/main amd64 x11-xkb-utils amd64 7.7+5 [158 kB]\n",
            "Get:15 http://archive.ubuntu.com/ubuntu focal/main amd64 xfonts-encodings all 1:1.0.5-0ubuntu1 [573 kB]\n",
            "Get:16 http://archive.ubuntu.com/ubuntu focal/main amd64 xfonts-utils amd64 1:7.7+6 [91.5 kB]\n",
            "Get:17 http://archive.ubuntu.com/ubuntu focal/main amd64 xfonts-base all 1:1.0.5 [5,896 kB]\n",
            "Get:18 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 xserver-common all 2:1.20.13-1ubuntu1~20.04.8 [27.2 kB]\n",
            "Get:19 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 xvfb amd64 2:1.20.13-1ubuntu1~20.04.8 [780 kB]\n",
            "Fetched 10.7 MB in 2s (6,379 kB/s)\n",
            "Selecting previously unselected package libdbus-glib-1-2:amd64.\n",
            "(Reading database ... 123105 files and directories currently installed.)\n",
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            "Preparing to unpack .../01-gconf2-common_3.2.6-6ubuntu1_all.deb ...\n",
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            "Preparing to unpack .../17-xserver-common_2%3a1.20.13-1ubuntu1~20.04.8_all.deb ...\n",
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            "Selecting previously unselected package xvfb.\n",
            "Preparing to unpack .../18-xvfb_2%3a1.20.13-1ubuntu1~20.04.8_amd64.deb ...\n",
            "Unpacking xvfb (2:1.20.13-1ubuntu1~20.04.8) ...\n",
            "Setting up libfontenc1:amd64 (1:1.1.4-0ubuntu1) ...\n",
            "Setting up gconf2-common (3.2.6-6ubuntu1) ...\n",
            "\n",
            "Creating config file /etc/gconf/2/path with new version\n",
            "Setting up libdbus-glib-1-2:amd64 (0.110-5fakssync1) ...\n",
            "Setting up xfonts-encodings (1:1.0.5-0ubuntu1) ...\n",
            "Setting up libgtk2.0-common (2.24.32-4ubuntu4) ...\n",
            "Setting up libxkbfile1:amd64 (1:1.1.0-1) ...\n",
            "Setting up libxfont2:amd64 (1:2.0.3-1) ...\n",
            "Setting up libgconf-2-4:amd64 (3.2.6-6ubuntu1) ...\n",
            "Setting up x11-xkb-utils (7.7+5) ...\n",
            "Setting up libgtk2.0-0:amd64 (2.24.32-4ubuntu4) ...\n",
            "Setting up xfonts-utils (1:7.7+6) ...\n",
            "Setting up xfonts-base (1:1.0.5) ...\n",
            "Setting up libgail18:amd64 (2.24.32-4ubuntu4) ...\n",
            "Setting up libgtk2.0-bin (2.24.32-4ubuntu4) ...\n",
            "Setting up xserver-common (2:1.20.13-1ubuntu1~20.04.8) ...\n",
            "Setting up libgail-common:amd64 (2.24.32-4ubuntu4) ...\n",
            "Setting up xvfb (2:1.20.13-1ubuntu1~20.04.8) ...\n",
            "Setting up gconf-service (3.2.6-6ubuntu1) ...\n",
            "Setting up gconf-service-backend (3.2.6-6ubuntu1) ...\n",
            "Processing triggers for fontconfig (2.13.1-2ubuntu3) ...\n",
            "Processing triggers for libc-bin (2.31-0ubuntu9.9) ...\n",
            "Processing triggers for man-db (2.9.1-1) ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Dai = Decentralization_Analysis(Dai_ent,dai_met)\n",
        "Dai_adoption = EIP_Analysis(Dai_ent,dai_met,adoption,timestamps)"
      ],
      "metadata": {
        "id": "Zdp-qUYjlqiO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.scatter(Dai_adoption.df, x='Day',y='AdoptionRate')\n",
        "fig.update_layout(yaxis_title = 'Adoption Rate',xaxis_title = 'Days from EIP-1559',font_family=\"Serif\",margin=go.layout.Margin(\n",
        "        l=0, #left margin\n",
        "        r=0, #right margin\n",
        "        b=0, #bottom margin\n",
        "        t=0  #top margin\n",
        "    ))\n",
        "fig.show()\n",
        "output_file = 'EIP-1559 Adoption.pdf'\n",
        "fig.write_image(output_file)"
      ],
      "metadata": {
        "id": "4yptqAqZt8mJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "c3be4279-b3b1-4069-d641-51a648e8f789"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "source": [
        "Y = Dai.df.SMA30\n",
        "X = Dai.df.Day\n",
        "C = 0"
      ],
      "metadata": {
        "id": "Lr1lsphtl7k-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rdplot(Y, X, C, p=1,nbins=34,title = 'EIP-1559 Discontinuity', x_label = 'Days', y_label = 'Decentralization Index')\n"
      ],
      "metadata": {
        "id": "mB3oOeoZli3W"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "Y = Dai.df2.SMA30\n",
        "X = Dai.df2.Day\n",
        "C = 0\n",
        "rdplot(Y, X, C, p=1,h=(11,22))\n"
      ],
      "metadata": {
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          "height": 881
        },
        "id": "K9opn1qEtiTE",
        "outputId": "1e2349b9-9459-4069-f47f-da899e16f098"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
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            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Call: rdplot\n",
            "Number of Observations:                    65\n",
            "Kernel:                               Uniform\n",
            "Polynomial Order Est. (p):                  1\n",
            "\n",
            "                                Left      Right\n",
            "------------------------------------------------\n",
            "Number of Observations            21         44\n",
            "Number of Effective Obs           11         22\n",
            "Bandwith poly. fit (h)            11         22\n",
            "Number of bins scale               1          1\n",
            "Bins Selected                     70         61\n",
            "Average Bin Length             0.585        nan\n",
            "Median Bin Length              0.721        nan\n",
            "IMSE-optimal bins               11.0       16.0\n",
            "Mimicking Variance bins         70.0       61.0\n",
            "\n",
            "Relative to IMSE-optimal:\n",
            "Implied scale                  6.364      3.812\n",
            "WIMSE variance weight          0.004      0.018\n",
            "WIMSE bias weight              0.996      0.982\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py:3474: RuntimeWarning: Mean of empty slice.\n",
            "/usr/local/lib/python3.10/dist-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y = Dai.df2.SMA30\n",
        "X = Dai.df2.Day\n",
        "C = 0\n",
        "rdplot(Y, X, C, p=1,title = 'EIP-1559 Discontinuity', x_label = 'Days', y_label = 'Decentralization Index')"
      ],
      "metadata": {
        "id": "uiRXBrkRvzLV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "outputId": "0ad53eea-c4c4-480a-ace8-9268c5926952"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Call: rdplot\n",
            "Number of Observations:                    65\n",
            "Kernel:                               Uniform\n",
            "Polynomial Order Est. (p):                  1\n",
            "\n",
            "                                Left      Right\n",
            "------------------------------------------------\n",
            "Number of Observations            21         44\n",
            "Number of Effective Obs           21         44\n",
            "Bandwith poly. fit (h)            21         44\n",
            "Number of bins scale               1          1\n",
            "Bins Selected                     70         61\n",
            "Average Bin Length             0.585        nan\n",
            "Median Bin Length              0.721        nan\n",
            "IMSE-optimal bins               11.0       16.0\n",
            "Mimicking Variance bins         70.0       61.0\n",
            "\n",
            "Relative to IMSE-optimal:\n",
            "Implied scale                  6.364      3.812\n",
            "WIMSE variance weight          0.004      0.018\n",
            "WIMSE bias weight              0.996      0.982\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py:3474: RuntimeWarning: Mean of empty slice.\n",
            "/usr/local/lib/python3.10/dist-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y = Dai.df2.SMA30\n",
        "X = Dai.df2.Day\n",
        "C = 0\n",
        "rdplot(Y, X, C, p=1, h=(5.5,11), title = 'EIP-1559 Discontinuity', x_label = 'Days', y_label = 'Decentralization Index')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "id": "GXp6ctxH261Z",
        "outputId": "89472126-63f4-4b13-fafd-463e0b08a2ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Call: rdplot\n",
            "Number of Observations:                    65\n",
            "Kernel:                               Uniform\n",
            "Polynomial Order Est. (p):                  1\n",
            "\n",
            "                                Left      Right\n",
            "------------------------------------------------\n",
            "Number of Observations            21         44\n",
            "Number of Effective Obs            5         11\n",
            "Bandwith poly. fit (h)           5.5         11\n",
            "Number of bins scale               1          1\n",
            "Bins Selected                     70         61\n",
            "Average Bin Length             0.585        nan\n",
            "Median Bin Length              0.721        nan\n",
            "IMSE-optimal bins               11.0       16.0\n",
            "Mimicking Variance bins         70.0       61.0\n",
            "\n",
            "Relative to IMSE-optimal:\n",
            "Implied scale                  6.364      3.812\n",
            "WIMSE variance weight          0.004      0.018\n",
            "WIMSE bias weight              0.996      0.982\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py:3474: RuntimeWarning: Mean of empty slice.\n",
            "/usr/local/lib/python3.10/dist-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(list(X))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LyxkOU7d2g22",
        "outputId": "bc976884-1907-4056-e472-bed2f6dd5711"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-21, -20, -19, -18, -17, -16, -15, -14, -13, -12, -11, -10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y = Dai.df2.SMA30\n",
        "X = Dai.df2.Day\n",
        "C = 0\n",
        "rdbwselect(y = Y, x = X, c = C, all = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KTLmkmRWryMa",
        "outputId": "65d56baf-8935-4ef1-fc77-8ec80a66a1aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Call: rdbwselect\n",
            "Number of Observations:                    65\n",
            "Polynomial Order Est. (p):                  1\n",
            "Polynomial Order Bias (q):                  2\n",
            "Kernel:                            Triangular\n",
            "Bandwidth Selection:                      All\n",
            "Var-Cov Estimator:                         NN\n",
            "\n",
            "          h (left)  h (right)  b (left)  b (right)\n",
            "mserd        4.604      4.604     8.996      8.996\n",
            "msetwo       4.588      5.990     8.977     10.301\n",
            "msesum       4.774      4.774     9.197      9.197\n",
            "msecomb1     4.604      4.604     8.996      8.996\n",
            "msecomb2     4.604      4.774     8.996      9.197\n",
            "cerrd        3.737      3.737     8.996      8.996\n",
            "certwo       3.724      4.862     8.977     10.301\n",
            "cersum       3.875      3.875     9.197      9.197\n",
            "cercomb1     3.737      3.737     8.996      8.996\n",
            "cercomb2     3.737      3.875     8.996      9.197\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rdplot(Y, X, C, p=1, h=(4.5,7.6))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "id": "BiG0uxa9sZBk",
        "outputId": "5c4be2a4-3866-47da-9e85-fdd6ecab706b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Call: rdplot\n",
            "Number of Observations:                    66\n",
            "Kernel:                               Uniform\n",
            "Polynomial Order Est. (p):                  1\n",
            "\n",
            "                                Left      Right\n",
            "------------------------------------------------\n",
            "Number of Observations            21         45\n",
            "Number of Effective Obs            4          8\n",
            "Bandwith poly. fit (h)           4.5        7.6\n",
            "Number of bins scale               1          1\n",
            "Bins Selected                     71         68\n",
            "Average Bin Length             0.535        nan\n",
            "Median Bin Length              0.647        nan\n",
            "IMSE-optimal bins               11.0       16.0\n",
            "Mimicking Variance bins         71.0       68.0\n",
            "\n",
            "Relative to IMSE-optimal:\n",
            "Implied scale                  6.455       4.25\n",
            "WIMSE variance weight          0.004      0.013\n",
            "WIMSE bias weight              0.996      0.987\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py:3474: RuntimeWarning: Mean of empty slice.\n",
            "/usr/local/lib/python3.10/dist-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    }
  ]
}