SAI_DAI_Decentralization_Analysis.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SciEcon/DecentralizationIndex/blob/main/SAI_DAI_Decentralization_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "***All data is from token genesis to 2021-07-31*** \n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "ornmgQIPONew"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ra2orrEVM879"
      },
      "source": [
        "### Summary Statistics of Index: Sai Dai"
      ]
    },
    {
      "cell_type": "code",
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        "id": "OkjcW0i7Pzkb"
      },
      "source": [
        "data_frames = [summary(Sai_ent),summary(Dai_ent),summary(SaiDai_ent)]\n",
        "summary_stats=pd.concat(data_frames,axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "summary_stats"
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      "outputs": [
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              "      <th></th>\n",
              "      <th>Sai</th>\n",
              "      <th>Dai</th>\n",
              "      <th>SaiDai</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1322.000000</td>\n",
              "      <td>622.000000</td>\n",
              "      <td>1322.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>577.553143</td>\n",
              "      <td>1283.635764</td>\n",
              "      <td>1106.985109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>906.623893</td>\n",
              "      <td>1017.673878</td>\n",
              "      <td>1037.766424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>41.748567</td>\n",
              "      <td>20.163122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>12.437335</td>\n",
              "      <td>594.787984</td>\n",
              "      <td>415.021961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>228.744179</td>\n",
              "      <td>1009.878534</td>\n",
              "      <td>794.812506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>736.934132</td>\n",
              "      <td>1645.778444</td>\n",
              "      <td>1468.110612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>6450.849354</td>\n",
              "      <td>6758.890478</td>\n",
              "      <td>6758.890478</td>\n",
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            "text/plain": [
              "               Sai          Dai       SaiDai\n",
              "count  1322.000000   622.000000  1322.000000\n",
              "mean    577.553143  1283.635764  1106.985109\n",
              "std     906.623893  1017.673878  1037.766424\n",
              "min       1.000000    41.748567    20.163122\n",
              "25%      12.437335   594.787984   415.021961\n",
              "50%     228.744179  1009.878534   794.812506\n",
              "75%     736.934132  1645.778444  1468.110612\n",
              "max    6450.849354  6758.890478  6758.890478"
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          "metadata": {},
          "execution_count": 94
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Decentralization Index Creation"
      ],
      "metadata": {
        "id": "gFU9Ifc1OrWZ"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2D_XUZN51Ucl"
      },
      "source": [
        "## Preliminaries"
      ]
    },
    {
      "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 re"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JzSBpCo-1Ucm"
      },
      "source": [
        "#Connect to Google Cloud\n",
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "print('Authenticated')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jjdjdvzo1Ucn",
        "outputId": "004a7392-68f8-4159-9520-93d1b0bed16a"
      },
      "source": [
        "#Connect to Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "print('Authenticated')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "Authenticated\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P96x-mGg1Ucn"
      },
      "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)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rrgoB02FJfFZ"
      },
      "source": [
        "##Query & Caculation Programs"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Our Decentralization Index\n",
        "def index_1(df): \n",
        "  df['pr'] = df['value']/df['value'].sum() \n",
        "  H = -df['pr']*np.log2(df['pr'])\n",
        "  E = H.sum()\n",
        "  I = 2**E\n",
        "  return I"
      ],
      "metadata": {
        "id": "Q9YmlpAoGHkn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ucarDAPFgzQ3"
      },
      "source": [
        "#For Entropy (regardless of sender/receiver)\n",
        "def Processing_tranvol(df,index):\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 = index(df)\n",
        "    return E"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bwkQ5mKwH2T9"
      },
      "source": [
        "#Query and Caculation Program (Entropy)\n",
        "def program(start, end, address, index):\n",
        "  duration= pd.date_range(start=start, end=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 = 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 = '\"\"\" + 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 = Processing_tranvol(df,index)\n",
        "    Entropy.loc[i,'val'] = Ent\n",
        "\n",
        "  return Entropy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XGmfAG3oHUPT"
      },
      "source": [
        "##Get Decentralization Index from CSV with Entropy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K_1LIGtDOc5i"
      },
      "source": [
        "#Our Decentralization Index and Moving Averages\n",
        "def ent_exp2(token):\n",
        "  ent_exp = pd.DataFrame()\n",
        "  ent_exp['val'] = np.exp2(token['val'])\n",
        "  ent_exp['date'] = token['date']\n",
        "  ent_exp['n'] = range(1,ent_exp.shape[0]+1)\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",
        "  return ent_exp"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HrnpFFpbTthC"
      },
      "source": [
        "#Get Summary Stats\n",
        "def summary(token):\n",
        "  name = [x for x in globals() if globals()[x] is token][0]\n",
        "  name = re.search(r'\\w+(?=[_])',name).group()\n",
        "  token = token[['val']].describe()\n",
        "  return token.rename(columns={'val':'{}'.format(name)})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qnp2iTAe7zE7"
      },
      "source": [
        "SaiDai_ent = pd.read_csv('/content/drive/MyDrive/Decentralization Research/Index_tokens/Sai_Dai_ent.csv',parse_dates=['date'])\n",
        "SaiDai_ent.set_index(['date'],drop=False,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AcZYX9e_HnLQ"
      },
      "source": [
        "Dai_ent= pd.read_csv('/content/drive/MyDrive/Decentralization Research/Index_tokens/Dai_ent.csv',)\n",
        "Dai_ent.set_index(['date'],drop=False,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hhtn020xU8gQ"
      },
      "source": [
        "Sai_ent= pd.read_csv('/content/drive/MyDrive/Decentralization Research/Index_tokens/Sai_ent.csv',)\n",
        "Sai_ent.set_index(['date'],drop=False,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Dai_ent = ent_exp2(Dai_ent)\n",
        "Sai_ent = ent_exp2(Sai_ent)\n",
        "SaiDai_ent = ent_exp2(SaiDai_ent)"
      ],
      "metadata": {
        "id": "n3qa1xPLHO54"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Sai_ent.to_csv('/content/drive/MyDrive/Crypto/Sai.csv',index=False)\n",
        "# Dai_ent.to_csv('/content/drive/MyDrive/Crypto/Dai.csv',index=False)\n",
        "# SaiDai_ent.to_csv('/content/drive/MyDrive/Crypto/SaiDai.csv',index=False)"
      ],
      "metadata": {
        "id": "-AE-XqhFO7iA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jmc7CZjEIOeW"
      },
      "source": [
        "##SAI Query (Entropy)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Queried from 2017-12-18 to 2021-07-31"
      ],
      "metadata": {
        "id": "JPW8AOz1J5aR"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L4VvLsJ0IN6x"
      },
      "source": [
        "SAI = '0x89d24a6b4ccb1b6faa2625fe562bdd9a23260359'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U86PWC70IU0I"
      },
      "source": [
        "start = date(2017,12,18)\n",
        "end = date(2021,7,31)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ppQ3FXtVIiBi"
      },
      "source": [
        "Sai_ent1 = program_ent(start, end, SAI)\n",
        "Sai_ent1.to_csv('/content/drive/MyDrive/Crypto/Sai_ent1.csv',index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Sai_ent = pd.read_csv('/content/drive/MyDrive/Crypto/Sai_ent.csv')"
      ],
      "metadata": {
        "id": "P35wdVVGLHBe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##DAI Query (Entropy)"
      ],
      "metadata": {
        "id": "Zo9hQ5y1Jkua"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Queried from 2019-11-18 to 2021-07-31"
      ],
      "metadata": {
        "id": "RfKE0zYEJ97w"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JJkdC2f6kcjy"
      },
      "source": [
        "start = date(2019,11,18)\n",
        "end = date(2021,7,31)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KRkZvX4-EkKV"
      },
      "source": [
        "DAI = '0x6b175474e89094c44da98b954eedeac495271d0f'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hxuCuu_5fumA"
      },
      "source": [
        "Dai_ent1 = program_ent(start, end, DAI)\n",
        "Dai_ent1.to_csv('/content/drive/MyDrive/Crypto/Dai_ent1.csv',index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Dai_ent = pd.read_csv('/content/drive/MyDrive/Crypto/Dai_ent.csv')"
      ],
      "metadata": {
        "id": "ihx89UwVNOx0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Dai_ent"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 723
        },
        "id": "bd1jZnzWNRJI",
        "outputId": "182b91d0-5bb0-470f-d16d-b5c577d6fc99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "          val                 date\n",
              "0         NaN           2019-11-13\n",
              "1         NaN           2019-11-14\n",
              "2         NaN           2019-11-15\n",
              "3         NaN           2019-11-16\n",
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              "..        ...                  ...\n",
              "622  9.834266  2021-07-27 00:00:00\n",
              "623  7.114131  2021-07-28 00:00:00\n",
              "624  6.066676  2021-07-29 00:00:00\n",
              "625  9.927643  2021-07-30 00:00:00\n",
              "626  8.061206  2021-07-31 00:00:00\n",
              "\n",
              "[627 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##SAI DAI Joint Dataset Creation"
      ],
      "metadata": {
        "id": "VnKuvTnLKMSB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Concatenated SAI and DAI at DAI Genesis Date: 2019-11-18\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "WBM1DKe8LF0O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SaiDai_ent = Sai_ent.iloc[:700].append(Dai_ent.drop(axis=0,index=[0,1,2,3,4]))"
      ],
      "metadata": {
        "id": "4neDLbBaKOpU"
      },
      "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",
        "\n"
      ],
      "metadata": {
        "id": "WJCmaqqmVYev"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "NOTE: Sai and Dai do not have individual TVL, only has the TVL of MakerDAO "
      ],
      "metadata": {
        "id": "4Q8HwtacV1vE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# import requests\n",
        "# import json \n",
        "# TVL_MAKER = requests.get('https://data-api.defipulse.com/api/v1/defipulse/api/GetHistory?resolution=history&project=maker&api-key=837333596a02d0be6f87493768ce14bd2604b5d1c94fa0479c4c67a5d38c')\n",
        "# TVL_MAKER = TVL_MAKER.json()\n",
        "# TVL_MAKER =pd.DataFrame(TVL_MAKER)\n",
        "# TVL_MAKER['project']='MAKER'\n",
        "# TVL_MAKER.to_csv('/content/drive/MyDrive/Decentralization Research/TVL/TVL_MAKER.csv')\n",
        "# # TVL_MAKER\n"
      ],
      "metadata": {
        "id": "rqGPQuQFXQj3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# TVL_MAKER"
      ],
      "metadata": {
        "id": "47F9J8V1LAqh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "TVL_MAKER=pd.read_csv('/content/drive/MyDrive/Decentralization Research/TVL/TVL_MAKER.csv')\n",
        "TVL_MAKER.drop(columns='Unnamed: 0',inplace=True)"
      ],
      "metadata": {
        "id": "-L8GrqU5XhAU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# TVL_MAKER"
      ],
      "metadata": {
        "id": "N6_ZDGeU6umm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Visualization"
      ],
      "metadata": {
        "id": "UxE2sISaOwdd"
      }
    },
    {
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GTp2_s5KD0wD",
        "outputId": "66eb6c49-b050-4d15-f5a9-4f2fd3a32738"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-02-03 05:31:12--  https://github.com/plotly/orca/releases/download/v1.2.1/orca-1.2.1-x86_64.AppImage\n",
            "Resolving github.com (github.com)... 140.82.112.3\n",
            "Connecting to github.com (github.com)|140.82.112.3|:443... connected.\n",
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            "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.111.133, ...\n",
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            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 51607939 (49M) [application/octet-stream]\n",
            "Saving to: ‘/usr/local/bin/orca’\n",
            "\n",
            "/usr/local/bin/orca 100%[===================>]  49.22M   114MB/s    in 0.4s    \n",
            "\n",
            "2022-02-03 05:31:13 (114 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 packages were automatically installed and are no longer required:\n",
            "  cuda-command-line-tools-10-0 cuda-command-line-tools-10-1\n",
            "  cuda-command-line-tools-11-0 cuda-compiler-10-0 cuda-compiler-10-1\n",
            "  cuda-compiler-11-0 cuda-cuobjdump-10-0 cuda-cuobjdump-10-1\n",
            "  cuda-cuobjdump-11-0 cuda-cupti-10-0 cuda-cupti-10-1 cuda-cupti-11-0\n",
            "  cuda-cupti-dev-11-0 cuda-documentation-10-0 cuda-documentation-10-1\n",
            "  cuda-documentation-11-0 cuda-documentation-11-1 cuda-gdb-10-0 cuda-gdb-10-1\n",
            "  cuda-gdb-11-0 cuda-gpu-library-advisor-10-0 cuda-gpu-library-advisor-10-1\n",
            "  cuda-libraries-10-0 cuda-libraries-10-1 cuda-libraries-11-0\n",
            "  cuda-memcheck-10-0 cuda-memcheck-10-1 cuda-memcheck-11-0 cuda-nsight-10-0\n",
            "  cuda-nsight-10-1 cuda-nsight-11-0 cuda-nsight-11-1 cuda-nsight-compute-10-0\n",
            "  cuda-nsight-compute-10-1 cuda-nsight-compute-11-0 cuda-nsight-compute-11-1\n",
            "  cuda-nsight-systems-10-1 cuda-nsight-systems-11-0 cuda-nsight-systems-11-1\n",
            "  cuda-nvcc-10-0 cuda-nvcc-10-1 cuda-nvcc-11-0 cuda-nvdisasm-10-0\n",
            "  cuda-nvdisasm-10-1 cuda-nvdisasm-11-0 cuda-nvml-dev-10-0 cuda-nvml-dev-10-1\n",
            "  cuda-nvml-dev-11-0 cuda-nvprof-10-0 cuda-nvprof-10-1 cuda-nvprof-11-0\n",
            "  cuda-nvprune-10-0 cuda-nvprune-10-1 cuda-nvprune-11-0 cuda-nvtx-10-0\n",
            "  cuda-nvtx-10-1 cuda-nvtx-11-0 cuda-nvvp-10-0 cuda-nvvp-10-1 cuda-nvvp-11-0\n",
            "  cuda-nvvp-11-1 cuda-samples-10-0 cuda-samples-10-1 cuda-samples-11-0\n",
            "  cuda-samples-11-1 cuda-sanitizer-11-0 cuda-sanitizer-api-10-1\n",
            "  cuda-toolkit-10-0 cuda-toolkit-10-1 cuda-toolkit-11-0 cuda-toolkit-11-1\n",
            "  cuda-tools-10-0 cuda-tools-10-1 cuda-tools-11-0 cuda-tools-11-1\n",
            "  cuda-visual-tools-10-0 cuda-visual-tools-10-1 cuda-visual-tools-11-0\n",
            "  cuda-visual-tools-11-1 default-jre dkms freeglut3 freeglut3-dev\n",
            "  keyboard-configuration libargon2-0 libcap2 libcryptsetup12\n",
            "  libdevmapper1.02.1 libidn11 libip4tc0 libjansson4 libnvidia-cfg1-510\n",
            "  libnvidia-common-460 libnvidia-common-510 libnvidia-extra-510\n",
            "  libnvidia-fbc1-510 libnvidia-gl-510 libpam-systemd libpolkit-agent-1-0\n",
            "  libpolkit-backend-1-0 libpolkit-gobject-1-0 libxi-dev libxmu-dev\n",
            "  libxmu-headers libxnvctrl0 libxtst6 nsight-compute-2020.2.1\n",
            "  nsight-compute-2022.1.0 nsight-systems-2020.3.2 nsight-systems-2020.3.4\n",
            "  nsight-systems-2021.5.2 nvidia-dkms-510 nvidia-kernel-common-510\n",
            "  nvidia-kernel-source-510 nvidia-modprobe nvidia-settings openjdk-11-jre\n",
            "  policykit-1 policykit-1-gnome python3-xkit screen-resolution-extra systemd\n",
            "  systemd-sysv udev xserver-xorg-core-hwe-18.04 xserver-xorg-video-nvidia-510\n",
            "Use 'apt autoremove' to remove them.\n",
            "The following additional packages will be installed:\n",
            "  gconf-service gconf-service-backend gconf2-common libdbus-glib-1-2\n",
            "  libgail-common libgail18 libgtk2.0-bin libgtk2.0-common\n",
            "Suggested packages:\n",
            "  gvfs\n",
            "The following NEW packages will be installed:\n",
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            "\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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"
      ],
      "metadata": {
        "id": "ZpO6Asf-7ozU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Get Data\n"
      ],
      "metadata": {
        "id": "IibmHXjBr9du"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nkVXwK-RD6JK"
      },
      "source": [
        "#Our Decentralization Index and Moving Averages\n",
        "def ent_exp2(token):\n",
        "  ent_exp = pd.DataFrame()\n",
        "  ent_exp['val'] = np.exp2(token['val'])\n",
        "  ent_exp['date'] = token['date']\n",
        "  ent_exp['n'] = range(1,ent_exp.shape[0]+1)\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",
        "  return ent_exp"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qPwTCDM6D6JS"
      },
      "source": [
        "#Get Summary Stats\n",
        "def summary(token):\n",
        "  name = [x for x in globals() if globals()[x] is token][0]\n",
        "  name = re.search(r'\\w+(?=[_])',name).group()\n",
        "  token = token[['val']].describe()\n",
        "  return token.rename(columns={'val':'{}'.format(name)})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e6MSDD_qD6JT"
      },
      "source": [
        "SaiDai_ent = pd.read_csv('/content/drive/MyDrive/Decentralization Research/Index_tokens/Sai_Dai_ent.csv',parse_dates=['date'])\n",
        "SaiDai_ent.set_index(['date'],drop=False,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BhCGY5erD6JT"
      },
      "source": [
        "Dai_ent= pd.read_csv('/content/drive/MyDrive/Decentralization Research/Index_tokens/Dai_ent.csv',)\n",
        "Dai_ent.set_index(['date'],drop=False,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5K6upMsaD6JU"
      },
      "source": [
        "Sai_ent= pd.read_csv('/content/drive/MyDrive/Decentralization Research/Index_tokens/Sai_ent.csv',)\n",
        "Sai_ent.set_index(['date'],drop=False,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Dai_ent = ent_exp2(Dai_ent)\n",
        "Sai_ent = ent_exp2(Sai_ent)\n",
        "SaiDai_ent = ent_exp2(SaiDai_ent)"
      ],
      "metadata": {
        "id": "yBjbCIJcD6JU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Timeseries"
      ],
      "metadata": {
        "id": "01AoMurhsBaA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig1 = make_subplots()\n",
        "fig1.add_trace(go.Scatter(x=Dai_ent.date, y=Dai_ent['SMA30'],\n",
        "              mode='lines', name='Dai'))\n",
        "fig1.add_trace(go.Scatter(x=Sai_ent.date, y=Sai_ent['SMA30'],\n",
        "              mode='lines', name='Sai'))\n",
        "#fig1.update_layout(title='Decentralization Index 30 Day Moving Average' ,autosize=True,)\n",
        "fig1.add_shape(\n",
        "              go.layout.Shape(\n",
        "                    type=\"line\",\n",
        "                    xref=\"x\",\n",
        "                    yref=\"y\",\n",
        "                    x0='2019-11-18',\n",
        "                    y0=0,\n",
        "                    x1='2019-11-18',\n",
        "                    y1=4000,\n",
        "                    name='Sai Transition',\n",
        "                    line=dict(color='black', width=3, dash='dot'),\n",
        "                ))\n",
        "fig1.add_shape(\n",
        "              go.layout.Shape(\n",
        "                    type=\"line\",\n",
        "                    xref=\"x\",\n",
        "                    yref=\"y\",\n",
        "                    x0='2020-03-12',\n",
        "                    y0=0,\n",
        "                    x1='2020-03-12',\n",
        "                    y1=4000,\n",
        "                    name='Sai Transition',\n",
        "                    line=dict(color='black', width=3, dash='dot'),\n",
        "                ))\n",
        "fig1.add_shape(\n",
        "              go.layout.Shape(\n",
        "                    type=\"line\",\n",
        "                    xref=\"x\",\n",
        "                    yref=\"y\",\n",
        "                    x0='2020-09-15',\n",
        "                    y0=0,\n",
        "                    x1='2020-09-15',\n",
        "                    y1=4000,\n",
        "                    name='Sai Transition',\n",
        "                    line=dict(color='black', width=3, dash='dot'),\n",
        "                ))\n",
        "fig1.add_shape(\n",
        "              go.layout.Shape(\n",
        "                    type=\"line\",\n",
        "                    xref=\"x\",\n",
        "                    yref=\"y\",\n",
        "                    x0='2021-07-20',\n",
        "                    y0=0,\n",
        "                    x1='2021-07-20',\n",
        "                    y1=4000,\n",
        "                    line=dict(color='black', width=3, dash='dot'),\n",
        "                ))\n",
        "fig1.add_shape(\n",
        "              go.layout.Shape(\n",
        "                    type=\"line\",\n",
        "                    xref=\"x\",\n",
        "                    yref=\"y\",\n",
        "                    x0='2020-03-25',\n",
        "                    y0=0,\n",
        "                    x1='2020-03-25',\n",
        "                    y1=4000,\n",
        "                    line=dict(color='black', width=3, dash='dot'),\n",
        "                ))\n",
        "fig1.add_shape(\n",
        "              go.layout.Shape(\n",
        "                    type=\"line\",\n",
        "                    xref=\"x\",\n",
        "                    yref=\"y\",\n",
        "                    x0='2021-05-03',\n",
        "                    y0=0,\n",
        "                    x1='2021-05-03',\n",
        "                    y1=4000,\n",
        "                    line=dict(color='black', width=3, dash='dot'),\n",
        "                ))\n",
        "fig1.add_annotation(\n",
        "                x='2019-11-18',\n",
        "                y=1.06,\n",
        "                yref='paper',\n",
        "                showarrow=False,\n",
        "                text='Dai Genesis')\n",
        "fig1.add_annotation(\n",
        "                x='2020-03-12',\n",
        "                y=0.95,\n",
        "                yref='paper',\n",
        "                showarrow=True,\n",
        "                ax=-20,\n",
        "                ay=-30,\n",
        "                align=\"center\",\n",
        "                text='Bitcoin Crash')\n",
        "fig1.add_annotation(\n",
        "                x='2020-09-15',\n",
        "                y=1.06,\n",
        "                yref='paper',\n",
        "                showarrow=False,\n",
        "                text='Re-introduce Stability Fees')\n",
        "fig1.add_annotation(\n",
        "                x='2021-07-20',\n",
        "                y=1.06,\n",
        "                yref='paper',\n",
        "                showarrow=False,\n",
        "                text='MakerDAO Fully Decentralized')\n",
        "fig1.add_annotation(\n",
        "                x='2020-03-25',\n",
        "                y=1,\n",
        "                yref='paper',\n",
        "                showarrow=True,\n",
        "                arrowsize=5,\n",
        "                ax=50,\n",
        "                ay=-30,\n",
        "                text='Transfer Governance to MakerDAO Community')\n",
        "fig1.add_annotation(\n",
        "                x='2021-05-03',\n",
        "                y=1,\n",
        "                yref='paper',\n",
        "                showarrow=True,\n",
        "                arrowsize=5,\n",
        "                ax=-50,\n",
        "                ay=-30,\n",
        "                text='Foundation Returns All Funds to DAO')\n",
        "py.iplot(fig1)\n",
        "fig1.write_image('SaiDai.pdf')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "icVO_tixrkd5",
        "outputId": "5df41790-6604-45e1-97b1-9c89b0dd6033"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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    {
      "cell_type": "code",
      "source": [
        "fig1 = make_subplots()\n",
        "fig1.add_trace(go.Scatter(x=Dai_ent.date, y=Dai_ent['EMA0.3'],\n",
        "              mode='lines', name='Dai'))\n",
        "fig1.add_trace(go.Scatter(x=Sai_ent.date, y=Sai_ent['EMA0.3'],\n",
        "              mode='lines', name='Sai'))\n",
        "fig1.update_layout(title='Decentralization Index EMA0.3' ,autosize=True,)\n",
        "fig1.add_shape(\n",
        "              go.layout.Shape(\n",
        "                    type=\"line\",\n",
        "                    xref=\"x\",\n",
        "                    yref=\"y\",\n",
        "                    x0='2019-11-18',\n",
        "                    y0=0,\n",
        "                    x1='2019-11-18',\n",
        "                    y1=5500,\n",
        "                    name='Sai Transition',\n",
        "                    line=dict(color='black', width=3, dash='dot'),\n",
        "                ))\n",
        "fig1.add_shape(\n",
        "              go.layout.Shape(\n",
        "                    type=\"line\",\n",
        "                    xref=\"x\",\n",
        "                    yref=\"y\",\n",
        "                    x0='2020-03-12',\n",
        "                    y0=0,\n",
        "                    x1='2020-03-12',\n",
        "                    y1=5500,\n",
        "                    name='Sai Transition',\n",
        "                    line=dict(color='black', width=3, dash='dot'),\n",
        "                ))\n",
        "fig1.add_shape(\n",
        "              go.layout.Shape(\n",
        "                    type=\"line\",\n",
        "                    xref=\"x\",\n",
        "                    yref=\"y\",\n",
        "                    x0='2020-09-15',\n",
        "                    y0=0,\n",
        "                    x1='2020-09-15',\n",
        "                    y1=5500,\n",
        "                    name='Sai Transition',\n",
        "                    line=dict(color='black', width=3, dash='dot'),\n",
        "                ))\n",
        "fig1.add_shape(\n",
        "              go.layout.Shape(\n",
        "                    type=\"line\",\n",
        "                    xref=\"x\",\n",
        "                    yref=\"y\",\n",
        "                    x0='2021-07-20',\n",
        "                    y0=0,\n",
        "                    x1='2021-07-20',\n",
        "                    y1=5500,\n",
        "                    line=dict(color='black', width=3, dash='dot'),\n",
        "                ))\n",
        "fig1.add_shape(\n",
        "              go.layout.Shape(\n",
        "                    type=\"line\",\n",
        "                    xref=\"x\",\n",
        "                    yref=\"y\",\n",
        "                    x0='2020-03-25',\n",
        "                    y0=0,\n",
        "                    x1='2020-03-25',\n",
        "                    y1=5500,\n",
        "                    line=dict(color='black', width=3, dash='dot'),\n",
        "                ))\n",
        "fig1.add_shape(\n",
        "              go.layout.Shape(\n",
        "                    type=\"line\",\n",
        "                    xref=\"x\",\n",
        "                    yref=\"y\",\n",
        "                    x0='2021-05-03',\n",
        "                    y0=0,\n",
        "                    x1='2021-05-03',\n",
        "                    y1=5500,\n",
        "                    line=dict(color='black', width=3, dash='dot'),\n",
        "                ))\n",
        "fig1.add_annotation(\n",
        "                x='2019-11-18',\n",
        "                y=1.06,\n",
        "                yref='paper',\n",
        "                showarrow=False,\n",
        "                ax = -100,\n",
        "                text='Dai Genesis')\n",
        "fig1.add_annotation(\n",
        "                x='2020-03-12',\n",
        "                y=0.95,\n",
        "                yref='paper',\n",
        "                showarrow=True,\n",
        "                ax=-20,\n",
        "                ay=-30,\n",
        "                align=\"center\",\n",
        "                text='Bitcoin Crash')\n",
        "fig1.add_annotation(\n",
        "                x='2020-09-15',\n",
        "                y=1.06,\n",
        "                yref='paper',\n",
        "                showarrow=False,\n",
        "                text='Re-introduce Stability Fees')\n",
        "fig1.add_annotation(\n",
        "                x='2021-07-20',\n",
        "                y=1.06,\n",
        "                yref='paper',\n",
        "                showarrow=False,\n",
        "                text='MakerDAO Fully Decentralized')\n",
        "fig1.add_annotation(\n",
        "                x='2020-03-25',\n",
        "                y=1,\n",
        "                yref='paper',\n",
        "                showarrow=True,\n",
        "                arrowsize=5,\n",
        "                ax=50,\n",
        "                ay=-30,\n",
        "                text='Transfer Governance to MakerDAO Community')\n",
        "fig1.add_annotation(\n",
        "                x='2021-05-03',\n",
        "                y=1,\n",
        "                yref='paper',\n",
        "                showarrow=True,\n",
        "                arrowsize=5,\n",
        "                ax=-50,\n",
        "                ay=-30,\n",
        "                text='Foundation Returns All Funds to DAO')\n",
        "py.iplot(fig1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "YZp3ei5-sYnQ",
        "outputId": "77df0dc3-76f1-4ed6-cc2c-2631124ef153"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "source": [
        "# fig1 = make_subplots()\n",
        "# fig1.add_trace(go.Scatter(x=Dai.df.date, y=Dai.df['Price_Dev'],\n",
        "#               mode='lines', name='Dai'))\n",
        "# fig1.add_trace(go.Scatter(x=Sai.df.date, y=Sai.df['Price_Dev'],\n",
        "#               mode='lines', name='Sai'))\n",
        "# fig1.update_layout(title='Price Deviation' ,autosize=True,)"
      ],
      "metadata": {
        "id": "_EXC1KKV49m9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tvl = TVL_MAKER.copy()\n",
        "tvl['date'] = pd.to_datetime(pd.to_datetime(TVL_MAKER['timestamp'], unit='s').dt.date)\n",
        "tvl.drop(['timestamp', 'project'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "vj8IFlfP7DxI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig1 = go.Figure()\n",
        "fig1.add_trace(go.Scatter(x=tvl['date'], y=tvl['tvlUSD'],\n",
        "              mode='lines', name='Dai'))\n",
        "#fig1.update_layout(title='Maker TVL' ,autosize=True,)\n",
        "fig1.write_image('MakerTVL.pdf')\n",
        "fig1.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "aQbRdovm6mCl",
        "outputId": "4ae53e00-56a9-4b21-a11b-b49a38d1e5ef"
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      "execution_count": null,
      "outputs": [
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    },
    {
      "cell_type": "markdown",
      "source": [
        "#Econometrics"
      ],
      "metadata": {
        "id": "rE78ApeFS_Bg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " \n",
        "**NOTE: according to data availability from CoinMetrics**\n",
        " \n",
        "**all analysis of DAI completed for period Dec. 20, 2019 to July 31, 2021;**\n",
        " \n",
        "**all analysis of SAI completed for period Jan. 22, 2018 to October 31, 2019**\n"
      ],
      "metadata": {
        "id": "vj1zVd47BvKi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install stargazer\n",
        "! pip install pingouin"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tbsx_oNXZoGp",
        "outputId": "ef6faa0e-3f4e-4612-e0a1-a923c0be82d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting stargazer\n",
            "  Downloading stargazer-0.0.5-py3-none-any.whl (9.7 kB)\n",
            "Installing collected packages: stargazer\n",
            "Successfully installed stargazer-0.0.5\n",
            "Collecting pingouin\n",
            "  Downloading pingouin-0.5.0.tar.gz (182 kB)\n",
            "\u001b[K     |████████████████████████████████| 182 kB 7.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.19 in /usr/local/lib/python3.7/dist-packages (from pingouin) (1.21.5)\n",
            "Collecting scipy>=1.7\n",
            "  Downloading scipy-1.7.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (38.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 38.1 MB 1.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas>=1.0 in /usr/local/lib/python3.7/dist-packages (from pingouin) (1.3.5)\n",
            "Requirement already satisfied: matplotlib>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from pingouin) (3.2.2)\n",
            "Requirement already satisfied: seaborn>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from pingouin) (0.11.2)\n",
            "Collecting statsmodels>=0.12.0\n",
            "  Downloading statsmodels-0.13.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 9.8 MB 8.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from pingouin) (1.0.2)\n",
            "Collecting pandas_flavor>=0.2.0\n",
            "  Downloading pandas_flavor-0.2.0-py2.py3-none-any.whl (6.6 kB)\n",
            "Collecting outdated\n",
            "  Downloading outdated-0.2.1-py3-none-any.whl (7.5 kB)\n",
            "Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from pingouin) (0.8.9)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.2->pingouin) (0.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.2->pingouin) (2.8.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.2->pingouin) (1.3.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.0.2->pingouin) (3.0.7)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0->pingouin) (2018.9)\n",
            "Requirement already satisfied: xarray in /usr/local/lib/python3.7/dist-packages (from pandas_flavor>=0.2.0->pingouin) (0.18.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=3.0.2->pingouin) (1.15.0)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.7/dist-packages (from statsmodels>=0.12.0->pingouin) (21.3)\n",
            "Requirement already satisfied: patsy>=0.5.2 in /usr/local/lib/python3.7/dist-packages (from statsmodels>=0.12.0->pingouin) (0.5.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from outdated->pingouin) (2.23.0)\n",
            "Collecting littleutils\n",
            "  Downloading littleutils-0.2.2.tar.gz (6.6 kB)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->outdated->pingouin) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->outdated->pingouin) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->outdated->pingouin) (2021.10.8)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->outdated->pingouin) (1.24.3)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->pingouin) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->pingouin) (3.1.0)\n",
            "Requirement already satisfied: setuptools>=40.4 in /usr/local/lib/python3.7/dist-packages (from xarray->pandas_flavor>=0.2.0->pingouin) (57.4.0)\n",
            "Building wheels for collected packages: pingouin, littleutils\n",
            "  Building wheel for pingouin (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pingouin: filename=pingouin-0.5.0-py3-none-any.whl size=193660 sha256=a5782480f6107fc5b0ec5e01c01ade4468b6139da0717cfa9883f585e1dab4db\n",
            "  Stored in directory: /root/.cache/pip/wheels/14/46/f9/cedd81d68d2515c24bbbd000d5b347e4fe092ccc4b568f7f70\n",
            "  Building wheel for littleutils (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for littleutils: filename=littleutils-0.2.2-py3-none-any.whl size=7048 sha256=017b064fcb288331b34561837ad8098f9de7e887efc5ac6e2fdc5900142c7bb2\n",
            "  Stored in directory: /root/.cache/pip/wheels/d6/64/cd/32819b511a488e4993f2fab909a95330289c3f4e0f6ef4676d\n",
            "Successfully built pingouin littleutils\n",
            "Installing collected packages: scipy, littleutils, statsmodels, pandas-flavor, outdated, pingouin\n",
            "  Attempting uninstall: scipy\n",
            "    Found existing installation: scipy 1.4.1\n",
            "    Uninstalling scipy-1.4.1:\n",
            "      Successfully uninstalled scipy-1.4.1\n",
            "  Attempting uninstall: statsmodels\n",
            "    Found existing installation: statsmodels 0.10.2\n",
            "    Uninstalling statsmodels-0.10.2:\n",
            "      Successfully uninstalled statsmodels-0.10.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed littleutils-0.2.2 outdated-0.2.1 pandas-flavor-0.2.0 pingouin-0.5.0 scipy-1.7.3 statsmodels-0.13.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Connect to Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "print('Authenticated')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iAuL7wgMnHm1",
        "outputId": "43d44c9b-f0e2-415b-c477-babc2a0943ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "Authenticated\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\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",
        "from statsmodels.tsa.ar_model import AutoReg\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"
      ],
      "metadata": {
        "id": "9jHorRQMZfvk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Read Entropy of Dai, Sai"
      ],
      "metadata": {
        "id": "KzMFIl9gYZZI"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8NgWOeuAYdn2"
      },
      "source": [
        "Dai_ent= pd.read_csv('/content/drive/MyDrive/Decentralization Research/Index_tokens/Dai_ent.csv',)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sOq5cdvAYdn4"
      },
      "source": [
        "Sai_ent= pd.read_csv('/content/drive/MyDrive/Decentralization Research/Index_tokens/Sai_ent.csv',)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Analysis with ETH and BTC"
      ],
      "metadata": {
        "id": "gevAKLo_iefc"
      }
    },
    {
      "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": "JoVYkrhbigPf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Sai2 = Decentralization_Analysis(Sai_ent, sai_met, btc_met, eth_met)\n",
        "Dai2 = Decentralization_Analysis(Dai_ent, dai_met, btc_met, eth_met)"
      ],
      "metadata": {
        "id": "6ZomDiWpt1Tx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c887f712-4d52-426a-d2f3-8f8c1e2c347a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py:5516: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[name] = value\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:5047: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  errors=errors,\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/util/_decorators.py:311: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return func(*args, **kwargs)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py:5516: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[name] = value\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:5047: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  errors=errors,\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/util/_decorators.py:311: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return func(*args, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Sai2.df"
      ],
      "metadata": {
        "id": "ELLJuoaXuyY1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "outputId": "481f0ba4-5854-477f-809b-e9b2db2be22e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-8e8a2694-09ef-4de5-a146-88491cd67ac3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<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>val_pct30</th>\n",
              "      <th>CapMrktCurUSD</th>\n",
              "      <th>PriceUSD</th>\n",
              "      <th>VtyDayRet30d</th>\n",
              "      <th>TxTfrValAdjUSD</th>\n",
              "      <th>TxTfrCnt</th>\n",
              "      <th>BTC_PriceUSD</th>\n",
              "      <th>BTC_VtyDayRet30d</th>\n",
              "      <th>BTC_TxTfrValAdjUSD</th>\n",
              "      <th>BTC_TxTfrCnt</th>\n",
              "      <th>ETH_PriceUSD</th>\n",
              "      <th>ETH_VtyDayRet30d</th>\n",
              "      <th>ETH_TxTfrValAdjUSD</th>\n",
              "      <th>ETH_TxTfrCnt</th>\n",
              "      <th>BTC_Ret</th>\n",
              "      <th>ETH_Ret</th>\n",
              "      <th>BTC_Ret7</th>\n",
              "      <th>ETH_Ret7</th>\n",
              "      <th>BTC_Ret14</th>\n",
              "      <th>ETH_Ret14</th>\n",
              "      <th>BTC_Ret21</th>\n",
              "      <th>ETH_Ret21</th>\n",
              "      <th>BTC_Ret30</th>\n",
              "      <th>ETH_Ret30</th>\n",
              "      <th>ETH_PC</th>\n",
              "      <th>BTC_PC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>188.810443</td>\n",
              "      <td>2018-02-22</td>\n",
              "      <td>67</td>\n",
              "      <td>-0.123246</td>\n",
              "      <td>1.771949e+07</td>\n",
              "      <td>1.005000</td>\n",
              "      <td>0.002950</td>\n",
              "      <td>6.007286e+06</td>\n",
              "      <td>418</td>\n",
              "      <td>9878.158943</td>\n",
              "      <td>0.071108</td>\n",
              "      <td>5.320435e+09</td>\n",
              "      <td>555475.0</td>\n",
              "      <td>807.076899</td>\n",
              "      <td>0.073895</td>\n",
              "      <td>2.804218e+09</td>\n",
              "      <td>477456.0</td>\n",
              "      <td>-0.069987</td>\n",
              "      <td>-0.050687</td>\n",
              "      <td>0.104736</td>\n",
              "      <td>-0.086692</td>\n",
              "      <td>0.371727</td>\n",
              "      <td>0.114150</td>\n",
              "      <td>0.040673</td>\n",
              "      <td>-0.233143</td>\n",
              "      <td>-0.025936</td>\n",
              "      <td>-0.152585</td>\n",
              "      <td>-0.167701</td>\n",
              "      <td>0.161640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>203.019391</td>\n",
              "      <td>2018-02-23</td>\n",
              "      <td>68</td>\n",
              "      <td>-0.041877</td>\n",
              "      <td>1.852419e+07</td>\n",
              "      <td>1.005000</td>\n",
              "      <td>0.002950</td>\n",
              "      <td>4.471814e+06</td>\n",
              "      <td>499</td>\n",
              "      <td>10159.065613</td>\n",
              "      <td>0.071504</td>\n",
              "      <td>3.719724e+09</td>\n",
              "      <td>472286.0</td>\n",
              "      <td>855.767194</td>\n",
              "      <td>0.073703</td>\n",
              "      <td>1.763113e+09</td>\n",
              "      <td>459682.0</td>\n",
              "      <td>-0.055523</td>\n",
              "      <td>-0.039957</td>\n",
              "      <td>-0.022616</td>\n",
              "      <td>-0.132053</td>\n",
              "      <td>0.201930</td>\n",
              "      <td>-0.001699</td>\n",
              "      <td>0.092733</td>\n",
              "      <td>-0.208105</td>\n",
              "      <td>-0.089520</td>\n",
              "      <td>-0.180810</td>\n",
              "      <td>-0.228657</td>\n",
              "      <td>0.052408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>153.907385</td>\n",
              "      <td>2018-02-24</td>\n",
              "      <td>69</td>\n",
              "      <td>-0.342585</td>\n",
              "      <td>1.857489e+07</td>\n",
              "      <td>1.005000</td>\n",
              "      <td>0.002950</td>\n",
              "      <td>2.089031e+06</td>\n",
              "      <td>252</td>\n",
              "      <td>9686.601780</td>\n",
              "      <td>0.067937</td>\n",
              "      <td>2.578483e+09</td>\n",
              "      <td>405255.0</td>\n",
              "      <td>834.002846</td>\n",
              "      <td>0.073747</td>\n",
              "      <td>1.889097e+09</td>\n",
              "      <td>407946.0</td>\n",
              "      <td>0.028437</td>\n",
              "      <td>0.060329</td>\n",
              "      <td>-0.002969</td>\n",
              "      <td>-0.088969</td>\n",
              "      <td>0.173041</td>\n",
              "      <td>-0.020376</td>\n",
              "      <td>0.156269</td>\n",
              "      <td>-0.062503</td>\n",
              "      <td>-0.095033</td>\n",
              "      <td>-0.182742</td>\n",
              "      <td>-0.139212</td>\n",
              "      <td>0.080657</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>61.093661</td>\n",
              "      <td>2018-02-25</td>\n",
              "      <td>70</td>\n",
              "      <td>-0.690692</td>\n",
              "      <td>1.914041e+07</td>\n",
              "      <td>1.005000</td>\n",
              "      <td>0.002950</td>\n",
              "      <td>9.469742e+05</td>\n",
              "      <td>148</td>\n",
              "      <td>9603.227477</td>\n",
              "      <td>0.067911</td>\n",
              "      <td>1.664269e+09</td>\n",
              "      <td>405647.0</td>\n",
              "      <td>842.311720</td>\n",
              "      <td>0.073664</td>\n",
              "      <td>9.236595e+08</td>\n",
              "      <td>398722.0</td>\n",
              "      <td>-0.046507</td>\n",
              "      <td>-0.025433</td>\n",
              "      <td>-0.126199</td>\n",
              "      <td>-0.144816</td>\n",
              "      <td>0.135553</td>\n",
              "      <td>-0.019645</td>\n",
              "      <td>0.060720</td>\n",
              "      <td>-0.132055</td>\n",
              "      <td>-0.129722</td>\n",
              "      <td>-0.200857</td>\n",
              "      <td>-0.206042</td>\n",
              "      <td>-0.042520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>76.967589</td>\n",
              "      <td>2018-02-26</td>\n",
              "      <td>71</td>\n",
              "      <td>-0.571438</td>\n",
              "      <td>1.962054e+07</td>\n",
              "      <td>1.005000</td>\n",
              "      <td>0.002950</td>\n",
              "      <td>1.621146e+06</td>\n",
              "      <td>180</td>\n",
              "      <td>10312.365713</td>\n",
              "      <td>0.067154</td>\n",
              "      <td>2.722494e+09</td>\n",
              "      <td>519234.0</td>\n",
              "      <td>865.863039</td>\n",
              "      <td>0.073474</td>\n",
              "      <td>1.512570e+09</td>\n",
              "      <td>489705.0</td>\n",
              "      <td>-0.008607</td>\n",
              "      <td>0.009963</td>\n",
              "      <td>-0.081215</td>\n",
              "      <td>-0.079753</td>\n",
              "      <td>0.186063</td>\n",
              "      <td>0.033786</td>\n",
              "      <td>0.163953</td>\n",
              "      <td>0.006740</td>\n",
              "      <td>-0.129792</td>\n",
              "      <td>-0.190449</td>\n",
              "      <td>-0.079717</td>\n",
              "      <td>0.050083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>673</th>\n",
              "      <td>2420.100382</td>\n",
              "      <td>2019-11-26</td>\n",
              "      <td>709</td>\n",
              "      <td>-0.239091</td>\n",
              "      <td>8.489666e+07</td>\n",
              "      <td>1.003901</td>\n",
              "      <td>0.002169</td>\n",
              "      <td>2.111336e+07</td>\n",
              "      <td>8489</td>\n",
              "      <td>7165.437730</td>\n",
              "      <td>0.038779</td>\n",
              "      <td>2.210674e+09</td>\n",
              "      <td>710520.0</td>\n",
              "      <td>147.942879</td>\n",
              "      <td>0.037964</td>\n",
              "      <td>1.991650e+08</td>\n",
              "      <td>274343.0</td>\n",
              "      <td>0.028180</td>\n",
              "      <td>0.035215</td>\n",
              "      <td>-0.127633</td>\n",
              "      <td>-0.176905</td>\n",
              "      <td>-0.180664</td>\n",
              "      <td>-0.205275</td>\n",
              "      <td>-0.242008</td>\n",
              "      <td>-0.212177</td>\n",
              "      <td>-0.228182</td>\n",
              "      <td>-0.184499</td>\n",
              "      <td>-0.325820</td>\n",
              "      <td>-0.416353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>674</th>\n",
              "      <td>2547.806589</td>\n",
              "      <td>2019-11-27</td>\n",
              "      <td>710</td>\n",
              "      <td>0.117641</td>\n",
              "      <td>8.387683e+07</td>\n",
              "      <td>0.995523</td>\n",
              "      <td>0.002642</td>\n",
              "      <td>2.567266e+07</td>\n",
              "      <td>9809</td>\n",
              "      <td>7526.863817</td>\n",
              "      <td>0.039174</td>\n",
              "      <td>2.143191e+09</td>\n",
              "      <td>700717.0</td>\n",
              "      <td>153.248322</td>\n",
              "      <td>0.038017</td>\n",
              "      <td>3.524941e+08</td>\n",
              "      <td>268287.0</td>\n",
              "      <td>0.003697</td>\n",
              "      <td>0.009420</td>\n",
              "      <td>-0.117211</td>\n",
              "      <td>-0.156709</td>\n",
              "      <td>-0.184723</td>\n",
              "      <td>-0.207072</td>\n",
              "      <td>-0.232065</td>\n",
              "      <td>-0.218132</td>\n",
              "      <td>-0.250333</td>\n",
              "      <td>-0.198456</td>\n",
              "      <td>-0.335822</td>\n",
              "      <td>-0.425260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>675</th>\n",
              "      <td>1184.183065</td>\n",
              "      <td>2019-11-28</td>\n",
              "      <td>711</td>\n",
              "      <td>-0.396677</td>\n",
              "      <td>8.437744e+07</td>\n",
              "      <td>0.999706</td>\n",
              "      <td>0.002758</td>\n",
              "      <td>2.250259e+07</td>\n",
              "      <td>7225</td>\n",
              "      <td>7436.336382</td>\n",
              "      <td>0.038991</td>\n",
              "      <td>1.430809e+09</td>\n",
              "      <td>683235.0</td>\n",
              "      <td>151.010392</td>\n",
              "      <td>0.038444</td>\n",
              "      <td>3.337904e+08</td>\n",
              "      <td>266008.0</td>\n",
              "      <td>0.050440</td>\n",
              "      <td>0.035861</td>\n",
              "      <td>-0.068060</td>\n",
              "      <td>-0.121332</td>\n",
              "      <td>-0.141429</td>\n",
              "      <td>-0.184994</td>\n",
              "      <td>-0.195412</td>\n",
              "      <td>-0.199984</td>\n",
              "      <td>-0.192325</td>\n",
              "      <td>-0.161726</td>\n",
              "      <td>-0.283819</td>\n",
              "      <td>-0.335568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>676</th>\n",
              "      <td>1259.577317</td>\n",
              "      <td>2019-11-29</td>\n",
              "      <td>712</td>\n",
              "      <td>-0.294279</td>\n",
              "      <td>8.454597e+07</td>\n",
              "      <td>1.005388</td>\n",
              "      <td>0.002952</td>\n",
              "      <td>2.052543e+07</td>\n",
              "      <td>6999</td>\n",
              "      <td>7747.738042</td>\n",
              "      <td>0.039195</td>\n",
              "      <td>1.942321e+09</td>\n",
              "      <td>666194.0</td>\n",
              "      <td>154.256289</td>\n",
              "      <td>0.037999</td>\n",
              "      <td>2.079736e+08</td>\n",
              "      <td>296888.0</td>\n",
              "      <td>-0.012027</td>\n",
              "      <td>-0.014603</td>\n",
              "      <td>-0.023113</td>\n",
              "      <td>-0.060462</td>\n",
              "      <td>-0.139854</td>\n",
              "      <td>-0.182696</td>\n",
              "      <td>-0.192646</td>\n",
              "      <td>-0.191466</td>\n",
              "      <td>-0.211518</td>\n",
              "      <td>-0.208667</td>\n",
              "      <td>-0.297351</td>\n",
              "      <td>-0.338157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>677</th>\n",
              "      <td>1529.228531</td>\n",
              "      <td>2019-11-30</td>\n",
              "      <td>713</td>\n",
              "      <td>0.012953</td>\n",
              "      <td>8.461772e+07</td>\n",
              "      <td>1.002154</td>\n",
              "      <td>0.003008</td>\n",
              "      <td>1.299529e+07</td>\n",
              "      <td>4859</td>\n",
              "      <td>7556.193721</td>\n",
              "      <td>0.039213</td>\n",
              "      <td>1.775029e+09</td>\n",
              "      <td>635189.0</td>\n",
              "      <td>152.016210</td>\n",
              "      <td>0.037881</td>\n",
              "      <td>2.070767e+08</td>\n",
              "      <td>251630.0</td>\n",
              "      <td>0.041876</td>\n",
              "      <td>0.021495</td>\n",
              "      <td>0.064612</td>\n",
              "      <td>0.029912</td>\n",
              "      <td>-0.084828</td>\n",
              "      <td>-0.142972</td>\n",
              "      <td>-0.118707</td>\n",
              "      <td>-0.161825</td>\n",
              "      <td>-0.156282</td>\n",
              "      <td>-0.158343</td>\n",
              "      <td>-0.210722</td>\n",
              "      <td>-0.215000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>647 rows × 29 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8e8a2694-09ef-4de5-a146-88491cd67ac3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "          const element = document.querySelector('#df-8e8a2694-09ef-4de5-a146-88491cd67ac3');\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "             val       date    n  ...  ETH_Ret30    ETH_PC    BTC_PC\n",
              "31    188.810443 2018-02-22   67  ...  -0.152585 -0.167701  0.161640\n",
              "32    203.019391 2018-02-23   68  ...  -0.180810 -0.228657  0.052408\n",
              "33    153.907385 2018-02-24   69  ...  -0.182742 -0.139212  0.080657\n",
              "34     61.093661 2018-02-25   70  ...  -0.200857 -0.206042 -0.042520\n",
              "35     76.967589 2018-02-26   71  ...  -0.190449 -0.079717  0.050083\n",
              "..           ...        ...  ...  ...        ...       ...       ...\n",
              "673  2420.100382 2019-11-26  709  ...  -0.184499 -0.325820 -0.416353\n",
              "674  2547.806589 2019-11-27  710  ...  -0.198456 -0.335822 -0.425260\n",
              "675  1184.183065 2019-11-28  711  ...  -0.161726 -0.283819 -0.335568\n",
              "676  1259.577317 2019-11-29  712  ...  -0.208667 -0.297351 -0.338157\n",
              "677  1529.228531 2019-11-30  713  ...  -0.158343 -0.210722 -0.215000\n",
              "\n",
              "[647 rows x 29 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Sai2.heat_map()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "grQFGHnJL8Cu",
        "outputId": "4b500f6f-c5fb-4e83-e937-830e55bf4394"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Sai2.autor()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 812
        },
        "id": "Vo9HqmOSKeNo",
        "outputId": "4993f7a0-828f-4b5b-b1c9-cbc6e5cfa772"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            AutoReg Model Results                             \n",
            "==============================================================================\n",
            "Dep. Variable:                    val   No. Observations:                  647\n",
            "Model:                     AutoReg(5)   Log Likelihood               -5029.604\n",
            "Method:               Conditional MLE   S.D. of innovations            611.146\n",
            "Date:                Sat, 05 Feb 2022   AIC                          10073.208\n",
            "Time:                        18:41:50   BIC                          10104.460\n",
            "Sample:                             5   HQIC                         10085.336\n",
            "                                  647                                         \n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const        119.5451     35.995      3.321      0.001      48.997     190.093\n",
            "val.L1         0.6815      0.039     17.419      0.000       0.605       0.758\n",
            "val.L2        -0.1156      0.047     -2.435      0.015      -0.209      -0.023\n",
            "val.L3         0.2236      0.047      4.766      0.000       0.132       0.316\n",
            "val.L4        -0.0318      0.048     -0.668      0.504      -0.125       0.062\n",
            "val.L5         0.1320      0.039      3.363      0.001       0.055       0.209\n",
            "                                    Roots                                    \n",
            "=============================================================================\n",
            "                  Real          Imaginary           Modulus         Frequency\n",
            "-----------------------------------------------------------------------------\n",
            "AR.1            1.0625           -0.0000j            1.0625           -0.0000\n",
            "AR.2           -1.0637           -1.2811j            1.6651           -0.3603\n",
            "AR.3           -1.0637           +1.2811j            1.6651            0.3603\n",
            "AR.4            0.6528           -1.4649j            1.6038           -0.1833\n",
            "AR.5            0.6528           +1.4649j            1.6038            0.1833\n",
            "-----------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:591: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
            "  ' ignored when e.g. forecasting.', ValueWarning)\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-23-a20ac3923274>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mSai2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-20-5abb697efa98>\u001b[0m in \u001b[0;36mautor\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     81\u001b[0m     \u001b[0ml\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m     \u001b[0ml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mar_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 83\u001b[0;31m     \u001b[0mstargazer_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStargazer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     84\u001b[0m     \u001b[0mstargazer_result\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignificant_digits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m     \u001b[0mstargazer_result\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow_confidence_intervals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/stargazer/stargazer.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, models)\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_models\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextract_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvalidate_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/stargazer/stargazer.py\u001b[0m in \u001b[0;36mextract_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    102\u001b[0m         \u001b[0mbe\u001b[0m \u001b[0mmodified\u001b[0m \u001b[0mby\u001b[0m \u001b[0many\u001b[0m \u001b[0mrendering\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m         \"\"\"\n\u001b[0;32m--> 104\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    105\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mm\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/stargazer/stargazer.py\u001b[0m in \u001b[0;36mvalidate_input\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     51\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mm\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRegressionResultsWrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Please use trained OLS models as inputs'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     54\u001b[0m             \u001b[0mtargets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendog_names\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Please use trained OLS models as inputs"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Sai2.check_stationarity()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "CgPE4fvhvdGt",
        "outputId": "79abbb52-7399-4242-88c3-66d2e604dff8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dickey-Fuller test results:\n",
            "Statistical Test          -4.1026\n",
            "P-Value                    0.0010\n",
            "Used Lags                  7.0000\n",
            "Observations Number      639.0000\n",
            "Critical Values (1%)      -3.4406\n",
            "Critical Values (5%)      -2.8661\n",
            "Critical Values (10%)     -2.5692\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test          -9.7524\n",
            "P-Value                    0.0000\n",
            "Used Lags                  4.0000\n",
            "Observations Number      642.0000\n",
            "Critical Values (1%)      -3.4406\n",
            "Critical Values (5%)      -2.8661\n",
            "Critical Values (10%)     -2.5692\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test          -6.0732\n",
            "P-Value                    0.0000\n",
            "Used Lags                 19.0000\n",
            "Observations Number      627.0000\n",
            "Critical Values (1%)      -3.4408\n",
            "Critical Values (5%)      -2.8662\n",
            "Critical Values (10%)     -2.5692\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test          -5.0231\n",
            "P-Value                    0.0000\n",
            "Used Lags                 19.0000\n",
            "Observations Number      627.0000\n",
            "Critical Values (1%)      -3.4408\n",
            "Critical Values (5%)      -2.8662\n",
            "Critical Values (10%)     -2.5692\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test          -5.7970\n",
            "P-Value                    0.0000\n",
            "Used Lags                 13.0000\n",
            "Observations Number      633.0000\n",
            "Critical Values (1%)      -3.4407\n",
            "Critical Values (5%)      -2.8661\n",
            "Critical Values (10%)     -2.5692\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test          -5.0512\n",
            "P-Value                    0.0000\n",
            "Used Lags                 12.0000\n",
            "Observations Number      634.0000\n",
            "Critical Values (1%)      -3.4407\n",
            "Critical Values (5%)      -2.8661\n",
            "Critical Values (10%)     -2.5692\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test          -4.1296\n",
            "P-Value                    0.0009\n",
            "Used Lags                  8.0000\n",
            "Observations Number      638.0000\n",
            "Critical Values (1%)      -3.4406\n",
            "Critical Values (5%)      -2.8661\n",
            "Critical Values (10%)     -2.5692\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test          -5.4375\n",
            "P-Value                    0.0000\n",
            "Used Lags                 16.0000\n",
            "Observations Number      630.0000\n",
            "Critical Values (1%)      -3.4408\n",
            "Critical Values (5%)      -2.8661\n",
            "Critical Values (10%)     -2.5692\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test         -26.5235\n",
            "P-Value                    0.0000\n",
            "Used Lags                  0.0000\n",
            "Observations Number      646.0000\n",
            "Critical Values (1%)      -3.4405\n",
            "Critical Values (5%)      -2.8660\n",
            "Critical Values (10%)     -2.5692\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test          -5.9754\n",
            "P-Value                    0.0000\n",
            "Used Lags                 19.0000\n",
            "Observations Number      627.0000\n",
            "Critical Values (1%)      -3.4408\n",
            "Critical Values (5%)      -2.8662\n",
            "Critical Values (10%)     -2.5692\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test          -4.2434\n",
            "P-Value                    0.0006\n",
            "Used Lags                 14.0000\n",
            "Observations Number      632.0000\n",
            "Critical Values (1%)      -3.4407\n",
            "Critical Values (5%)      -2.8661\n",
            "Critical Values (10%)     -2.5692\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test          -5.2872\n",
            "P-Value                    0.0000\n",
            "Used Lags                 12.0000\n",
            "Observations Number      634.0000\n",
            "Critical Values (1%)      -3.4407\n",
            "Critical Values (5%)      -2.8661\n",
            "Critical Values (10%)     -2.5692\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test          -4.0192\n",
            "P-Value                    0.0013\n",
            "Used Lags                  6.0000\n",
            "Observations Number      640.0000\n",
            "Critical Values (1%)      -3.4406\n",
            "Critical Values (5%)      -2.8661\n",
            "Critical Values (10%)     -2.5692\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test          -2.9470\n",
            "P-Value                    0.0401\n",
            "Used Lags                  6.0000\n",
            "Observations Number      640.0000\n",
            "Critical Values (1%)      -3.4406\n",
            "Critical Values (5%)      -2.8661\n",
            "Critical Values (10%)     -2.5692\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test          -4.1820\n",
            "P-Value                    0.0007\n",
            "Used Lags                 16.0000\n",
            "Observations Number      630.0000\n",
            "Critical Values (1%)      -3.4408\n",
            "Critical Values (5%)      -2.8661\n",
            "Critical Values (10%)     -2.5692\n",
            "dtype: float64\n",
            "Stationary Variables: ['val', 'ETH_Ret', 'ETH_Ret7', 'ETH_Ret14', 'ETH_Ret21', 'ETH_Ret30', 'ETH_VtyDayRet30d', 'ETH_PC', 'BTC_Ret', 'BTC_Ret7', 'BTC_Ret14', 'BTC_Ret21', 'BTC_Ret30', 'BTC_VtyDayRet30d', 'BTC_PC']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Sai2.stargazer_reverse_eth()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "id": "IR_2J-givOuy",
        "outputId": "efaa594d-0dc5-4d65-c416-9e8fe254ed63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table style=\"text-align:center\"><tr><td colspan=\"8\" style=\"border-bottom: 1px solid black\"></td></tr><tr><td style=\"text-align:left\"></td><td colspan=\"7\"><em>Dependent variable:val</em></td></tr><tr><td style=\"text-align:left\"></td><tr><td style=\"text-align:left\"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td><td>(5)</td><td>(6)</td><td>(7)</td></tr><tr><td colspan=\"8\" style=\"border-bottom: 1px solid black\"></td></tr><tr><td style=\"text-align:left\">ETH_Ret</td><td>766.402<sup></sup></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\"></td><td>(1261.848)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\">ETH_Ret7</td><td></td><td>869.406<sup>*</sup></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\"></td><td></td><td>(520.834)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\">ETH_Ret14</td><td></td><td></td><td>784.206<sup>***</sup></td><td></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\"></td><td></td><td></td><td>(298.941)</td><td></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\">ETH_Ret21</td><td></td><td></td><td></td><td>770.518<sup>***</sup></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\"></td><td></td><td></td><td></td><td>(226.084)</td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\">ETH_Ret30</td><td></td><td></td><td></td><td></td><td>813.912<sup>***</sup></td><td></td><td></td></tr><tr><td style=\"text-align:left\"></td><td></td><td></td><td></td><td></td><td>(182.544)</td><td></td><td></td></tr><tr><td style=\"text-align:left\">ETH_VtyDayRet30d</td><td></td><td></td><td></td><td></td><td></td><td>-18345.301<sup>***</sup></td><td></td></tr><tr><td style=\"text-align:left\"></td><td></td><td></td><td></td><td></td><td></td><td>(3138.375)</td><td></td></tr><tr><td style=\"text-align:left\">ETH_PC</td><td></td><td></td><td></td><td></td><td></td><td></td><td>536.188<sup>***</sup></td></tr><tr><td style=\"text-align:left\"></td><td></td><td></td><td></td><td></td><td></td><td></td><td>(137.019)</td></tr><tr><td style=\"text-align:left\">const</td><td>1043.661<sup>***</sup></td><td>1051.636<sup>***</sup></td><td>1055.066<sup>***</sup></td><td>1058.171<sup>***</sup></td><td>1069.077<sup>***</sup></td><td>1959.454<sup>***</sup></td><td>1042.491<sup>***</sup></td></tr><tr><td style=\"text-align:left\"></td><td>(56.611)</td><td>(56.625)</td><td>(56.704)</td><td>(56.826)</td><td>(57.615)</td><td>(183.344)</td><td>(55.217)</td></tr><td colspan=\"8\" style=\"border-bottom: 1px solid black\"></td></tr><tr><td style=\"text-align: left\">Observations</td><td>647</td><td>647</td><td>647</td><td>647</td><td>647</td><td>647</td><td>647</td></tr><tr><td style=\"text-align: left\">R<sup>2</sup></td><td>0.001</td><td>0.012</td><td>0.022</td><td>0.035</td><td>0.050</td><td>0.049</td><td>0.044</td></tr><tr><td style=\"text-align: left\">Adjusted R<sup>2</sup></td><td>-0.000</td><td>0.010</td><td>0.021</td><td>0.033</td><td>0.049</td><td>0.048</td><td>0.042</td></tr><tr><td style=\"text-align: left\">Residual Std. Error</td><td>1073.315 (df=645)</td><td>1067.556 (df=645)</td><td>1061.961 (df=645)</td><td>1055.119 (df=645)</td><td>1046.736 (df=645)</td><td>1047.163 (df=645)</td><td>1050.152 (df=645)</td></tr><tr><td style=\"text-align: left\">F Statistic</td><td>0.369<sup></sup> (df=1; 645)</td><td>2.786<sup>*</sup> (df=1; 645)</td><td>6.882<sup>***</sup> (df=1; 645)</td><td>11.615<sup>***</sup> (df=1; 645)</td><td>19.880<sup>***</sup> (df=1; 645)</td><td>34.170<sup>***</sup> (df=1; 645)</td><td>15.313<sup>***</sup> (df=1; 645)</td></tr><tr><td colspan=\"8\" style=\"border-bottom: 1px solid black\"></td></tr><tr><td style=\"text-align: left\">Note:</td>\n",
              " <td colspan=\"7\" style=\"text-align: right\">\n",
              "  <sup>*</sup>p&lt;0.1;\n",
              "  <sup>**</sup>p&lt;0.05;\n",
              "  <sup>***</sup>p&lt;0.01\n",
              " </td></tr></table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Sai2.stargazer_reverse_btc()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "id": "JPx1JkozyQhA",
        "outputId": "86ae85bc-2ae5-49ec-9ae1-b0b205ea3298"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table style=\"text-align:center\"><tr><td colspan=\"8\" style=\"border-bottom: 1px solid black\"></td></tr><tr><td style=\"text-align:left\"></td><td colspan=\"7\"><em>Dependent variable:val</em></td></tr><tr><td style=\"text-align:left\"></td><tr><td style=\"text-align:left\"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td><td>(5)</td><td>(6)</td><td>(7)</td></tr><tr><td colspan=\"8\" style=\"border-bottom: 1px solid black\"></td></tr><tr><td style=\"text-align:left\">BTC_Ret</td><td>1998.629<sup></sup></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\"></td><td>(1988.974)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\">BTC_Ret7</td><td></td><td>1911.523<sup>**</sup></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\"></td><td></td><td>(798.870)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\">BTC_Ret14</td><td></td><td></td><td>1717.610<sup>***</sup></td><td></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\"></td><td></td><td></td><td>(507.868)</td><td></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\">BTC_Ret21</td><td></td><td></td><td></td><td>1653.969<sup>***</sup></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\"></td><td></td><td></td><td></td><td>(380.851)</td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\">BTC_Ret30</td><td></td><td></td><td></td><td></td><td>1872.264<sup>***</sup></td><td></td><td></td></tr><tr><td style=\"text-align:left\"></td><td></td><td></td><td></td><td></td><td>(291.215)</td><td></td><td></td></tr><tr><td style=\"text-align:left\">BTC_VtyDayRet30d</td><td></td><td></td><td></td><td></td><td></td><td>594.051<sup></sup></td><td></td></tr><tr><td style=\"text-align:left\"></td><td></td><td></td><td></td><td></td><td></td><td>(2919.573)</td><td></td></tr><tr><td style=\"text-align:left\">BTC_PC</td><td></td><td></td><td></td><td></td><td></td><td></td><td>1206.663<sup>***</sup></td></tr><tr><td style=\"text-align:left\"></td><td></td><td></td><td></td><td></td><td></td><td></td><td>(225.768)</td></tr><tr><td style=\"text-align:left\">const</td><td>1042.219<sup>***</sup></td><td>1040.003<sup>***</sup></td><td>1030.943<sup>***</sup></td><td>1020.407<sup>***</sup></td><td>1011.314<sup>***</sup></td><td>1019.716<sup>***</sup></td><td>1042.491<sup>***</sup></td></tr><tr><td style=\"text-align:left\"></td><td>(56.416)</td><td>(55.348)</td><td>(53.815)</td><td>(52.220)</td><td>(49.525)</td><td>(118.201)</td><td>(52.688)</td></tr><td colspan=\"8\" style=\"border-bottom: 1px solid black\"></td></tr><tr><td style=\"text-align: left\">Observations</td><td>647</td><td>647</td><td>647</td><td>647</td><td>647</td><td>647</td><td>647</td></tr><tr><td style=\"text-align: left\">R<sup>2</sup></td><td>0.005</td><td>0.033</td><td>0.058</td><td>0.086</td><td>0.145</td><td>0.000</td><td>0.120</td></tr><tr><td style=\"text-align: left\">Adjusted R<sup>2</sup></td><td>0.003</td><td>0.032</td><td>0.057</td><td>0.084</td><td>0.144</td><td>-0.001</td><td>0.119</td></tr><tr><td style=\"text-align: left\">Residual Std. Error</td><td>1071.289 (df=645)</td><td>1055.842 (df=645)</td><td>1042.084 (df=645)</td><td>1026.838 (df=645)</td><td>992.962 (df=645)</td><td>1073.918 (df=645)</td><td>1007.404 (df=645)</td></tr><tr><td style=\"text-align: left\">F Statistic</td><td>1.010<sup></sup> (df=1; 645)</td><td>5.725<sup>**</sup> (df=1; 645)</td><td>11.438<sup>***</sup> (df=1; 645)</td><td>18.860<sup>***</sup> (df=1; 645)</td><td>41.334<sup>***</sup> (df=1; 645)</td><td>0.041<sup></sup> (df=1; 645)</td><td>28.566<sup>***</sup> (df=1; 645)</td></tr><tr><td colspan=\"8\" style=\"border-bottom: 1px solid black\"></td></tr><tr><td style=\"text-align: left\">Note:</td>\n",
              " <td colspan=\"7\" style=\"text-align: right\">\n",
              "  <sup>*</sup>p&lt;0.1;\n",
              "  <sup>**</sup>p&lt;0.05;\n",
              "  <sup>***</sup>p&lt;0.01\n",
              " </td></tr></table>"
            ],
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              "<IPython.core.display.HTML object>"
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Dai2.autor()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YdVJvbeRK1C7",
        "outputId": "d12660dd-e996-4b0f-9c33-86f7aed78c16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:591: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
            "  ' ignored when e.g. forecasting.', ValueWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            AutoReg Model Results                             \n",
            "==============================================================================\n",
            "Dep. Variable:                    val   No. Observations:                  559\n",
            "Model:                     AutoReg(5)   Log Likelihood               -4418.014\n",
            "Method:               Conditional MLE   S.D. of innovations            703.323\n",
            "Date:                Sat, 05 Feb 2022   AIC                           8850.028\n",
            "Time:                        18:35:26   BIC                           8880.248\n",
            "Sample:                             5   HQIC                          8861.833\n",
            "                                  559                                         \n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const        228.7253     53.853      4.247      0.000     123.176     334.275\n",
            "val.L1         0.4897      0.042     11.563      0.000       0.407       0.573\n",
            "val.L2         0.0955      0.047      2.046      0.041       0.004       0.187\n",
            "val.L3         0.1397      0.047      3.004      0.003       0.049       0.231\n",
            "val.L4         0.1702      0.047      3.643      0.000       0.079       0.262\n",
            "val.L5        -0.0760      0.042     -1.792      0.073      -0.159       0.007\n",
            "                                    Roots                                    \n",
            "=============================================================================\n",
            "                  Real          Imaginary           Modulus         Frequency\n",
            "-----------------------------------------------------------------------------\n",
            "AR.1            1.1210           -0.0000j            1.1210           -0.0000\n",
            "AR.2           -1.6466           -0.0000j            1.6466           -0.5000\n",
            "AR.3           -0.1434           -1.5216j            1.5283           -0.2650\n",
            "AR.4           -0.1434           +1.5216j            1.5283            0.2650\n",
            "AR.5            3.0507           -0.0000j            3.0507           -0.0000\n",
            "-----------------------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Dai2.check_stationarity()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "NBjwkaMkwplu",
        "outputId": "4ddc6d2d-e9ed-4b05-8193-1ce5ac7b3d17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dickey-Fuller test results:\n",
            "Statistical Test          -3.5444\n",
            "P-Value                    0.0069\n",
            "Used Lags                  8.0000\n",
            "Observations Number      551.0000\n",
            "Critical Values (1%)      -3.4423\n",
            "Critical Values (5%)      -2.8668\n",
            "Critical Values (10%)     -2.5696\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test          -6.2575\n",
            "P-Value                    0.0000\n",
            "Used Lags                 12.0000\n",
            "Observations Number      547.0000\n",
            "Critical Values (1%)      -3.4424\n",
            "Critical Values (5%)      -2.8668\n",
            "Critical Values (10%)     -2.5696\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test          -4.9812\n",
            "P-Value                    0.0000\n",
            "Used Lags                 17.0000\n",
            "Observations Number      542.0000\n",
            "Critical Values (1%)      -3.4425\n",
            "Critical Values (5%)      -2.8669\n",
            "Critical Values (10%)     -2.5696\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test          -3.7503\n",
            "P-Value                    0.0035\n",
            "Used Lags                 14.0000\n",
            "Observations Number      545.0000\n",
            "Critical Values (1%)      -3.4424\n",
            "Critical Values (5%)      -2.8669\n",
            "Critical Values (10%)     -2.5696\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test          -4.5342\n",
            "P-Value                    0.0002\n",
            "Used Lags                 13.0000\n",
            "Observations Number      546.0000\n",
            "Critical Values (1%)      -3.4424\n",
            "Critical Values (5%)      -2.8668\n",
            "Critical Values (10%)     -2.5696\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test          -4.7273\n",
            "P-Value                    0.0001\n",
            "Used Lags                 19.0000\n",
            "Observations Number      539.0000\n",
            "Critical Values (1%)      -3.4425\n",
            "Critical Values (5%)      -2.8669\n",
            "Critical Values (10%)     -2.5696\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test          -2.7382\n",
            "P-Value                    0.0677\n",
            "Used Lags                  4.0000\n",
            "Observations Number      555.0000\n",
            "Critical Values (1%)      -3.4422\n",
            "Critical Values (5%)      -2.8668\n",
            "Critical Values (10%)     -2.5696\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test         -10.7726\n",
            "P-Value                    0.0000\n",
            "Used Lags                  3.0000\n",
            "Observations Number      556.0000\n",
            "Critical Values (1%)      -3.4422\n",
            "Critical Values (5%)      -2.8668\n",
            "Critical Values (10%)     -2.5695\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test          -4.6097\n",
            "P-Value                    0.0001\n",
            "Used Lags                 16.0000\n",
            "Observations Number      543.0000\n",
            "Critical Values (1%)      -3.4425\n",
            "Critical Values (5%)      -2.8669\n",
            "Critical Values (10%)     -2.5696\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test          -3.5229\n",
            "P-Value                    0.0074\n",
            "Used Lags                 14.0000\n",
            "Observations Number      545.0000\n",
            "Critical Values (1%)      -3.4424\n",
            "Critical Values (5%)      -2.8669\n",
            "Critical Values (10%)     -2.5696\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test          -4.2888\n",
            "P-Value                    0.0005\n",
            "Used Lags                  6.0000\n",
            "Observations Number      553.0000\n",
            "Critical Values (1%)      -3.4422\n",
            "Critical Values (5%)      -2.8668\n",
            "Critical Values (10%)     -2.5696\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test          -3.2269\n",
            "P-Value                    0.0185\n",
            "Used Lags                  6.0000\n",
            "Observations Number      552.0000\n",
            "Critical Values (1%)      -3.4423\n",
            "Critical Values (5%)      -2.8668\n",
            "Critical Values (10%)     -2.5696\n",
            "dtype: float64\n",
            "Dickey-Fuller test results:\n",
            "Statistical Test          -2.4125\n",
            "P-Value                    0.1382\n",
            "Used Lags                  1.0000\n",
            "Observations Number      558.0000\n",
            "Critical Values (1%)      -3.4421\n",
            "Critical Values (5%)      -2.8667\n",
            "Critical Values (10%)     -2.5695\n",
            "dtype: float64\n",
            "Stationary Variables: ['val', 'ETH_Ret', 'ETH_Ret7', 'ETH_Ret14', 'ETH_Ret21', 'ETH_Ret30', 'BTC_Ret', 'BTC_Ret7', 'BTC_Ret14', 'BTC_Ret21', 'BTC_Ret30']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Dai2.stargazer_reverse_eth()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "id": "ERSd7P19wk_d",
        "outputId": "f92653b1-803c-45e8-e518-0cff564b2c62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table style=\"text-align:center\"><tr><td colspan=\"8\" style=\"border-bottom: 1px solid black\"></td></tr><tr><td style=\"text-align:left\"></td><td colspan=\"7\"><em>Dependent variable:val</em></td></tr><tr><td style=\"text-align:left\"></td><tr><td style=\"text-align:left\"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td><td>(5)</td><td>(6)</td><td>(7)</td></tr><tr><td colspan=\"8\" style=\"border-bottom: 1px solid black\"></td></tr><tr><td style=\"text-align:left\">ETH_Ret</td><td>-1831.393<sup></sup></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\"></td><td>(1366.873)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\">ETH_Ret7</td><td></td><td>-102.925<sup></sup></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\"></td><td></td><td>(549.664)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\">ETH_Ret14</td><td></td><td></td><td>169.221<sup></sup></td><td></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\"></td><td></td><td></td><td>(342.763)</td><td></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\">ETH_Ret21</td><td></td><td></td><td></td><td>391.680<sup></sup></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\"></td><td></td><td></td><td></td><td>(266.600)</td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\">ETH_Ret30</td><td></td><td></td><td></td><td></td><td>443.710<sup>**</sup></td><td></td><td></td></tr><tr><td style=\"text-align:left\"></td><td></td><td></td><td></td><td></td><td>(191.323)</td><td></td><td></td></tr><tr><td style=\"text-align:left\">ETH_VtyDayRet30d</td><td></td><td></td><td></td><td></td><td></td><td>-32.986<sup></sup></td><td></td></tr><tr><td style=\"text-align:left\"></td><td></td><td></td><td></td><td></td><td></td><td>(2136.514)</td><td></td></tr><tr><td style=\"text-align:left\">ETH_PC</td><td></td><td></td><td></td><td></td><td></td><td></td><td>254.369<sup></sup></td></tr><tr><td style=\"text-align:left\"></td><td></td><td></td><td></td><td></td><td></td><td></td><td>(158.502)</td></tr><tr><td style=\"text-align:left\">const</td><td>1285.590<sup>***</sup></td><td>1278.585<sup>***</sup></td><td>1258.554<sup>***</sup></td><td>1218.772<sup>***</sup></td><td>1181.144<sup>***</sup></td><td>1275.678<sup>***</sup></td><td>1273.973<sup>***</sup></td></tr><tr><td style=\"text-align:left\"></td><td>(58.979)</td><td>(63.703)</td><td>(65.310)</td><td>(68.101)</td><td>(69.799)</td><td>(133.185)</td><td>(55.860)</td></tr><td colspan=\"8\" style=\"border-bottom: 1px solid black\"></td></tr><tr><td style=\"text-align: left\">Observations</td><td>559</td><td>559</td><td>559</td><td>559</td><td>559</td><td>559</td><td>559</td></tr><tr><td style=\"text-align: left\">R<sup>2</sup></td><td>0.010</td><td>0.000</td><td>0.001</td><td>0.012</td><td>0.023</td><td>0.000</td><td>0.014</td></tr><tr><td style=\"text-align: left\">Adjusted R<sup>2</sup></td><td>0.008</td><td>-0.002</td><td>-0.000</td><td>0.010</td><td>0.021</td><td>-0.002</td><td>0.012</td></tr><tr><td style=\"text-align: left\">Residual Std. Error</td><td>1020.975 (df=557)</td><td>1025.956 (df=557)</td><td>1025.377 (df=557)</td><td>1020.123 (df=557)</td><td>1014.436 (df=557)</td><td>1026.070 (df=557)</td><td>1018.931 (df=557)</td></tr><tr><td style=\"text-align: left\">F Statistic</td><td>1.795<sup></sup> (df=1; 557)</td><td>0.035<sup></sup> (df=1; 557)</td><td>0.244<sup></sup> (df=1; 557)</td><td>2.158<sup></sup> (df=1; 557)</td><td>5.379<sup>**</sup> (df=1; 557)</td><td>0.000<sup></sup> (df=1; 557)</td><td>2.575<sup></sup> (df=1; 557)</td></tr><tr><td colspan=\"8\" style=\"border-bottom: 1px solid black\"></td></tr><tr><td style=\"text-align: left\">Note:</td>\n",
              " <td colspan=\"7\" style=\"text-align: right\">\n",
              "  <sup>*</sup>p&lt;0.1;\n",
              "  <sup>**</sup>p&lt;0.05;\n",
              "  <sup>***</sup>p&lt;0.01\n",
              " </td></tr></table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Dai2.stargazer_reverse_btc()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "id": "9IfvLAtKch8c",
        "outputId": "f5a7f54a-32a2-4159-ae7b-da5b169f8813"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table style=\"text-align:center\"><tr><td colspan=\"8\" style=\"border-bottom: 1px solid black\"></td></tr><tr><td style=\"text-align:left\"></td><td colspan=\"7\"><em>Dependent variable:val</em></td></tr><tr><td style=\"text-align:left\"></td><tr><td style=\"text-align:left\"></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td><td>(5)</td><td>(6)</td><td>(7)</td></tr><tr><td colspan=\"8\" style=\"border-bottom: 1px solid black\"></td></tr><tr><td style=\"text-align:left\">BTC_Ret</td><td>-2896.656<sup></sup></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\"></td><td>(1808.711)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\">BTC_Ret7</td><td></td><td>-750.178<sup></sup></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\"></td><td></td><td>(672.075)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\">BTC_Ret14</td><td></td><td></td><td>-325.353<sup></sup></td><td></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\"></td><td></td><td></td><td>(422.519)</td><td></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\">BTC_Ret21</td><td></td><td></td><td></td><td>127.183<sup></sup></td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\"></td><td></td><td></td><td></td><td>(334.516)</td><td></td><td></td><td></td></tr><tr><td style=\"text-align:left\">BTC_Ret30</td><td></td><td></td><td></td><td></td><td>178.575<sup></sup></td><td></td><td></td></tr><tr><td style=\"text-align:left\"></td><td></td><td></td><td></td><td></td><td>(253.038)</td><td></td><td></td></tr><tr><td style=\"text-align:left\">BTC_VtyDayRet30d</td><td></td><td></td><td></td><td></td><td></td><td>1588.300<sup></sup></td><td></td></tr><tr><td style=\"text-align:left\"></td><td></td><td></td><td></td><td></td><td></td><td>(2666.581)</td><td></td></tr><tr><td style=\"text-align:left\">BTC_PC</td><td></td><td></td><td></td><td></td><td></td><td></td><td>50.838<sup></sup></td></tr><tr><td style=\"text-align:left\"></td><td></td><td></td><td></td><td></td><td></td><td></td><td>(204.327)</td></tr><tr><td style=\"text-align:left\">const</td><td>1284.587<sup>***</sup></td><td>1292.775<sup>***</sup></td><td>1290.119<sup>***</sup></td><td>1264.126<sup>***</sup></td><td>1253.571<sup>***</sup></td><td>1213.626<sup>***</sup></td><td>1273.973<sup>***</sup></td></tr><tr><td style=\"text-align:left\"></td><td>(57.804)</td><td>(61.971)</td><td>(63.316)</td><td>(64.770)</td><td>(67.440)</td><td>(126.570)</td><td>(56.316)</td></tr><td colspan=\"8\" style=\"border-bottom: 1px solid black\"></td></tr><tr><td style=\"text-align: left\">Observations</td><td>559</td><td>559</td><td>559</td><td>559</td><td>559</td><td>559</td><td>559</td></tr><tr><td style=\"text-align: left\">R<sup>2</sup></td><td>0.014</td><td>0.007</td><td>0.003</td><td>0.001</td><td>0.002</td><td>0.001</td><td>0.000</td></tr><tr><td style=\"text-align: left\">Adjusted R<sup>2</sup></td><td>0.012</td><td>0.005</td><td>0.001</td><td>-0.001</td><td>0.000</td><td>-0.001</td><td>-0.001</td></tr><tr><td style=\"text-align: left\">Residual Std. Error</td><td>1018.963 (df=557)</td><td>1022.683 (df=557)</td><td>1024.682 (df=557)</td><td>1025.714 (df=557)</td><td>1024.991 (df=557)</td><td>1025.637 (df=557)</td><td>1025.908 (df=557)</td></tr><tr><td style=\"text-align: left\">F Statistic</td><td>2.565<sup></sup> (df=1; 557)</td><td>1.246<sup></sup> (df=1; 557)</td><td>0.593<sup></sup> (df=1; 557)</td><td>0.145<sup></sup> (df=1; 557)</td><td>0.498<sup></sup> (df=1; 557)</td><td>0.355<sup></sup> (df=1; 557)</td><td>0.062<sup></sup> (df=1; 557)</td></tr><tr><td colspan=\"8\" style=\"border-bottom: 1px solid black\"></td></tr><tr><td style=\"text-align: left\">Note:</td>\n",
              " <td colspan=\"7\" style=\"text-align: right\">\n",
              "  <sup>*</sup>p&lt;0.1;\n",
              "  <sup>**</sup>p&lt;0.05;\n",
              "  <sup>***</sup>p&lt;0.01\n",
              " </td></tr></table>"
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