Analyzing_index_eth_to_stablecoin_ratio.ipynb

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  "metadata": {
    "colab": {
      "name": "Analyzing index eth_to_stablecoin_ratio",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qTmLBxDxBAZL"
      },
      "source": [
        "### Provide your credentials to the runtime"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uI4f2Z1YC7tq"
      },
      "source": [
        "In this notebook we'll validate data and do some EDA on the produced indexes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SeTJb51SKs_W",
        "outputId": "0ce89444-d90d-4849-b692-8f0b62fec1bf"
      },
      "source": [
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "print('Authenticated')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Authenticated\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "agTqsh2GaeQo"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "# change plotting colors per client request\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "# Increase default figure and font sizes for easier viewing.\n",
        "plt.rcParams['figure.figsize'] = (8, 6)\n",
        "plt.rcParams['font.size'] = 14"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hWK0nJWgKs_8"
      },
      "source": [
        "project_id = 'supple-antenna-326401'"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uz7VI5QEvodR"
      },
      "source": [
        "from google.cloud import bigquery\n",
        "\n",
        "client = bigquery.Client(project=project_id)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C0XeeHuJvfL4"
      },
      "source": [
        "compound_borrows_df = client.query('''select * from `arcx_dev.compound_usdc_sample_address`''').to_dataframe().drop(['log_index','contract_address'],axis=1)\n",
        "compound_borrows_df['borrowAmount'] = compound_borrows_df['borrowAmount'].apply(lambda x: int(x)/10**6)\n",
        "compound_borrows_df['accountBorrows'] = compound_borrows_df['accountBorrows'].apply(lambda x: int(x)/10**6)\n",
        "compound_borrows_df['totalBorrows'] = compound_borrows_df['totalBorrows'].apply(lambda x: int(x)/10**6)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R8FmsRXoDcOo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "994615dd-ec2a-42c2-beeb-272bb4e4e688"
      },
      "source": [
        "compound_borrows_df"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>block_timestamp</th>\n",
              "      <th>block_number</th>\n",
              "      <th>transaction_hash</th>\n",
              "      <th>borrower</th>\n",
              "      <th>borrowAmount</th>\n",
              "      <th>accountBorrows</th>\n",
              "      <th>totalBorrows</th>\n",
              "      <th>has_liquidate_event</th>\n",
              "      <th>rk</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "<p>14358 rows × 9 columns</p>\n",
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            ],
            "text/plain": [
              "                block_timestamp  block_number  ... has_liquidate_event rk\n",
              "0     2021-07-24 09:56:56+00:00      12888298  ...                   0  1\n",
              "1     2021-08-01 11:35:16+00:00      12939392  ...                   0  1\n",
              "2     2021-06-28 13:24:34+00:00      12722770  ...                   0  1\n",
              "3     2021-07-22 02:53:17+00:00      12873650  ...                   0  1\n",
              "4     2021-09-06 01:28:54+00:00      13169258  ...                   0  1\n",
              "...                         ...           ...  ...                 ... ..\n",
              "14353 2021-07-26 07:52:50+00:00      12900608  ...                   0  1\n",
              "14354 2021-08-08 11:46:25+00:00      12984248  ...                   0  1\n",
              "14355 2021-07-09 10:00:54+00:00      12792522  ...                   0  1\n",
              "14356 2021-07-09 16:11:24+00:00      12794142  ...                   0  1\n",
              "14357 2021-07-10 15:07:18+00:00      12800327  ...                   0  1\n",
              "\n",
              "[14358 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XGR77h5V_y4s"
      },
      "source": [
        "The sample data is comprised with the compound `borrow` events and then a column with `has_liquidation_event` if a liquidation event occured after that borrow. This is a certainly a simplification of the underlying borrowing mechanics."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BGTdC3YWmhI1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "69a431b8-27c2-4c89-8c11-ba1b1fd9f8b4"
      },
      "source": [
        "compound_borrows_df['has_liquidate_event'].value_counts()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    14071\n",
              "1      287\n",
              "Name: has_liquidate_event, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mLrFP_hNCP0u"
      },
      "source": [
        "the idea is to label liquidation events as negative samples, and \n",
        "ongoing borrows as positive.  Again, this might not be a good representation ( a lot of factors might be missing)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mmWof5pkCiNI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90e4a83e-ca47-47bd-b65f-e23b6deb72d3"
      },
      "source": [
        "compound_borrows_df['borrower'].nunique()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3019"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jL6K0JBtDr5A"
      },
      "source": [
        "we have ~3k unique addresses we're going to compute the index on"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UWSOGeSAk0QX"
      },
      "source": [
        "# data validation\n",
        "at this point we've already run the dbt models and created:\n",
        "```\n",
        "10:41:59 | 1 of 4 START table model arcx_dev.compound_usdc_sample_address....... [RUN]\n",
        "10:42:08 | 1 of 4 OK created table model arcx_dev.compound_usdc_sample_address.. [CREATE TABLE (16.4k rows, 3.6 MB processed) in 8.37s]\n",
        "10:42:08 | 2 of 4 START table model arcx_dev.ethereum_balances_over_time........ [RUN]\n",
        "10:42:42 | 2 of 4 OK created table model arcx_dev.ethereum_balances_over_time... [CREATE TABLE (1.1m rows, 558.1 GB processed) in 34.53s]\n",
        "10:42:42 | 3 of 4 START table model arcx_dev.stablecoin_balances_over_time...... [RUN]\n",
        "10:42:52 | 3 of 4 OK created table model arcx_dev.stablecoin_balances_over_time. [CREATE TABLE (1.5m rows, 117.5 GB processed) in 9.75s]\n",
        "10:42:52 | 4 of 4 START table model arcx_dev.stablecoin_balances_over_time_adjusted [RUN]\n",
        "10:43:09 | 4 of 4 OK created table model arcx_dev.stablecoin_balances_over_time_adjusted [CREATE TABLE (1.1m rows, 237.0 MB processed) in 17.07s]\n",
        "```\n",
        "\n",
        "we'll use etherscan.io to check for the latest balances on\n",
        "* eth\n",
        "* stablecoin (usdt+usdc+dai) "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m-ZKYbXXlAID",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6274d707-f1b9-448d-b40e-d4cbf6757503"
      },
      "source": [
        "sample_address = '0x9c92d74ae777fa12715f0033ba027ecb86bbff6a'\n",
        "print(f'http://etherscan.io/address/{sample_address}')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "http://etherscan.io/address/0x9c92d74ae777fa12715f0033ba027ecb86bbff6a\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1gT06gDSmbYw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "8e6ca5f9-b964-4c63-84fd-86cbd840db3c"
      },
      "source": [
        "client.query(f\"select * from arcx_dev.ethereum_balances_over_time where address='{sample_address}' order by date desc limit 1\").to_dataframe()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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            "text/plain": [
              "                                      address        date            balance\n",
              "0  0x9c92d74ae777fa12715f0033ba027ecb86bbff6a  2021-09-20  53957931308944491"
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "InDgw8CFmbAf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "d0fd1261-3a99-40e3-a6ee-4761be376b92"
      },
      "source": [
        "client.query(f\"select * from arcx_dev.stablecoin_balances_over_time where address='{sample_address}' order by date desc limit 3\").to_dataframe()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>2</th>\n",
              "      <td>0xa0b86991c6218b36c1d19d4a2e9eb0ce3606eb48</td>\n",
              "      <td>0x9c92d74ae777fa12715f0033ba027ecb86bbff6a</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>20000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                token_address  ...    balance\n",
              "0  0xa0b86991c6218b36c1d19d4a2e9eb0ce3606eb48  ...   20000000\n",
              "1  0xdac17f958d2ee523a2206206994597c13d831ec7  ...  407920813\n",
              "2  0xa0b86991c6218b36c1d19d4a2e9eb0ce3606eb48  ...   20000000\n",
              "\n",
              "[3 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2mwhwva6IFy7"
      },
      "source": [
        "looks like we have about 407 of usdt + 20 usdc + 0 DAI. adds up to the etherscan results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0QhFDilEIWSK"
      },
      "source": [
        "# Get ethereum prices through API"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_WMTiJCxMKrL"
      },
      "source": [
        "import requests\n",
        "import datetime\n",
        "from_date_epoch = 1420151457\n",
        "current_date_epoch = datetime.datetime.now()\n",
        "current_timestamp = datetime.datetime.now()\n",
        "etehreum_price_json = requests.get('https://api.coingecko.com/api/v3/coins/ethereum/market_chart/range?vs_currency=usd&from=1420151457&to=1632090657').json()\n"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dqr55SCOMKnE"
      },
      "source": [
        "import pandas as pd\n",
        "prices_df = pd.DataFrame.from_records(etehreum_price_json['prices'])\n",
        "prices_df.columns = ['epoch','price']"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nWerKXyoNUip"
      },
      "source": [
        "import datetime\n",
        "prices_df['date'] = prices_df['epoch'].apply(lambda x: datetime.datetime.fromtimestamp(x/1000))"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "34BaLJ7WOfVr",
        "outputId": "6b9c6348-f196-47de-f307-7ac2ee5bd7a6"
      },
      "source": [
        "epoch = datetime.datetime.utcfromtimestamp(0)\n",
        "current_time_in_ms = (datetime.datetime.now() - epoch).total_seconds() * 1000.0\n",
        "print(current_time_in_ms)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1632195545212.801\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OP3s2os9IilJ"
      },
      "source": [
        "# explore index data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5GOC3fY2IrmZ"
      },
      "source": [
        "eth_to_stable_index_df = client.query('''\n",
        "  select * from `arcx_dev.eth_to_stable_index`\n",
        "''').to_dataframe()"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vcWvZTO1kTz2"
      },
      "source": [
        "import pandas as pd\n",
        "eth_to_stable_index_df['stablecoin_usd_balance'] = pd.to_numeric(eth_to_stable_index_df['stablecoin_usd_balance'])"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mFNLbvJcM0x8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1fbc6e63-4cc2-44ed-9928-2ee0a0503e74"
      },
      "source": [
        "eth_to_stable_index_df['eth_to_stablecoin_ratio'].isnull().value_counts()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True     994810\n",
              "False    122821\n",
              "Name: eth_to_stablecoin_ratio, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3R-dXhm3NauP"
      },
      "source": [
        "per index definition, it does not apply when any eth_usd_balance or stablecoin_usd_balance is below 100. This is an arbitrary threshold to avoid corner cases, it should be fined tuned. \n",
        "\n",
        "~90% of the time the index is non-applicable for this batch of addresses"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xgtszKwLN1uf"
      },
      "source": [
        "valid_eth_to_stable_index_df = eth_to_stable_index_df[~eth_to_stable_index_df['eth_to_stablecoin_ratio'].isnull()]"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mQByIbjcN8l7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "f0dc0b9c-7982-431b-bded-a3bcd807fb4b"
      },
      "source": [
        "plt.hist(valid_eth_to_stable_index_df['eth_to_stablecoin_ratio'],bins=20,range=[0,10])\n",
        "plt.title('index distribution')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'index distribution')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RSSR-9CKM0R0"
      },
      "source": [
        "this looks like a power law.. most values lie under the 0-0.5 interval and then it drops exponentially."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1kPtXlkqcKTj"
      },
      "source": [
        "Let's cut on a specific date and see the distribution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QbEqgDZ5OzEG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "6a6523ce-a3d0-4000-c9bd-2708d554dc6f"
      },
      "source": [
        "import datetime\n",
        "valid_eth_to_stable_index_df[valid_eth_to_stable_index_df['date']==datetime.date(2021,9,19)]['eth_to_stablecoin_ratio'].hist(bins=20,range=[0,10])\n",
        "plt.title('distribution of index')"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'distribution of index')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z5gBgjaWoFev"
      },
      "source": [
        "Let's see top 10 - bottom 10 to get a better sense of their parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vtd-aUWScTkl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "bd0152bd-df22-4027-e092-1e755c34e50c"
      },
      "source": [
        "valid_eth_to_stable_index_df[valid_eth_to_stable_index_df['date']==datetime.date(2021,9,19)].sort_values('eth_to_stablecoin_ratio').head(10)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>address</th>\n",
              "      <th>date</th>\n",
              "      <th>stablecoin_usd_balance</th>\n",
              "      <th>eth_usd_balance</th>\n",
              "      <th>eth_to_stablecoin_ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>27313</th>\n",
              "      <td>0xcf407e4a4ff843859c41093a6d685f332fedfe15</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>8.929891e+06</td>\n",
              "      <td>5.59917e-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107493</th>\n",
              "      <td>0x0d24f692c05036602076b3f51242b5a34c55ee38</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>153.509705</td>\n",
              "      <td>2.332555e+06</td>\n",
              "      <td>6.58118e-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26300</th>\n",
              "      <td>0x60002173b8c04e143ec6297c88e11e3025ef2254</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>166.574412</td>\n",
              "      <td>9.841896e+05</td>\n",
              "      <td>0.00016925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94978</th>\n",
              "      <td>0x22fa8cc33a42320385cbd3690ed60a021891cb32</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>1762.613337</td>\n",
              "      <td>9.093480e+06</td>\n",
              "      <td>0.000193833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73536</th>\n",
              "      <td>0x00f282c40b92bed05f1776cadf1c8b96b9fbaee3</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>1241.417866</td>\n",
              "      <td>5.413296e+06</td>\n",
              "      <td>0.000229328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79060</th>\n",
              "      <td>0xcb33844b365c53d3462271cee9b719b6fc8ba06a</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>1148.305385</td>\n",
              "      <td>3.578497e+06</td>\n",
              "      <td>0.00032089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26963</th>\n",
              "      <td>0x7b23e38fbd0e677a1d1f72fe29a1aea9fd83ade8</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>623.035239</td>\n",
              "      <td>1.408085e+06</td>\n",
              "      <td>0.00044247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22566</th>\n",
              "      <td>0x5f681ad96ce4ad8b3d7d745ba1e34109afebdab8</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>1.904660e+05</td>\n",
              "      <td>0.000525028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107635</th>\n",
              "      <td>0x716034c25d9fb4b38c837afe417b7f2b9af3e9ae</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>3227.000111</td>\n",
              "      <td>4.011288e+06</td>\n",
              "      <td>0.00080448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36796</th>\n",
              "      <td>0x775d90be88bfc66aba77a551d489e43ce5a33112</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>6213.907928</td>\n",
              "      <td>6.110440e+06</td>\n",
              "      <td>0.00101693</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                           address  ... eth_to_stablecoin_ratio\n",
              "27313   0xcf407e4a4ff843859c41093a6d685f332fedfe15  ...             5.59917e-05\n",
              "107493  0x0d24f692c05036602076b3f51242b5a34c55ee38  ...             6.58118e-05\n",
              "26300   0x60002173b8c04e143ec6297c88e11e3025ef2254  ...              0.00016925\n",
              "94978   0x22fa8cc33a42320385cbd3690ed60a021891cb32  ...             0.000193833\n",
              "73536   0x00f282c40b92bed05f1776cadf1c8b96b9fbaee3  ...             0.000229328\n",
              "79060   0xcb33844b365c53d3462271cee9b719b6fc8ba06a  ...              0.00032089\n",
              "26963   0x7b23e38fbd0e677a1d1f72fe29a1aea9fd83ade8  ...              0.00044247\n",
              "22566   0x5f681ad96ce4ad8b3d7d745ba1e34109afebdab8  ...             0.000525028\n",
              "107635  0x716034c25d9fb4b38c837afe417b7f2b9af3e9ae  ...              0.00080448\n",
              "36796   0x775d90be88bfc66aba77a551d489e43ce5a33112  ...              0.00101693\n",
              "\n",
              "[10 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vvd6onl5j7BJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "8d472555-0694-4c09-cd31-94e8778e6465"
      },
      "source": [
        "valid_eth_to_stable_index_df[valid_eth_to_stable_index_df['date']==datetime.date(2021,9,19)].sort_values('eth_to_stablecoin_ratio',ascending=False).head(10)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>address</th>\n",
              "      <th>date</th>\n",
              "      <th>stablecoin_usd_balance</th>\n",
              "      <th>eth_usd_balance</th>\n",
              "      <th>eth_to_stablecoin_ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20465</th>\n",
              "      <td>0x0b4928839e6b77c3e162c67dbd4474574cac21ba</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>2.118060e+06</td>\n",
              "      <td>514.587122</td>\n",
              "      <td>4116.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70673</th>\n",
              "      <td>0xbeb90b87629892a9fe9d47df81b2f87ab7a9ba8f</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>3.984264e+05</td>\n",
              "      <td>135.475606</td>\n",
              "      <td>2940.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31835</th>\n",
              "      <td>0xdb7030beb1c07668aa49ea32fbe0282fe8e9d12f</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>9.999983e+06</td>\n",
              "      <td>18249.297446</td>\n",
              "      <td>547.965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96413</th>\n",
              "      <td>0x02c5c20c7aa2725dd244e3deb4bb3435e91e1d68</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>2.826744e+05</td>\n",
              "      <td>759.694774</td>\n",
              "      <td>372.089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94538</th>\n",
              "      <td>0x68af1f3f341a1e690d16e0b03f516866093972fd</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>1.057949e+05</td>\n",
              "      <td>340.397297</td>\n",
              "      <td>310.798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26918</th>\n",
              "      <td>0x2bd7d71390ed6946159b1cb49bc270cbe3ef1a53</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>6.488113e+04</td>\n",
              "      <td>255.410278</td>\n",
              "      <td>254.027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48531</th>\n",
              "      <td>0xa42830ee059c77caf8c8200b44aa9813cb0720c5</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>3.437409e+05</td>\n",
              "      <td>1489.387075</td>\n",
              "      <td>230.794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30102</th>\n",
              "      <td>0xfb7e3844348dc2eb31ec32f91aaa82fe5b7b2d71</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>4.000000e+04</td>\n",
              "      <td>238.155128</td>\n",
              "      <td>167.958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84386</th>\n",
              "      <td>0xcae2d5aed9f5e0075d49a4b5f492b4a73a64dc25</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>6.093164e+04</td>\n",
              "      <td>379.398242</td>\n",
              "      <td>160.601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114231</th>\n",
              "      <td>0xdca313c4df33c2142b2adf202d6abf4fa56e1d41</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>1.048991e+06</td>\n",
              "      <td>7763.581479</td>\n",
              "      <td>135.117</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                           address  ... eth_to_stablecoin_ratio\n",
              "20465   0x0b4928839e6b77c3e162c67dbd4474574cac21ba  ...                 4116.04\n",
              "70673   0xbeb90b87629892a9fe9d47df81b2f87ab7a9ba8f  ...                 2940.95\n",
              "31835   0xdb7030beb1c07668aa49ea32fbe0282fe8e9d12f  ...                 547.965\n",
              "96413   0x02c5c20c7aa2725dd244e3deb4bb3435e91e1d68  ...                 372.089\n",
              "94538   0x68af1f3f341a1e690d16e0b03f516866093972fd  ...                 310.798\n",
              "26918   0x2bd7d71390ed6946159b1cb49bc270cbe3ef1a53  ...                 254.027\n",
              "48531   0xa42830ee059c77caf8c8200b44aa9813cb0720c5  ...                 230.794\n",
              "30102   0xfb7e3844348dc2eb31ec32f91aaa82fe5b7b2d71  ...                 167.958\n",
              "84386   0xcae2d5aed9f5e0075d49a4b5f492b4a73a64dc25  ...                 160.601\n",
              "114231  0xdca313c4df33c2142b2adf202d6abf4fa56e1d41  ...                 135.117\n",
              "\n",
              "[10 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tHoeTtcBoQ8x"
      },
      "source": [
        "At high (>100) or  low (<0.001) values of  **eth_to_stablecoin_ratio** I found very imbalanced wallets either with a lot of ETH or a lot of stablecoins. Either case they look like *whales* holding a lot of value in assets. \n",
        "\n",
        "this means the index probably won't work well on this range because whales might behave very differently. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pK54flh1pUwU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "a5fff9f7-69a2-4499-847b-f8b93160387e"
      },
      "source": [
        "condition = (valid_eth_to_stable_index_df['date']==datetime.date(2021,5,19)) & (valid_eth_to_stable_index_df['eth_to_stablecoin_ratio']>0.1) & (valid_eth_to_stable_index_df['eth_to_stablecoin_ratio']<100)\n",
        "valid_eth_to_stable_index_df[condition]['eth_to_stablecoin_ratio'].hist(bins=20,range=[0.1,100])\n",
        "plt.title('distribution of index for snapshot')"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'distribution of index for snapshot')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8CUdHR7Rr184w3dXVFVevXjW6FKTG4cOHzfZvY2NTp7G3b98ejo6OOHnyJPLz803aay5V6t69u8V91kXNc7xnzx6z7TVnDz+s9ddVt27dAADp6elmX3/mnt+ePXsCAPbv31+ndS1evBjbtm3D0KFDkZ6eDmdnZ/z9739HTk7OA4zcWNOmTeHn54erV68iIyPDpP1h1r1Zs2Z48cUX8fnnn+Ovf/0rrl27hgMHDjxwf7/99hsAYNiwYSZt5p6P+7VXVVUhPT0dwB/P990UCgXat2+P119/Hfv370eTJk2MPkMeRM37wNwtkX/55Rfk5eUhICDAaG+7ru/1ho7BXU/UXL4yb948o2/k5eXl+OCDDyzuJz093ew32Zq9jbt/4qr5+f1+18g+qC+++MJw3KzGjBkzUFJSgpdfftnw8x4AhISEoKqqyuQ61507d+Lrr78223+zZs1w7dq1Wr+5/5mdnR3+9re/4datW3j//feNrt3NycnB/PnzoVAoDJcqSS0yMhLNmjXD5s2bsW/fPqO2devW4ciRI+jUqRNCQ0MfyvrrqmXLlujfvz9+//13w2VaNVJTU81+AXnxxRfh5+eHzz77DNu2bTPb77Fjx4xe44cPH8a7776LVq1aYe3atfD398dnn32G/Px8REVFSfKBHR0dDQD4xz/+YfTlsKioyPD+mjBhgtXrKS8vNxvMQgjk5eUBgFV3tKu518GfQ+/s2bN4991377lsWlqayfX3S5cuxYULF9C/f394eXkBAE6dOmX214nr16+jsrLS6jvy1TwXH3zwgdEhk8rKSkyfPh2A6XNR1/d6Q8dj3PVE7969MXnyZCxfvhydO3fGiBEjDNdxazQai29runDhQuzevRvPPPMM2rRpAxcXF2RlZSE1NRUODg5G10GGh4fDxsYGS5YswfXr1w3HACdPnizJz1TPPfccevfujdGjR+OJJ57Avn37kJ6eDl9fX8ybN89o3ilTpiAxMRGxsbFIS0tDmzZtkJGRgV27dmHYsGHYvHmzSf/9+/fHhg0b8Nxzz+Evf/kLmjRpgieffBKDBw+udUwJCQnYv38/Vq9ejWPHjiEiIsJwHfeNGzcwY8YMhISEWL3t5jg5OWHdunUYPnw4IiIiMHz4cPj6+uLEiRP49ttvodFosH79+gc+NPEwrFixAj179sRbb70FnU5nuI5769atGDx4MLZv3240v52dHZKTkw3Xb4eEhCAoKAjOzs64ePEijh07hjNnzuDYsWNo1qwZCgsLMXr0aAgh8PXXXxuOlUZFRSEtLQ1r1qxBfHw8Zs+ebdV2TJ8+Hd9//z2+//57dOnSBYMGDUJlZSW2bNmCy5cv4+9//ztGjhxp1ToAoLS0FM888wx8fX0RHByMVq1aobKyEj/++COOHz+O0NBQsydlWWrw4MHw8/PDokWLcPLkSXTr1g0XLlxAamoqXnjhhXt+CR80aBAiIyMNr7tjx47h+++/R7NmzbBixQrDfD/88APeeust9OzZEwEBAfDw8EBubi5SUlJQXV2N999//4HHDwB//etfsX37dmzYsAEdO3bE0KFDDddxZ2VloV+/fpg2bZrRMg/yXm/QHtfp7GSqurpaLF++XLRv317Y29uLFi1aiIkTJwq9Xm/2cilzl0nt3LlTjBs3TnTs2FGo1Wrh4OAg/Pz8xIQJE8Tp06dN1rlhwwYRFBQkHBwcDJe/nDt3Tghx/8vF7nc52J49e8SaNWtEYGCgUKlUonnz5mL8+PEiNzfXbH/p6emib9++wtHRUTg7O4u+ffuKffv21Xo5WF5envjb3/4mnnjiCWFjY2MyFtRyaZBerxfvv/++CAgIEPb29qJp06YiLCxMbNmypdZtrO1yrJptramZJf773/+K4cOHi+bNmwulUik8PT3F2LFjRXZ2dp3XXxtzy9zv+axtPb/++qsYPny4UKvVwtHRUYSGhorU1NRanxch7lyvHBcXJ7p06SIcHR2Fg4OD8PX1FYMHDxarV68Wt27dEkIIMXz4cLPXjwshRElJiejYsaOwsbERu3fvvu82369WZWVlIiEhQXTp0kWoVCrh6OgoevToIVatWmX2umg8wCVIFRUVYuHChWLgwIHCx8dHqFQq4ebmJrp37y7+9a9/mVzbXXM5mLnXz549ewQAMXPmTKPpFy5cEFFRUcLT01OoVCrRsWNHsWDBAlFZWXnf5z0lJUWEhIQIR0dHodFoxIgRI8Svv/5qNH9GRoaYNm2aCA4OFs2bNxf29vbC29tbDBo0SOzatcto3gd9f9y+fVt89tln4qmnnhKOjo5CpVKJwMBAsXDhQrOXAN7vvd7YKIQwc68/IiJqEOT+VwDJFI9xExERyQiDm4iISEYY3ERERDLCY9xEREQywj1uIiIiGWFwExERyYhsbsAixa0Pa2i1WrO3vCTLsYbWYw2lwTpajzW0ntQ19PT0rLWNe9xEREQywuAmIiKSEQY3ERGRjDC4iYiIZITBTUREJCMWnVWenJyMQ4cOIScnB0qlEv7+/oiKioKPj49hHiEENm3ahN27d6O4uBj+/v6Ijo6Gt7e3YZ7i4mIkJibiyJEjAIDg4GCMHz8eTk5OEm8WERFRw2TRHndGRgYGDBiAOXPmYObMmbC1tcWcOXOM/gh6SkoKUlNTMW7cOMyfPx8uLi746KOPjP7w+bJly3Du3DnExcUhLi4O586dw/Lly6XfKiIiogbKouCOi4tD37594ePjAx8fH0yePBlFRUXIzMwEcGdve8eOHYiMjERoaCh8fHwQGxuL0tJSHDhwAABw6dIlHD9+HDExMQgICEBAQABiYmJw9OhRSa/RJiIiasge6AYspaWlEELA2dkZAJCXlwe9Xo/AwEDDPPb29ujQoQPOnDmD/v37IysrCyqVCu3atTPM065dOzRp0gRnzpwxudhcp9NBp9MBABISEqDVah9kqGYplUpJ+2uMWEPrsYbSYB2txxpa71HW8IGCOzExEa1bt0ZAQAAAQK/XAwA0Go3RfGq1GgUFBYZ5XFxcoFAoDO0KhQJqtdqw/N0iIiIQERFheCzlHWl4lyDrsYbWYw2lwTpajzW0Xr2+c9rnn3+OM2fO4B//+AdsbHhSOhER0aNUp+Rdt24dDh48iBkzZsDDw8MwvWZP+897zoWFhVCr1YZ5ioqKcPdfERVCoLCw0GRPnYiIiMyzOLgTExMNoe3l5WXU5u7uDo1GgxMnThimVVRUIDMz03BMOyAgAGVlZcjKyjLMk5WVhfLycqPj3kRERFQ7i45xr169Gvv378fbb78NZ2dnw561SqWCSqWCQqHA888/j+TkZHh5eaFFixbYunUrVCoVnn76aQBAy5Yt0bVrV6xcuRKvvfYaAGDlypUICgq652/5D8PVob0k79N21TbJ+yQiIvozi4J7165dAIDZs2cbTR8xYgRGjRoFABgyZAgqKiqwZs0alJSUwM/PD3FxcXBwcDDMP2XKFKxduxZz584FAHTv3h3R0dGSbAgREVFjoBB3H3Sux6S81vv2qy9K1leNxrbHzbNQrccaSoN1tB5raL16fVY5ERERPT4MbiIiIhlhcBMREckIg5uIiEhGGNxEREQywuAmIiKSEQY3ERGRjDC4iYiIZITBTUREJCMMbiIiIhlhcBMREckIg5uIiEhGGNxEREQywuAmIiKSEQY3ERGRjDC4iYiIZITBTUREJCMMbiIiIhlhcBMREckIg5uIiEhGGNxEREQywuAmIiKSEQY3ERGRjDC4iYiIZITBTUREJCMMbiIiIhlhcBMREckIg5uIiEhGlJbMlJGRge3bt+Ps2bMoKCjAxIkT0adPH0P7qFGjzC43YMAATJgwAQCwYsUK7N2716jd398fc+fOfcChExERNT4WBXdZWRm8vb0RFhaGTz/91KR95cqVRo+zs7OxYMEC9OrVy2h6ly5dMHny5D9WrrRo9URERPT/LErOoKAgBAUFAbiz5/xnGo3G6PHhw4fRokULdOzY0Wi6nZ2dybxERERkOcl3ecvKypCeno4RI0aYtGVmZmLChAlwcnJChw4d8NJLL0GtVks9BCIiogZL8uA+cOAAqqqqjI6BA0DXrl0REhICd3d35OXlYePGjZg9ezYSEhJgZ2dn0o9Op4NOpwMAJCQkQKvVSjbGq5L19AcpxycHSqWy0W2z1FhDabCO1mMNrfcoayh5cO/evRvBwcFwcXExmt67d2/D/318fODr64tJkybh6NGjCAkJMeknIiICERERhsf5+flSD1VS9X18UtNqtY1um6XGGkqDdbQea2g9qWvo6elZa5ukl4OdP38e2dnZRoFbGzc3N7i5ueHKlStSDoGIiKhBkzS4dTod3N3d0aVLl/vOW1RUhBs3bsDV1VXKIRARETVoFl8OlpubCwAQQiA/Px/nz5+Hs7Oz4Tf98vJy7N+/H0OGDIFCoTBZPikpCaGhodBoNLh27Ro2bNgAtVqNHj16SLxJREREDZdFwZ2dnY34+HjD46SkJCQlJSEsLAyTJk0CAKSnp6O8vNzkpDQAsLGxwcWLF7Fv3z6UlJTA1dUVnTp1wrRp0+Dg4CDNlhARETUCCiGEeNyDsEROTo5kfd1+9UXJ+qphu2qb5H3WZzyZxXqsoTRYR+uxhtaT7clpRERE9HAxuImIiGSEwU1ERCQjDG4iIiIZYXATERHJCIObiIhIRhjcREREMsLgJiIikhEGNxERkYwwuImIiGSEwU1ERCQjDG4iIiIZYXATERHJCIObiIhIRhjcREREMsLgJiIikhEGNxERkYwwuImIiGSEwU1ERCQjDG4iIiIZYXATERHJCIObiIhIRhjcREREMsLgJiIikhEGNxERkYwwuImIiGSEwU1ERCQjDG4iIiIZUVoyU0ZGBrZv346zZ8+ioKAAEydORJ8+fQztK1aswN69e42W8ff3x9y5cw2PKysr8cUXX+DgwYOoqKhA586dMWHCBDRr1kyaLSEiImoELArusrIyeHt7IywsDJ9++qnZebp06YLJkyf/0bHSuOt169bhyJEjePPNN+Hs7Iz169cjISEBCxYsgI0Nd/yJiIgsYVFiBgUFISoqCqGhoVAoFGbnsbOzg0ajMfxzdnY2tN26dQtpaWl45ZVXEBgYCF9fX8TGxuLChQs4ceKENFtCRETUCFi0x22JzMxMTJgwAU5OTujQoQNeeuklqNVqAMDZs2dx+/ZtPPnkk4b5tVotvLy8kJWVha5du0o1DCIiogZNkuDu2rUrQkJC4O7ujry8PGzcuBGzZ89GQkIC7OzsoNfrYWNjg6ZNmxotp1arodfrzfap0+mg0+kAAAkJCdBqtVIMFQBwVbKe/iDl+ORAqVQ2um2WGmsoDdbReqyh9R5lDSUJ7t69exv+7+PjA19fX0yaNAlHjx5FSEjIA/UZERGBiIgIw+P8/Hyrx/kw1ffxSU2r1Ta6bZYaaygN1tF6rKH1pK6hp6dnrW0P5awwNzc3uLm54cqVKwAAjUaD6upq3Lx502i+wsJCaDSahzEEIiKiBumhBHdRURFu3LgBV1dXAICvry9sbW2NTkS7fv06Ll++jICAgIcxBCIiogbJ4svBcnNzAQBCCOTn5+P8+fNwdnaGs7MzkpKSEBoaCo1Gg2vXrmHDhg1Qq9Xo0aMHAMDR0RHh4eH48ssv4eLigqZNm2L9+vXw8fFBYGDgw9s6IiKiBsai4M7OzkZ8fLzhcVJSEpKSkhAWFoZXX30VFy9exL59+1BSUgJXV1d06tQJ06ZNg4ODg2GZsWPHwtbWFkuWLDHcgGXSpEm8hpuIiKgOFEII8bgHYYmcnBzJ+rr96ouS9VXDdtU2yfusz3gyi/VYQ2mwjtZjDa0n+5PTiIiI6OFgcBMREckIg5uIiEhGGNxEREQywuAmIiKSEQY3ERGRjDC4iYiIZITBTUREJCMMbiIiIhlhcBMREckIg5uIiEhGGNxEREQywuAmIiKSEQY3ERGRjDC4iYiIZITBTUREJCMMbiIiIhlhcBMREckIg5uIiEhGGNxEREQywuAmIiKSEQY3ERGRjDC4iYiIZITBTUREJCMMbiIiIhlhcBMREckIg5uIiEhGGNxEREQywuAmIiKSEaUlM2VkZGD79u04e/YsCkos1nIAABhASURBVAoKMHHiRPTp0wcAUFVVha+//hrHjx/H1atX4eDggE6dOuHll1+GVqs19DFr1ixkZGQY9durVy9MnTpVuq0hIiJq4CwK7rKyMnh7eyMsLAyffvqpUVtFRQXOnTuHYcOGoXXr1rh16xbWr1+PuXPn4pNPPoGtra1h3j59+iAqKsrw2N7eXqLNICIiahwsCu6goCAEBQUBAFasWGHU5ujoiH/+859G02JiYjB9+nRcvnwZPj4+hulNmjSBRqOxdsxERESNlkXBXVe3bt0CADg5ORlNT09PR3p6OtRqNbp27YqRI0fCwcHhYQyBiIioQZI8uKuqqvDFF1+ge/fuaNasmWH6008/Da1WCzc3N1y8eBEbNmzAhQsX8OGHH5rtR6fTQafTAQASEhKMjpdb66pkPf1ByvHJgVKpbHTbLDXWUBqso/VYQ+s9yhpKGty3b9/GsmXLUFJSgnfeeceoLSIiwvB/Hx8feHh44IMPPsDZs2fh6+tr0ldERITRMvn5+VIOVXL1fXxS02q1jW6bpcYaSoN1tB5raD2pa+jp6Vlrm2SXg92+fRtLly7FhQsXMGPGDDRt2vSe8/v6+sLGxga5ublSDYGIiKjBk2SPu6qqCkuWLMHFixcxa9Ysi05Au3DhAqqrq3myGhERUR1YfDlYzZ6xEAL5+fk4f/48nJ2d4erqikWLFiE7OxvvvvsuFAoF9Ho9gDtnnNvb2yM3NxcHDhxAt27d0LRpU1y6dAlffPEF2rRpg/bt2z+8rSMiImpgLAru7OxsxMfHGx4nJSUhKSkJYWFhGDlyJI4cOQIAeO+994yWq7lRi1KpxMmTJ7Fjxw6UlZWhWbNmCAoKwsiRI2Fjw5u3ERERWcqi4O7UqROSkpJqbb9XG3DnoP3dwU9EREQPhru7REREMsLgJiIikhEGNxERkYwwuImIiGSEwU1ERCQjDG4iIiIZYXATERHJCIObiIhIRhjcREREMsLgJiIikhEGNxERkYwwuImIiGSEwU1ERCQjDG4iIiIZYXATERHJCIObiIhIRhjcREREMsLgJiIikhEGNxERkYwwuImIiGSEwU1ERCQjDG4iIiIZYXATERHJCIObiIhIRhjcREREMsLgJiIikhEGNxERkYwwuImIiGREaclMGRkZ2L59O86ePYuCggJMnDgRffr0MbQLIbBp0ybs3r0bxcXF8Pf3R3R0NLy9vQ3zFBcXIzExEUeOHAEABAcHY/z48XBycpJ2i4iIiBowi/a4y8rK4O3tjXHjxsHe3t6kPSUlBampqRg3bhzmz58PFxcXfPTRRygtLTXMs2zZMpw7dw5xcXGIi4vDuXPnsHz5cum2hIiIqBGwKLiDgoIQFRWF0NBQKBQKozYhBHbs2IHIyEiEhobCx8cHsbGxKC0txYEDBwAAly5dwvHjxxETE4OAgAAEBAQgJiYGR48eRU5OjvRbRURE1EBZfYw7Ly8Per0egYGBhmn29vbo0KEDzpw5AwDIysqCSqVCu3btDPO0a9cOTZo0McxDRERE92fRMe570ev1AACNRmM0Xa1Wo6CgwDCPi4uL0d66QqGAWq02LP9nOp0OOp0OAJCQkACtVmvtUA2uStbTH6QcnxwolcpGt81SYw2lwTpajzW03qOsodXB/bBEREQgIiLC8Dg/P/8xjub+6vv4pKbVahvdNkuNNZQG62g91tB6UtfQ09Oz1jarfyqv2dP+855zYWEh1Gq1YZ6ioiIIIQztQggUFhaa7KkTERFR7awObnd3d2g0Gpw4ccIwraKiApmZmYZj2gEBASgrK0NWVpZhnqysLJSXlxsd9yYiIqJ7s+in8rKyMuTm5gK4s6ecn5+P8+fPw9nZGVqtFs8//zySk5Ph5eWFFi1aYOvWrVCpVHj66acBAC1btkTXrl2xcuVKvPbaawCAlStXIigo6J4/BxAREZExi4I7Ozsb8fHxhsdJSUlISkpCWFgYJk2ahCFDhqCiogJr1qxBSUkJ/Pz8EBcXBwcHB8MyU6ZMwdq1azF37lwAQPfu3REdHS3x5hARETVsCnH3ged6TMrrvW+/+qJkfdWwXbVN8j7rM57MYj3WUBqso/VYQ+vJ6uQ0IiIienQY3ERERDLC4CYiIpIRBjcREZGMMLiJiIhkhMFNREQkIwxuIiIiGWFwExERyQiDm4iISEYY3ERERDLC4CYiIpIRBjcREZGMMLiJiIhkhMFNREQkIwxuIiIiGWFwExERyQiDm4iISEYY3ERERDLC4CYiIpIRBjcREZGMMLiJiIhkhMFNREQkIwxuIiIiGWFwExERyQiDm4iISEYY3ERERDLC4CYiIpIRBjcREZGMMLiJiIhkRClFJ5MmTcK1a9dMpnfr1g3vv/8+kpKSsHnzZqM2tVqNVatWSbF6IiKiRkOS4J4/fz6qq6sNjwsKCvDee++hZ8+ehmmenp6YNWuW4bGNDXf2iYiI6kqS4HZxcTF6nJaWBgcHB/Tq1cswzdbWFhqNRorVERERNVqSBPfdhBBIS0vDM888A3t7e8P0q1ev4rXXXoNSqYS/vz9eeukleHh4SL16IiKiBk0hhBBSdvjzzz9j7ty5WLhwIVq3bg0AOHbsGEpLS+Hl5YXCwkJs3boVly9fxqJFi9C0aVOz/eh0Ouh0OgBAQkICKioqJBvj1aG97j9THXkkp0veZ32mVCpRVVX1uIcha6yhNFhH67GG1pO6hnfv+JqsS7K1/L/du3ejbdu2htAG7pykdreAgADExsZi7969GDRokNl+IiIiEBERYXicn58v9VAlVd/HJzWtVtvotllqrKE0WEfrsYbWk7qGnp6etbZJeoZYYWEhDh8+jH79+t1zPpVKBW9vb1y5ckXK1RMRETV4kgb3jz/+CDs7Ozz99NP3nK+iogKXL1+Gq6urlKsnIiJq8CT7qbzmpLRevXpBpVIZta1fvx7BwcHQarUoLCzEli1bUF5ejrCwMKlWT0RE1ChIFtynTp3ClStXMHnyZJO2GzduYOnSpSgqKoKLiwv8/f0xd+5cNG/eXKrVExERNQqSBXfnzp2RlJRktm3q1KlSrYaIiKhR4+3LiIiIZITBTUREJCMMbiIiIhlhcBMREckIg5uIiEhGGNxEREQywuAmIiKSEQY3ERGRjDC4iYiIZITBTUREJCMMbiIiIhlhcBMREckIg5uIiEhGGNxEREQywuAmIiKSEQY3ERGRjDC4iYiIZITBTUREJCMMbiIiIhlhcBMREckIg5uIiEhGGNxEREQywuAmIiKSEQY3ERGRjDC4iYiIZITBTUREJCMMbiIiIhlhcBMREcmIUopOkpKSsHnzZqNparUaq1atAgAIIbBp0ybs3r0bxcXF8Pf3R3R0NLy9vaVYPRERUaMhSXADgKenJ2bNmmV4bGPzx858SkoKUlNTMXHiRHh6emLz5s346KOPsGTJEjg4OEg1BCIiogZPsp/KbW1todFoDP9cXFwA3Nnb3rFjByIjIxEaGgofHx/ExsaitLQUBw4ckGr1REREjYJke9xXr17Fa6+9BqVSCX9/f7z00kvw8PBAXl4e9Ho9AgMDDfPa29ujQ4cOOHPmDPr37y/VEIiIiBo8SYLb398fEydOhJeXFwoLC7F161Z8+OGHWLRoEfR6PQBAo9EYLaNWq1FQUFBrnzqdDjqdDgCQkJAArVYrxVABAFcl6+kPUo5PDpRKZaPbZqmxhtJgHa3HGlrvUdZQkuDu1q2b0eOAgADExsZi79698Pf3f6A+IyIiEBERYXicn59v1Rgftvo+PqlptdpGt81SYw2lwTpajzW0ntQ19PT0rLXtoVwOplKp4O3tjStXrhj2tGv2vGsUFhZCrVY/jNUTERE1WA8luCsqKnD58mW4urrC3d0dGo0GJ06cMGrPzMxEu3btHsbqiYiIGixJfipfv349goODodVqUVhYiC1btqC8vBxhYWFQKBR4/vnnkZycDC8vL7Ro0QJbt26FSqXC008/LcXqiYiIGg1JgvvGjRtYunQpioqK4OLiAn9/f8ydOxfNmzcHAAwZMgQVFRVYs2YNSkpK4Ofnh7i4OF7DTUREVEeSBPfUqVPv2a5QKDBq1CiMGjVKitURERE1WrxXORERkYwwuImIiGSEwU1ERCQjDG4iIiIZYXATERHJCIObiIhIRhjcREREMsLgJiIikhEGNxERkYwwuImIiGSEwU1ERCQjDG4iIiIZYXATERHJCIObiIhIRhjcREREMsLgJiIikhEGNxERkYwwuImIiGSEwU1ERCQjDG4iIiIZYXATERHJCIObiIhIRhjcREREMsLgJiIikhEGNxERkYwwuImIiGSEwU1ERCQjDG4iIiIZUUrRSXJyMg4dOoScnBwolUr4+/sjKioKPj4+hnlWrFiBvXv3Gi3n7++PuXPnSjEEIiKiRkGS4M7IyMCAAQPQtm1bAMDGjRsxZ84cLF68GM7Ozob5unTpgsmTJ/+xcqUkqyciImo0JEnOuLg4o8eTJ0/GmDFjkJmZieDgYMN0Ozs7aDQaKVZJRETUKD2UXd7S0lIIIYz2tgEgMzMTEyZMgJOTEzp06ICXXnoJarX6YQyBiIioQVIIIYTUnS5atAi5ublISEiAjc2d898OHjyIJk2awN3dHXl5edi4cSOqq6uRkJAAOzs7kz50Oh10Oh0AICEhARUVFZKN7+rQXpL1VcMjOV3yPuszpVKJqqqqxz0MWWMNpcE6Wo81tJ7UNbS3t6+1TfLg/vzzz5Geno7Zs2fDw8Oj1vlu3LiBSZMmYerUqQgJCblvvzk5OZKN8farL0rWVw3bVdsk77M+02q1yM/Pf9zDkDXWUBqso/VYQ+tJXUNPT89a2yS9HGzdunU4ePAgZsyYcc/QBgA3Nze4ubnhypUrUg6BiIioQZMsuBMTEw2h7eXldd/5i4qKcOPGDbi6uko1BCIiogZPkpPTVq9ejf379+Ptt9+Gs7Mz9Ho9AEClUkGlUqGsrAxJSUkIDQ2FRqPBtWvXsGHDBqjVavTo0UOKIRARETUKkgT3rl27AACzZ882mj5ixAiMGjUKNjY2uHjxIvbt24eSkhK4urqiU6dOmDZtGhwcHKQYAhERUaMgSXAnJSXds93e3t7kWm8iIiKqO96rnIiISEYY3ERERDLC4CYiIpIR/pUPiUh9U5fGdkMXIiKyDPe4iYiIZITBTUREJCMMbiIiIhlhcBMREckIg5uIiEhGGNxEREQywuAmIiKSEQY3ERGRjDC4iYiIZITBTUREJCMMbiIiIhnhvcobCanvpY7kdGn7IyIii3CPm4iISEYY3ERERDLC4CYiIpIRBjcREZGM8OS0ekryk8mIiKhB4B43ERGRjHCPmx7I1aG9JO/TdtU2yfskImpouMdNREQkIwxuIiIiGeFP5dRgSX2CH3/KJ6L6gHvcREREMsLgJiIikhH+VE5kIal/er8K/vzeGDyMezLwddO4PfLg3rlzJ7Zt2wa9Xo+WLVti7Nix6NChw6MeBhERkSw90uBOT0/HunXrEB0djfbt22PXrl2YN28eFi9eDK1W+yiHQvVQY7xbXH0/gU4Wzwn/xCw9ADn/qeNHGtypqakICwtDREQEAGD8+PE4fvw4du3ahaioqEc5FCIis2TxZUViD+OGSvTwPLKT06qqqnD27Fk8+eSTRtMDAwNx5syZRzUMIiIiWXtke9xFRUWorq6GWq02mq7RaHDy5EmT+XU6HXQ6HQAgISEBnp6e0g3m2yPS9UXUkMnkvcLPBys1xm1+CCR9Hd5Dvb0cLCIiAgkJCUhISJC87/fee0/yPhsb1tB6rKE0WEfrsYbWe5Q1fGTB7eLiAhsbGxQWFhpN1+v10Gg0j2oYREREsvbIglupVMLX1xcnTpwwmn7y5Em0a9fuUQ2DiIhI1mxnzZo161GtzMHBAUlJSXB1dYW9vT22bNmCjIwMTJw4EU5OTo9qGAAAX1/fR7q+hog1tB5rKA3W0XqsofUeVQ0VQgjxSNb0/2puwFJQUABvb2+MGTMGHTt2fJRDICIikq1HHtxERET04OrtWeVERERkqlH9kRHeJ91yycnJOHToEHJycqBUKuHv74+oqCj4+PgY5hFCYNOmTdi9ezeKi4vh7++P6OhoeHt7P8aR11/Jycn46quv8OyzzyI6OhoAa2ipgoIC/Oc//8GxY8dQVlYGd3d3vPrqq4bDbKzjvVVXVyMpKQn79+83XMnzzDPPYOTIkbC1tQXAGv5ZRkYGtm/fjrNnz6KgoAATJ05Enz59DO2W1Ku4uBiJiYk4cuTOdfLBwcEYP3681ed0NZo97pr7pA8dOhQLFixAu3btMG/ePOTn5z/uodVLGRkZGDBgAObMmYOZM2fC1tYWc+bMQXFxsWGelJQUpKamYty4cZg/fz5cXFzw0UcfobS09DGOvH7KysqCTqdDq1atjKazhvdXUlKCf/7znwCA999/H4sXL8b48ePh4uJimId1vLdvvvkGO3fuxLhx47B48WKMGzcOO3fuRHJysmEe1tBYWVkZvL29MW7cONjb25u0W1KvZcuW4dy5c4iLi0NcXBzOnTuH5cuXWz22RhPcd98nvWXLlhg/fjxcXV2xa9euxz20eikuLg59+/aFj48PfHx8MHnyZBQVFSEzMxPAnW+bO3bsQGRkJEJDQ+Hj44PY2FiUlpbiwIEDj3n09cutW7ewfPlyvPHGG0bftFlDy6SkpMDV1RWxsbHw8/ODu7s7unTpgpYtWwJgHS2RlZWF7t27Izg4GO7u7ggODkb37t3x22+/AWANzQkKCkJUVBRCQ0OhUCiM2iyp16VLl3D8+HHExMQgICAAAQEBiImJwdGjR5GTk2PV2BpFcPM+6dYrLS2FEALOzs4AgLy8POj1egQGBhrmsbe3R4cOHVjTP/n3v/+NkJAQdO7c2Wg6a2iZw4cPw8/PD4sXL8aECRPw9ttv4/vvv0fNebWs4/21b98ep06dwuXLlwHcCZVTp06hW7duAFjDurKkXllZWVCpVEb3KWnXrh2aNGlidU0bxTHuut4nnUwlJiaidevWCAgIAHDnjncATO56p1arUVBQ8MjHV1/pdDrk5uZi8uTJJm2soWXy8vKwa9cuvPDCC4iMjMT58+exdu1aAMBzzz3HOlpgyJAhKC0txfTp02FjY4Pbt29j2LBhePbZZwHwtVhXltRLr9fDxcXFaG9doVBArVYbln9QjSK4yTqff/45zpw5g9mzZ8PGplH8SCOJnJwcfPXVV5gzZw6USr7VHlR1dTXatm1r+NO/bdq0wZUrV7Bz504899xzj3l08pCeno59+/ZhypQp8Pb2xvnz55GYmAh3d3eEh4c/7uFRHTWKTxPeJ/3BrVu3Dunp6Zg5cyY8PDwM02vqptfrodVqDdMLCwtNftlorLKysnDz5k1Mnz7dMK26uhqnT5/GDz/8gEWLFgFgDe/H1dXVcDy7RsuWLfHdd98B4GvREl9++SUGDx6M3r17AwB8fHxw7do1JCcnIzw8nDWsI0vqpdFoUFRUBCGEYa9bCIHCwkKrc6dR7D7xPukPJjExEQcPHsSMGTPg5eVl1Obu7g6NRmNU04qKCmRmZrKm/++pp57CJ598goULFxr+tW3bFr169cLChQvRokUL1tAC7dq1MzmZJycnx/CBydfi/ZWXl5v8WmZjY2M4T4A1rBtL6hUQEICysjJkZWUZ5snKykJ5ebnVNX2k9yp/nOrTfdLlYPXq1di3bx+mT58OrVaLsrIylJWVAbjzRUihUOD27dv45ptv4Onpierqaqxfvx56vR4xMTGws7N7zFvw+Nnb20OtVhv9O3DgAJo3b46+ffuyhhbSarXYvHkzbGxs4OrqipMnT+Lrr79GZGQk/Pz8WEcLXLp0Cfv27YOnpydsbW1x6tQpfPXVV+jduzeefPJJ1tCMsrIyXLp0CXq9HmlpafDx8YGjoyOqqqrg5OR033q5uLjg119/xYEDB9CmTRtcv34dK1euhJ+fHwYOHGjV2BrVLU95n3TLjRo1yuz0ESNGGNpqbkCg0+lQUlICPz8/REdHG92khYzNmjUL3t7eJjdgYQ3v7ejRo/jqq68Me9rPPvssBg4caPQTJOtYu9LSUmzcuBGHDh1CYWEhXF1d0atXL4wYMcJwjTJraOzUqVOIj483mR4WFoZJkyZZVK/i4mKsXbsW//3vfwEA3bt3R3R0tNU7i40quImIiOSuURzjJiIiaigY3ERERDLC4CYiIpIRBjcREZGMMLiJiIhkhMFNREQkIwxuIiIiGWFwExERyQiDm4iISEb+D/V4K4bPKdQcAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tsguQMTDtbWS"
      },
      "source": [
        "Still under this range we find the distribution to be skewed. Looks like its more frequent to have eth>>stable than the other way around. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yJXKRN1et3sc"
      },
      "source": [
        "Let's track the index across time for a couple of addresses:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4F6MasdRt9US",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "outputId": "304f2a6a-5ba8-4bfd-d365-ab2a7f14fc64"
      },
      "source": [
        "valid_eth_to_stable_index_df[valid_eth_to_stable_index_df['address']==sample_address].sort_values(by='date').plot(x='date',y='eth_to_stablecoin_ratio',title='index across time')"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2d96f6d4d0>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7TNJg06AujiT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "outputId": "08ac2445-ee24-46de-85be-96d09afed62a"
      },
      "source": [
        "valid_eth_to_stable_index_df[valid_eth_to_stable_index_df['address']=='0xc124e47aa04fa869aa4a2f6fcef717b709562dba'].sort_values(by='date').plot(x='date',y='eth_to_stablecoin_ratio',title='index across time')"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2d947fd110>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qu0iaVPZvgDV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "outputId": "ce5c4015-c59b-4ef0-d108-52135dda3b68"
      },
      "source": [
        "valid_eth_to_stable_index_df[valid_eth_to_stable_index_df['address']=='0x40c839b831c90173dc7fbce49a25274a4688ddd9'].sort_values(by='date').plot(x='date',y='eth_to_stablecoin_ratio',title='index across time')"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2d9496e450>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GhDxdGsrvnML"
      },
      "source": [
        "* might these charts reflect a particular strategy?\n",
        "* are addresses that mantain a particular ratio across time more trustable?\n",
        "* there are probably more features to be derived from aggregating this index across different time windows."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_WhtIAQJwlIl"
      },
      "source": [
        "# use case\n",
        "\n",
        "We'll measure the index at the time of the borrow events and explore its distribution for the liquidated vs no liquidated wallets.\n",
        "\n",
        "* for addresses with liquidation events, we pick the last borrow.\n",
        "* for addresses with no liquidations so far, we pick the last borrow as well."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b3Mhxzz77aMF"
      },
      "source": [
        "join_eth_to_stable_ratio = '''with last_borrow_compound as (\n",
        "    SELECT *,row_number() over( partition by borrower order by block_timestamp desc) as rank FROM `arcx_dev.compound_usdc_sample_address` \n",
        ")\n",
        "select * from last_borrow_compound \n",
        "inner join `arcx_dev.eth_to_stable_index` eth_ratio \n",
        "on eth_ratio.address=last_borrow_compound.borrower and eth_ratio.date=cast(last_borrow_compound.block_timestamp as date)\n",
        "where rank=1\n",
        "order by block_timestamp desc\n",
        "'''\n",
        "\n",
        "eth_to_stable_df = client.query(join_eth_to_stable_ratio).to_dataframe()"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a2tHg5we8Cy3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "outputId": "a0dd002a-b69c-46fd-cca2-e2db3a8efad3"
      },
      "source": [
        "eth_to_stable_df"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>block_timestamp</th>\n",
              "      <th>block_number</th>\n",
              "      <th>transaction_hash</th>\n",
              "      <th>log_index</th>\n",
              "      <th>contract_address</th>\n",
              "      <th>borrower</th>\n",
              "      <th>borrowAmount</th>\n",
              "      <th>accountBorrows</th>\n",
              "      <th>totalBorrows</th>\n",
              "      <th>has_liquidate_event</th>\n",
              "      <th>rk</th>\n",
              "      <th>rank</th>\n",
              "      <th>address</th>\n",
              "      <th>date</th>\n",
              "      <th>stablecoin_usd_balance</th>\n",
              "      <th>eth_usd_balance</th>\n",
              "      <th>eth_to_stablecoin_ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-09-19 22:05:22+00:00</td>\n",
              "      <td>13258798</td>\n",
              "      <td>0xc7cc6954400cb92e879f464fd16793e6bae4ecd14f3b...</td>\n",
              "      <td>120</td>\n",
              "      <td>0x39aa39c021dfbae8fac545936693ac917d5e7563</td>\n",
              "      <td>0x9c92d74ae777fa12715f0033ba027ecb86bbff6a</td>\n",
              "      <td>20000000</td>\n",
              "      <td>20000000</td>\n",
              "      <td>3600312935234503</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0x9c92d74ae777fa12715f0033ba027ecb86bbff6a</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>427.920813</td>\n",
              "      <td>184.945356</td>\n",
              "      <td>2.313769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021-09-19 19:52:29+00:00</td>\n",
              "      <td>13258172</td>\n",
              "      <td>0xac5b3dbbbf507413db16d4ca3fb44e978a031f2dc60a...</td>\n",
              "      <td>471</td>\n",
              "      <td>0x39aa39c021dfbae8fac545936693ac917d5e7563</td>\n",
              "      <td>0x8b51980087898a3882f9e46ebe83bb600e4c8e8e</td>\n",
              "      <td>10000000000</td>\n",
              "      <td>97058154070</td>\n",
              "      <td>3600421141098809</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0x8b51980087898a3882f9e46ebe83bb600e4c8e8e</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>0</td>\n",
              "      <td>261.167656</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-09-19 18:43:39+00:00</td>\n",
              "      <td>13257838</td>\n",
              "      <td>0x58f3e8e3dc7265db2a8f7d973800bcfeb238beffed7b...</td>\n",
              "      <td>158</td>\n",
              "      <td>0x39aa39c021dfbae8fac545936693ac917d5e7563</td>\n",
              "      <td>0x41d5c91836cb9fbbb644c30ac4278befec52caee</td>\n",
              "      <td>217417521900</td>\n",
              "      <td>1213956014857</td>\n",
              "      <td>3600467814671001</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0x41d5c91836cb9fbbb644c30ac4278befec52caee</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>17407.5219</td>\n",
              "      <td>205.364294</td>\n",
              "      <td>84.764112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021-09-19 17:17:53+00:00</td>\n",
              "      <td>13257471</td>\n",
              "      <td>0x56eac66a66985d0e9ad932f0e2100bee480fb39ad3fd...</td>\n",
              "      <td>27</td>\n",
              "      <td>0x39aa39c021dfbae8fac545936693ac917d5e7563</td>\n",
              "      <td>0x40c839b831c90173dc7fbce49a25274a4688ddd9</td>\n",
              "      <td>460000000000</td>\n",
              "      <td>671219728319</td>\n",
              "      <td>3600395811760379</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0x40c839b831c90173dc7fbce49a25274a4688ddd9</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>7084.536606</td>\n",
              "      <td>345601.794356</td>\n",
              "      <td>0.020499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021-09-19 10:18:42+00:00</td>\n",
              "      <td>13255559</td>\n",
              "      <td>0x15f773aef14ed49807e7b45521337333e9faa6adc75c...</td>\n",
              "      <td>429</td>\n",
              "      <td>0x39aa39c021dfbae8fac545936693ac917d5e7563</td>\n",
              "      <td>0x0047db03e71ec758fbf227ed601bae2564eec23b</td>\n",
              "      <td>15000000000</td>\n",
              "      <td>60338462292</td>\n",
              "      <td>3603489706981509</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0x0047db03e71ec758fbf227ed601bae2564eec23b</td>\n",
              "      <td>2021-09-19</td>\n",
              "      <td>0</td>\n",
              "      <td>3.399332</td>\n",
              "      <td>NaN</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2985</th>\n",
              "      <td>2021-05-01 02:46:17+00:00</td>\n",
              "      <td>12345678</td>\n",
              "      <td>0x7d804518ec6ac0b204fb79ca46c365f91822fbb5caef...</td>\n",
              "      <td>359</td>\n",
              "      <td>0x39aa39c021dfbae8fac545936693ac917d5e7563</td>\n",
              "      <td>0x6aa8d6c337000bb93abff3ef852336c1c1df39d8</td>\n",
              "      <td>3000000000</td>\n",
              "      <td>8047975525</td>\n",
              "      <td>3518864587675676</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0x6aa8d6c337000bb93abff3ef852336c1c1df39d8</td>\n",
              "      <td>2021-05-01</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2986</th>\n",
              "      <td>2021-05-01 01:33:02+00:00</td>\n",
              "      <td>12345376</td>\n",
              "      <td>0xa52f71ea984f6767b19ad527c77f03b4189c5a0c7555...</td>\n",
              "      <td>323</td>\n",
              "      <td>0x39aa39c021dfbae8fac545936693ac917d5e7563</td>\n",
              "      <td>0x34c02997bf359e0e17f8221295c0bb09c2e2346c</td>\n",
              "      <td>749330162</td>\n",
              "      <td>749330162</td>\n",
              "      <td>3519503456244520</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0x34c02997bf359e0e17f8221295c0bb09c2e2346c</td>\n",
              "      <td>2021-05-01</td>\n",
              "      <td>0</td>\n",
              "      <td>124.896170</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2987</th>\n",
              "      <td>2021-05-01 01:09:15+00:00</td>\n",
              "      <td>12345277</td>\n",
              "      <td>0x1dfcdd7bc99899bf65e13cab808a9523599afabc365b...</td>\n",
              "      <td>30</td>\n",
              "      <td>0x39aa39c021dfbae8fac545936693ac917d5e7563</td>\n",
              "      <td>0x6513b81d8240cb94abc089f2cba60be6fda2f805</td>\n",
              "      <td>400000000000</td>\n",
              "      <td>809783904803</td>\n",
              "      <td>3519932624642296</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0x6513b81d8240cb94abc089f2cba60be6fda2f805</td>\n",
              "      <td>2021-05-01</td>\n",
              "      <td>0</td>\n",
              "      <td>4671.965313</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2988</th>\n",
              "      <td>2021-05-01 00:52:48+00:00</td>\n",
              "      <td>12345188</td>\n",
              "      <td>0x2d1a582ce31928e372229f1b56aa4cb5a449f9629cdb...</td>\n",
              "      <td>330</td>\n",
              "      <td>0x39aa39c021dfbae8fac545936693ac917d5e7563</td>\n",
              "      <td>0xc124e47aa04fa869aa4a2f6fcef717b709562dba</td>\n",
              "      <td>1563705236</td>\n",
              "      <td>56913761541</td>\n",
              "      <td>3519526695814557</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0xc124e47aa04fa869aa4a2f6fcef717b709562dba</td>\n",
              "      <td>2021-05-01</td>\n",
              "      <td>54341.852494025</td>\n",
              "      <td>1283.257234</td>\n",
              "      <td>42.346812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2989</th>\n",
              "      <td>2021-05-01 00:45:54+00:00</td>\n",
              "      <td>12345152</td>\n",
              "      <td>0x750f37e2d88d063324baddf6aafa708cbfbaef2db064...</td>\n",
              "      <td>249</td>\n",
              "      <td>0x39aa39c021dfbae8fac545936693ac917d5e7563</td>\n",
              "      <td>0xd4c6985ea6f9c923060cc430491ec87769dbb410</td>\n",
              "      <td>10000000000</td>\n",
              "      <td>120014583713</td>\n",
              "      <td>3519522733955535</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0xd4c6985ea6f9c923060cc430491ec87769dbb410</td>\n",
              "      <td>2021-05-01</td>\n",
              "      <td>0</td>\n",
              "      <td>194.464122</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2990 rows × 17 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               block_timestamp  ...  eth_to_stablecoin_ratio\n",
              "0    2021-09-19 22:05:22+00:00  ...                 2.313769\n",
              "1    2021-09-19 19:52:29+00:00  ...                      NaN\n",
              "2    2021-09-19 18:43:39+00:00  ...                84.764112\n",
              "3    2021-09-19 17:17:53+00:00  ...                 0.020499\n",
              "4    2021-09-19 10:18:42+00:00  ...                      NaN\n",
              "...                        ...  ...                      ...\n",
              "2985 2021-05-01 02:46:17+00:00  ...                      NaN\n",
              "2986 2021-05-01 01:33:02+00:00  ...                      NaN\n",
              "2987 2021-05-01 01:09:15+00:00  ...                      NaN\n",
              "2988 2021-05-01 00:52:48+00:00  ...                42.346812\n",
              "2989 2021-05-01 00:45:54+00:00  ...                      NaN\n",
              "\n",
              "[2990 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_61AYCON6flf"
      },
      "source": [
        "# remove invalid ranges\n",
        "condition = (eth_to_stable_df['eth_to_stablecoin_ratio']>0.01) & (eth_to_stable_df['eth_to_stablecoin_ratio']<50)\n",
        "valid_eth_to_stable_df = eth_to_stable_df[condition]"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ORPq8E5W7pLk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "01eaa5df-cead-4cbc-8639-bfa197b8303d"
      },
      "source": [
        "valid_eth_to_stable_df[valid_eth_to_stable_df['has_liquidate_event']==0]['eth_to_stablecoin_ratio'].plot(kind='hist',bins=20,title='index distribution for non-liquidated borrowers')"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2d950a1950>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H4VKmo8F77-t",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "e7608644-998a-499b-e35e-03ec8c9816ed"
      },
      "source": [
        "valid_eth_to_stable_df[valid_eth_to_stable_df['has_liquidate_event']==1]['eth_to_stablecoin_ratio'].plot(kind='hist',bins=20,title='index distribution for liquidated borrowers')"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2d94954310>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AfovVB-h8bW0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "2c5041ae-3120-4530-957f-5b8155518bc7"
      },
      "source": [
        "eth_to_stable_df.groupby('has_liquidate_event')['eth_to_stablecoin_ratio'].describe()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>has_liquidate_event</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>523.0</td>\n",
              "      <td>21.919304</td>\n",
              "      <td>144.951326</td>\n",
              "      <td>0.000049</td>\n",
              "      <td>0.218422</td>\n",
              "      <td>1.165447</td>\n",
              "      <td>7.349181</td>\n",
              "      <td>2977.729242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44.0</td>\n",
              "      <td>11.220255</td>\n",
              "      <td>22.249409</td>\n",
              "      <td>0.017036</td>\n",
              "      <td>1.192814</td>\n",
              "      <td>2.964638</td>\n",
              "      <td>11.671348</td>\n",
              "      <td>114.298733</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     count       mean  ...        75%          max\n",
              "has_liquidate_event                    ...                        \n",
              "0                    523.0  21.919304  ...   7.349181  2977.729242\n",
              "1                     44.0  11.220255  ...  11.671348   114.298733\n",
              "\n",
              "[2 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c65x7nnEikoj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "8c4f4e1e-41d1-4886-94e0-c039827bd0c6"
      },
      "source": [
        "valid_eth_to_stable_df.groupby('has_liquidate_event')['eth_to_stablecoin_ratio'].describe()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>has_liquidate_event</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>469.0</td>\n",
              "      <td>4.686708</td>\n",
              "      <td>8.253724</td>\n",
              "      <td>0.010815</td>\n",
              "      <td>0.229171</td>\n",
              "      <td>1.031879</td>\n",
              "      <td>4.731206</td>\n",
              "      <td>46.978196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>41.0</td>\n",
              "      <td>5.917953</td>\n",
              "      <td>8.319865</td>\n",
              "      <td>0.017036</td>\n",
              "      <td>0.931623</td>\n",
              "      <td>2.457025</td>\n",
              "      <td>7.512236</td>\n",
              "      <td>40.973427</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     count      mean       std  ...       50%       75%        max\n",
              "has_liquidate_event                             ...                               \n",
              "0                    469.0  4.686708  8.253724  ...  1.031879  4.731206  46.978196\n",
              "1                     41.0  5.917953  8.319865  ...  2.457025  7.512236  40.973427\n",
              "\n",
              "[2 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q_lZSXPWMKUY"
      },
      "source": [
        " ## observations\n",
        " * both index distributions for liquidated vs non-liquidated look similar \n",
        " * sample size for liquidated events is low when considering ETH and stablecoin thresholds. \n",
        " * with this setup the index is highly skewed and clusters a lot of addresses within the 0-1 range, some transformations might help. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sw1vS49zAueS"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aP43aP4dAz29"
      },
      "source": [
        "# conclusions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s0KDOL2iCoUu"
      },
      "source": [
        "* An index was created using Stablecoin balances (usdc+dai+usdt) and ETH balances over time for a given sample of addresses.\n",
        "* To remove complexity and corner cases, we established arbitrary boundaries for the index to work i.e. the index doesn`t apply when ETH_USD or STABLE_USD <100. \n",
        "* We detected outliers matched to wallets holding a lot of usd_value \n",
        "* We sampled the index for wallets interacting with the Compound protocol and try to distinguish addresses which had / had not liquidation events. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eIVQxDMzCnJA"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}