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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/DecentralizationIndex/blob/main/Decentralization_Classes.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Prelim"
      ],
      "metadata": {
        "id": "EY84CIZrd6DF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install stargazer\n",
        "!pip install pingouin\n",
        "!pip install statsmodels"
      ],
      "metadata": {
        "id": "iT7fCMDwgAx8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NkfjSfaGdcbO"
      },
      "outputs": [],
      "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",
        "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 re\n",
        "\n",
        "from stargazer.stargazer import Stargazer\n",
        "import pingouin\n",
        "import IPython.core.display\n",
        "import requests\n",
        "import json \n",
        "from datetime import datetime\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.core.display import HTML\n",
        "import statsmodels.api as sm\n",
        "\n",
        "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
        "from statsmodels.tsa.stattools import adfuller\n",
        "from statsmodels.tsa.seasonal import seasonal_decompose\n",
        "import seaborn as sns\n",
        "from math import sqrt\n",
        "from statsmodels.tsa.stattools import grangercausalitytests\n",
        "from statsmodels.tsa.api import VAR\n",
        "from sklearn.decomposition import PCA\n",
        "from statsmodels.tsa.ar_model import AutoReg"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Connect to Google Cloud\n",
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "print('Authenticated')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uf4CWLQMmTq3",
        "outputId": "cacb4f3b-71ee-4460-9b0a-d4a6b02e4912"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Authenticated\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Connect to Google BigQuery\n",
        "PROJECT_ID = 'trans-invention-305714'\n",
        "\n",
        "from google.cloud import bigquery\n",
        "client = bigquery.Client(project=PROJECT_ID, location='US')\n",
        "dataset_ref = client.dataset(\"crypto_ethereum\", project=\"bigquery-public-data\")\n",
        "dataset = client.get_dataset(dataset_ref)\n",
        "tables = list(client.list_tables(dataset))\n",
        "\n",
        "# Print names of all tables in the dataset\n",
        "for table in tables:  \n",
        "  print(table.table_id)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U2G1c_zdlrVX",
        "outputId": "0e754d90-2536-47e3-fac5-e66284b535c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "amended_tokens\n",
            "balances\n",
            "blocks\n",
            "contracts\n",
            "logs\n",
            "sessions\n",
            "token_transfers\n",
            "tokens\n",
            "traces\n",
            "transactions\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Class for Querying and Processing Decentralization Index"
      ],
      "metadata": {
        "id": "KfQtqR3aeFCL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Decentralization:\n",
        "  \"\"\"\n",
        "      class Decentralization:\n",
        "              def __init__(self, start, end, address):\n",
        "      \n",
        "\n",
        "              :param start: the start date of querying\n",
        "              :type token: datetime.date object\n",
        "              :param end: the end date of querying\n",
        "              :type metrics: datetime.date\n",
        "              :param address: address of ERC-20 token \n",
        "              :type address: string\n",
        "              \n",
        "    \n",
        "      Public Functions:\n",
        "          - ``program``: returns pandas dataframe with date and decentralization index value\n",
        "          \n",
        "    \n",
        "  \"\"\"\n",
        "  def __init__(self, start, end, address):\n",
        "    self.start = start\n",
        "    self.end = end\n",
        "    self.address = address\n",
        "\n",
        "  #Query and Caculation Program\n",
        "  def program(self):\n",
        "      duration= pd.date_range(start=self.start, end=self.end)\n",
        "      days = np.size(duration)\n",
        "      Entropy= pd.DataFrame(np.zeros(days), columns=['val'])\n",
        "      Entropy['date'] = duration\n",
        "      \n",
        "      for i in range(0, days):\n",
        "        start_date = self.start + timedelta(days=i)\n",
        "        end_date = start_date + timedelta(days=1)\n",
        "        sql = \"\"\"\n",
        "        SELECT token_address, from_address, to_address,block_timestamp, cast(value AS NUMERIC) as value FROM \n",
        "        `bigquery-public-data.crypto_ethereum.token_transfers` \n",
        "        WHERE \n",
        "        token_address = '\"\"\" + self.address + \"\"\"' AND cast(value AS NUMERIC) <> 0\n",
        "        AND\n",
        "          block_timestamp >= TIMESTAMP('\"\"\" + str(start_date) + \"\"\" 00:00:00+00')\n",
        "        AND \n",
        "          block_timestamp < TIMESTAMP('\"\"\" + str(end_date) + \"\"\" 00:00:00+00')\n",
        "        \"\"\"\n",
        "        df = client.query(sql).to_dataframe(progress_bar_type='tqdm_notebook')\n",
        "        Ent = self.Processing_tranvol(df)\n",
        "        Entropy.loc[i,'val'] = Ent\n",
        "\n",
        "      return Entropy\n",
        "\n",
        "  #Processing for or Entropy (regardless of sender/receiver)\n",
        "  def Processing_tranvol(self,df):\n",
        "      df.rename(columns={'f0_':'value','from_address':'from','to_address':'to'}, inplace = True)\n",
        "      df['value'] = df['value'].astype(float)\n",
        "      df = df.dropna()\n",
        "      E = self.index_1(df)\n",
        "      return E\n",
        "\n",
        "  #Transaction Entropy\n",
        "  def index_1(self,df): \n",
        "    df['pr'] = df['value']/df['value'].sum() \n",
        "    H = -df['pr']*np.log2(df['pr'])\n",
        "    E = H.sum()\n",
        "    V = 2**E\n",
        "    return V"
      ],
      "metadata": {
        "id": "q0DaHgR0ebeU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Example Usage"
      ],
      "metadata": {
        "id": "cEZv9Oexl563"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "start = date(2021,7,28)\n",
        "end = date(2021,7,31)"
      ],
      "metadata": {
        "id": "qrZx4JSbf8pn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "UNI = '0x1f9840a85d5af5bf1d1762f925bdaddc4201f984'"
      ],
      "metadata": {
        "id": "-49EayVal5K-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Uni = Decentralization(start = start, end = end, address = UNI)\n",
        "df_uni = Uni.program()"
      ],
      "metadata": {
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        "id": "yHHFHemKobj-",
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              "          val       date\n",
              "0  174.786472 2021-07-28\n",
              "1  274.551509 2021-07-29\n",
              "2  579.624903 2021-07-30\n",
              "3  394.990031 2021-07-31"
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          },
          "metadata": {},
          "execution_count": 63
        }
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    },
    {
      "cell_type": "markdown",
      "source": [
        "#Class for Econometrics Analysis"
      ],
      "metadata": {
        "id": "GJTMFLrFeQOz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Decentralization_Analysis:\n",
        "  \"\"\"\n",
        "    class Decentralization_Analysis:\n",
        "            def __init__(self, token, metrics, BTC, ETH):\n",
        "    \n",
        "\n",
        "            :param token: the dataframe containing shannon entropy values for daily transactions\n",
        "            :type token: pandas.DataFrame\n",
        "            :param metrics: all market metrics of token inputed retrieved from CoinMetrics\n",
        "            :type metrics: pandas.DataFrame\n",
        "            :param BTC: all market metrics of Bitcoin retrieved from CoinMetrics \n",
        "            :type BTC: pandas.DataFrame\n",
        "            :param ETH: all market metrics of Ethereum retrieved from CoinMetrics \n",
        "            :type ETH: pandas.DataFrame\n",
        "            \n",
        "  \n",
        "    Public Functions:\n",
        "        - ``autor``: returns autoregression of our dependent variable: decentralization index.\n",
        "        - ``check_stationarity``: return Dickey Fuller test results and autocorrelation figures\n",
        "        - ``heat_map``: returns correlation heatmap of variables\n",
        "        - ``stargazer_reverse_eth``: displays regression results with independent variables: \n",
        "                Ether market variables; dependent variable: decentralization index\n",
        "        - ``stargazer_reverse_btc``: displays regression results with independent variables: \n",
        "                Bitcoin market variables; dependent variable: decentralization index\n",
        "   \n",
        "    \"\"\"\n",
        "\n",
        "  def __init__(self, token, metrics, BTC, ETH):\n",
        "    self.reset()\n",
        "    self.token = token.copy()\n",
        "    self.metrics = metrics.copy()\n",
        "    self.btc = BTC.copy()\n",
        "    self.eth = ETH.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'] = np.exp2(self.token['val'])\n",
        "    ent_exp['date'] = self.token['date']\n",
        "    ent_exp['n'] = range(1,ent_exp.shape[0]+1)\n",
        "\n",
        "    ent_exp['val_pct30'] = ent_exp['val'].pct_change(30)\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",
        "    columns2 =['time', 'PriceUSD', 'VtyDayRet30d', 'TxTfrValAdjUSD', \n",
        "                            'TxTfrCnt']\n",
        "\n",
        "    btc1 = self.btc[columns2]\n",
        "    btc1.time=pd.to_datetime(btc1.time)\n",
        "    btc1.rename(columns={'time':'date','PriceUSD':'BTC_PriceUSD','VtyDayRet30d':'BTC_VtyDayRet30d',\n",
        "                         'TxTfrValAdjUSD':'BTC_TxTfrValAdjUSD','TxTfrCnt':'BTC_TxTfrCnt'},inplace=True)\n",
        "    btc1.dropna(inplace=True)\n",
        "    df = df.merge(btc1,on='date')\n",
        "    \n",
        "    eth1 = self.eth[columns2]\n",
        "    eth1.time=pd.to_datetime(eth1.time)\n",
        "    eth1.rename(columns={'time':'date','PriceUSD':'ETH_PriceUSD','VtyDayRet30d':'ETH_VtyDayRet30d',\n",
        "                         'TxTfrValAdjUSD':'ETH_TxTfrValAdjUSD','TxTfrCnt':'ETH_TxTfrCnt'},inplace=True)\n",
        "    eth1.dropna(inplace=True)\n",
        "    df = df.merge(eth1,on='date')\n",
        "\n",
        "    df['BTC_VtyDayRet30d'] = df['BTC_VtyDayRet30d'].shift(30)\n",
        "    df['ETH_VtyDayRet30d'] = df['ETH_VtyDayRet30d'].shift(30)\n",
        "\n",
        "\n",
        "    df['BTC_Ret'] = df['BTC_PriceUSD'].pct_change(1).shift(1)\n",
        "    df['ETH_Ret'] = df['ETH_PriceUSD'].pct_change(1).shift(1)\n",
        "    df['BTC_Ret7'] = df['BTC_PriceUSD'].pct_change(7).shift(1)\n",
        "    df['ETH_Ret7'] = df['ETH_PriceUSD'].pct_change(7).shift(1)\n",
        "    df['BTC_Ret14'] = df['BTC_PriceUSD'].pct_change(14).shift(1)\n",
        "    df['ETH_Ret14'] = df['ETH_PriceUSD'].pct_change(14).shift(1)\n",
        "    df['BTC_Ret21'] = df['BTC_PriceUSD'].pct_change(21).shift(1)\n",
        "    df['ETH_Ret21'] = df['ETH_PriceUSD'].pct_change(21).shift(1)\n",
        "    df['BTC_Ret30'] = df['BTC_PriceUSD'].pct_change(30).shift(1)\n",
        "    df['ETH_Ret30'] = df['ETH_PriceUSD'].pct_change(30).shift(1)\n",
        "\n",
        "    \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",
        "    x=df[['ETH_Ret','ETH_Ret7','ETH_Ret14','ETH_Ret21','ETH_Ret30','ETH_VtyDayRet30d']]\n",
        "    x2=df[['BTC_Ret','BTC_Ret7','BTC_Ret14','BTC_Ret21','BTC_Ret30','BTC_VtyDayRet30d']]\n",
        "    pca = PCA(n_components=1)\n",
        "    df['ETH_PC'] = pca.fit_transform(x)\n",
        "    df['BTC_PC'] = pca.fit_transform(x2)\n",
        "    self.df = df.copy()\n",
        "\n",
        "  def autor(self):\n",
        "    variables = ['val']\n",
        "    ar_model = AutoReg(self.df.val, lags=5).fit()\n",
        "    print(ar_model.summary())\n",
        " \n",
        "  def check_stationarity(self, lags_plots=48, figsize=(22,8)):\n",
        "    \"Use Series as parameter\"\n",
        "    variables = ['val']\n",
        "    variables.extend(['ETH_Ret','ETH_Ret7','ETH_Ret14','ETH_Ret21','ETH_Ret30','ETH_VtyDayRet30d','ETH_PC',\n",
        "                    'BTC_Ret','BTC_Ret7','BTC_Ret14','BTC_Ret21','BTC_Ret30','BTC_VtyDayRet30d','BTC_PC'])\n",
        "    significant = []\n",
        "    fig = plot_pacf(self.df['val'].dropna(), lags=lags_plots, zero=False, method='ols', color='teal',title='Partial Autocorrelation');\n",
        "    fig.savefig('PAC.pdf') \n",
        "    for y in variables:\n",
        "      select = self.df[y][self.df[y]!=0]   ##Some rates data on DeFi pulse is missing have '0' value\n",
        "      if select.empty:\n",
        "        continue\n",
        "      col = pd.Series(select.dropna())\n",
        "      # fig = plt.figure()\n",
        "\n",
        "      # ax1 = plt.subplot2grid((3, 3), (0, 0), colspan=2)\n",
        "      # ax2 = plt.subplot2grid((3, 3), (1, 0))\n",
        "      # ax3 = plt.subplot2grid((3, 3), (1, 1))\n",
        "      # ax4 = plt.subplot2grid((3, 3), (2, 0), colspan=2)\n",
        "      # col.plot(ax=ax1, figsize=figsize, color='teal')\n",
        "      # ax1.set_title(y)\n",
        "      # plot_acf(col, lags=lags_plots, zero=False, ax=ax2, color='teal');\n",
        "      # fig = plot_pacf(col, lags=lags_plots, zero=False, method='ols', color='teal',title=y);\n",
        "      # # sns.distplot(col, bins=int(sqrt(len(col))), ax=ax4, color='teal')\n",
        "      # fig.set_title('Partial Autocorrelation')\n",
        "\n",
        "      # plt.tight_layout()\n",
        "      \n",
        "      print('Dickey-Fuller test results:')\n",
        "      adfinput = adfuller(col)\n",
        "      adftest = pd.Series(adfinput[0:4], index=['Statistical Test','P-Value','Used Lags','Observations Number'])\n",
        "      if adftest.loc['P-Value'] < 0.05:\n",
        "        significant.append(y)\n",
        "      adftest = round(adftest,4)\n",
        "      \n",
        "      for key, value in adfinput[4].items():\n",
        "          adftest[\"Critical Values (%s)\"%key] = value.round(4)  \n",
        "      print(adftest)\n",
        "      fig.show()\n",
        "\n",
        "    print(\"Stationary Variables:\", significant)\n",
        "\n",
        "  def heat_map(self):\n",
        "    corr_columns = ['ETH_Ret','ETH_Ret7','ETH_Ret14','ETH_Ret21','ETH_Ret30','ETH_VtyDayRet30d','ETH_PC',\n",
        "                    'BTC_Ret','BTC_Ret7','BTC_Ret14','BTC_Ret21','BTC_Ret30','BTC_VtyDayRet30d','BTC_PC']\n",
        "    plt.figure(figsize=(12, 6))\n",
        "    \n",
        "    mask = np.triu(np.ones_like(self.df[corr_columns].corr(), dtype=np.bool))\n",
        "    heatmap = sns.heatmap(self.df[corr_columns].corr(), mask=mask, vmin=-1, vmax=1, annot=True, cmap='YlGnBu') \n",
        "    # heatmap.set_title('Triangle Correlation Heatmap: Market Return and Volatility', fontdict={'fontsize':18}, pad=16);\n",
        "    plt.savefig('Return_Heatmap.pdf', bbox_inches=\"tight\", pad_inches=1) \n",
        "\n",
        "  def regression(self, decentralized, economical):\n",
        "      a = self.df[[decentralized,economical]].copy()\n",
        "      # add lag term for dependent variable\n",
        "      a['lag_term'] = a[economical].shift(-1)\n",
        "      a.dropna(inplace=True)\n",
        "      X = a[[decentralized,'lag_term']][a[economical]!=0] \n",
        "      y = a[economical][a[economical]!=0]  ##Some rates data on DeFi pulse is missing have '0' value\n",
        "      if y.empty:\n",
        "        return 1\n",
        "    # add a constant to the dependent variables\n",
        "      X = sm.add_constant(X)\n",
        "    # conduct regression\n",
        "      model = sm.OLS(y, X).fit(cov_type='HAC',cov_kwds={'maxlags':1}) #Newey–West estimator\n",
        "      return model\n",
        "\n",
        "  def regression_rev(self, decentralized, economical):\n",
        "      a = self.df[[decentralized,economical]].copy()\n",
        "      a.dropna(inplace=True)\n",
        "      y = a[decentralized][a[economical]!=0] \n",
        "      X = a[economical][a[economical]!=0]  ##Some rates data on DeFi pulse is missing have '0' value\n",
        "      if y.empty:\n",
        "        return 1\n",
        "    # add a constant to the dependent variables\n",
        "      X = sm.add_constant(X)\n",
        "    # conduct regression\n",
        "      model = sm.OLS(y, X).fit(cov_type='HAC',cov_kwds={'maxlags':1}) #Newey–West estimator\n",
        "      return model\n",
        "\n",
        "  def stargazer_reverse_eth(self):\n",
        "    # variables = list(self.df.columns[8:])\n",
        "    variables =['ETH_Ret','ETH_Ret7','ETH_Ret14','ETH_Ret21','ETH_Ret30','ETH_VtyDayRet30d','ETH_PC']\n",
        "    lis = [\"val\"]\n",
        "    for j in lis:\n",
        "      independent = list()\n",
        "      for i in variables:\n",
        "        i = self.regression_rev (j,i)\n",
        "        independent.append(i)\n",
        "     \n",
        "      \n",
        "      # if 1 in lis:\n",
        "      #   continue\n",
        "      stargazer_result = Stargazer(independent)\n",
        "      stargazer_result.significant_digits(3)\n",
        "      stargazer_result.show_confidence_intervals(False)\n",
        "      covariates = variables.copy()\n",
        "      covariates.append('const')\n",
        "      \n",
        "      stargazer_result.covariate_order(covariates)\n",
        "      file_name = \"ETH.tex\" #Include directory path if needed\n",
        "      tex_file = open(file_name, \"w\" ) #This will overwrite an existing file\n",
        "      tex_file.write( stargazer_result.render_latex() )\n",
        "      tex_file.close()\n",
        "      file = \"ETH.html\" #Include directory path if needed\n",
        "      html_file = open(file, \"w\" ) #This will overwrite an existing file\n",
        "      html_file.write( stargazer_result.render_html() )\n",
        "      html_file.close()\n",
        "      display(HTML(stargazer_result.render_html()))\n",
        "\n",
        "  def stargazer_reverse_btc(self):\n",
        "    # variables = list(self.df.columns[8:])\n",
        "    variables =['BTC_Ret','BTC_Ret7','BTC_Ret14','BTC_Ret21','BTC_Ret30','BTC_VtyDayRet30d','BTC_PC']\n",
        "    lis = [\"val\"]\n",
        "    for j in lis:\n",
        "      independent = list()\n",
        "      for i in variables:\n",
        "        i = self.regression_rev (j,i)\n",
        "        independent.append(i)\n",
        "     \n",
        "      \n",
        "      # if 1 in lis:\n",
        "      #   continue\n",
        "      stargazer_result = Stargazer(independent)\n",
        "      stargazer_result.significant_digits(3)\n",
        "      stargazer_result.show_confidence_intervals(False)\n",
        "      covariates = variables.copy()\n",
        "      covariates.append('const')\n",
        "      \n",
        "      stargazer_result.covariate_order(covariates)\n",
        "      file_name = \"BTC.tex\" #Include directory path if needed\n",
        "      tex_file = open(file_name, \"w\" ) #This will overwrite an existing file\n",
        "      tex_file.write( stargazer_result.render_latex() )\n",
        "      tex_file.close()\n",
        "      file = \"BTC.html\" #Include directory path if needed\n",
        "      html_file = open(file, \"w\" ) #This will overwrite an existing file\n",
        "      html_file.write( stargazer_result.render_html() )\n",
        "      html_file.close()\n",
        "      display(HTML(stargazer_result.render_html()))\n",
        "      \n",
        "  def reset(self):\n",
        "      self.token = None\n",
        "      self.metrics = None\n",
        "      self.df = None\n",
        "      self.tvl = None\n"
      ],
      "metadata": {
        "id": "w2Zw-6pweS56"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}