ecb_dwh_api.ipynb

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      "source": [
        "### Accessing the European Central Bank DWH risk data with Python\n",
        "\n",
        "The European Central Bank (ECB) and the European Banking Authority (EBA) have made a large amount of credit risk information available. Although much of this information is not yet in a machine-readable format, this option is becoming increasingly popular. \n",
        "\n",
        "See also:\n",
        "- [Introduction to the ECB DWH](https://sdw-wsrest.ecb.europa.eu/help/)\n",
        "- [Available data series](https://sdw.ecb.europa.eu/browseSelection.do?node=9698058)\n"
      ],
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      "cell_type": "code",
      "source": [
        "import requests\n",
        "import pandas as pd\n",
        "import io\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "PKjTCdU9EwBX"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### A. Helper functions"
      ],
      "metadata": {
        "id": "QOW1tnXTl0hW"
      }
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    {
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      "source": [
        "def sup_ecb_data(countries, data_items, report_type='estimates'):\n",
        "  # Query the ECB DWH for risk data\n",
        "\n",
        "  country_list = \"+\".join(countries)\n",
        "  item_list = \"+\".join(data_items)\n",
        "\n",
        "  entrypoint = 'https://sdw-wsrest.ecb.europa.eu/service/'\n",
        "  resource = 'data'\n",
        "  flowRef ='SUP'\n",
        "\n",
        "  if report_type == 'estimates':\n",
        "    key = f'Q.B01.{country_list}._Z.{item_list}._T._Z._Z._Z._Z.PCT.C'\n",
        "  elif report_type == 'risk_weights':\n",
        "    key = f'Q.{country_list}.W0._Z.{item_list}._T._Z._Z._Z._Z.PCT.C'\n",
        "\n",
        "  # Define the parameters\n",
        "  params = {\n",
        "      'startPeriod': '2000-01-01',  # Start date of the time series\n",
        "      'endPeriod': '2022-10-01'     # End of the time series\n",
        "            }\n",
        "\n",
        "  # Create the query\n",
        "  request_url = entrypoint + resource + '/'+ flowRef + '/' + key\n",
        "\n",
        "  # Make the HTTP request\n",
        "  return requests.get(request_url, params=params, headers={'Accept': 'text/csv'})\n",
        "\n",
        "\n",
        "def createPlots(df, group_label, first_dim, second_dim, title):\n",
        "  # create the scatter plot and add group names next to the datapoints\n",
        "  for group, name in df.groupby(group_label):\n",
        "      plt.scatter(name[first_dim], name[second_dim], label=group)\n",
        "\n",
        "  for i, txt in enumerate(df[group_label]):\n",
        "      plt.annotate(txt, \n",
        "                  (df[first_dim][i], \n",
        "                  df[second_dim][i]), \n",
        "                  xytext=(10,0), \n",
        "                  textcoords='offset points')\n",
        "\n",
        "  # Add info and show the plot\n",
        "  plt.xlabel(f'{first_dim} (%)')\n",
        "  plt.ylabel(f'{second_dim} (%)')\n",
        "  plt.title(f'{title}', y =1.1)\n",
        "\n",
        "  plt.gca().spines['top'].set_visible(False)\n",
        "  plt.gca().spines['right'].set_visible(False)"
      ],
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      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### B. View available time series"
      ],
      "metadata": {
        "id": "I1Q6ZLRdtfsd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Source: https://sdw.ecb.europa.eu/datastructure.do?conceptMnemonic=CB_ITEM&datasetinstanceid=359#cl\n",
        "data_catalogue = pd.read_csv('ecb_dwh_data_catalogue.csv', sep='\\t', encoding='utf-8')\n",
        "data_catalogue.head()"
      ],
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      "execution_count": 120,
      "outputs": [
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              "   Unnamed: 0                                              Label  \\\n",
              "0           0       TOTAL RISK EXPOSURE AMOUNT / TOTAL EXPOSURES   \n",
              "1           1  TOTAL EXPOSURES FOR CREDIT, COUNTERPARTY CREDI...   \n",
              "2           2  Exposures to credit risk - Risk exposure amoun...   \n",
              "3           3                           Securitisation positions   \n",
              "4           4  Exposures to credit risk - Standardised approa...   \n",
              "\n",
              "                                        Key    From      To  \\\n",
              "0  SUP.Q.AT.W0._Z.E0000._T._Z._Z.ALL.LE.E.C  2015Q2  2022Q3   \n",
              "1  SUP.Q.AT.W0._Z.E1000._T._Z._Z.ALL.LE.E.C  2015Q2  2022Q3   \n",
              "2  SUP.Q.AT.W0._Z.E1100._T._Z._Z.ALL.LE.E.C  2015Q2  2022Q3   \n",
              "3  SUP.Q.AT.W0._Z.E1300._T._Z._Z.ALL.LE.E.C  2020Q1  2022Q3   \n",
              "4  SUP.Q.AT.W0._Z.E2000._T._Z._Z.ALL.LE.E.C  2015Q2  2022Q3   \n",
              "\n",
              "          Last Updated Frequency Country   Item Country.1 Data type  \\\n",
              "0  2023-01-11 10:00:00         Q      AT  E0000       ALL        LE   \n",
              "1  2023-01-11 10:00:00         Q      AT  E1000       ALL        LE   \n",
              "2  2023-01-11 10:00:00         Q      AT  E1100       ALL        LE   \n",
              "3  2023-01-11 10:00:00         Q      AT  E1300       ALL        LE   \n",
              "4  2023-01-11 10:00:00         Q      AT  E2000       ALL        LE   \n",
              "\n",
              "  Balance sheet suffix                                   Long description  \n",
              "0                    E   Austria, World (all entities, including refer...  \n",
              "1                    E   Austria, World (all entities, including refer...  \n",
              "2                    E   Austria, World (all entities, including refer...  \n",
              "3                    E   Austria, World (all entities, including refer...  \n",
              "4                    E   Austria, World (all entities, including refer...  "
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              "      <th>0</th>\n",
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              "      <td>TOTAL RISK EXPOSURE AMOUNT / TOTAL EXPOSURES</td>\n",
              "      <td>SUP.Q.AT.W0._Z.E0000._T._Z._Z.ALL.LE.E.C</td>\n",
              "      <td>2015Q2</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Exposures to credit risk - Risk exposure amoun...</td>\n",
              "      <td>SUP.Q.AT.W0._Z.E1100._T._Z._Z.ALL.LE.E.C</td>\n",
              "      <td>2015Q2</td>\n",
              "      <td>2022Q3</td>\n",
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              "      <td>Q</td>\n",
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              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Securitisation positions</td>\n",
              "      <td>SUP.Q.AT.W0._Z.E1300._T._Z._Z.ALL.LE.E.C</td>\n",
              "      <td>2020Q1</td>\n",
              "      <td>2022Q3</td>\n",
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              "      <td>Q</td>\n",
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              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>Exposures to credit risk - Standardised approa...</td>\n",
              "      <td>SUP.Q.AT.W0._Z.E2000._T._Z._Z.ALL.LE.E.C</td>\n",
              "      <td>2015Q2</td>\n",
              "      <td>2022Q3</td>\n",
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      "source": [
        "### C. Get PD, LGD and RWA data"
      ],
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        "id": "B4FHNmqxltSb"
      }
    },
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      "source": [
        "# Get PD and LGD data\n",
        "countries = ['ES', 'DE', 'FR', 'IT', 'EE', 'SE', \n",
        "             'IT', 'NL', 'CH', 'FI', 'BE']\n",
        "data_items = ['EPD05', 'EL005']\n",
        "\n",
        "# Request\n",
        "response = sup_ecb_data(countries, data_items, report_type='estimates')\n",
        "\n",
        "# Read the response as a file into a Pandas DataFrame\n",
        "est_data = pd.read_csv(io.StringIO(response.text))\n",
        "\n",
        "est_data = est_data[['TIME_PERIOD', 'TITLE', 'TITLE_COMPL', 'COUNT_AREA', 'OBS_VALUE']]\n",
        "est_data.columns = ['SnapshotDate', 'Item', 'Description', 'Area', 'Value']"
      ],
      "metadata": {
        "id": "9yJI2vbdVGGW"
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      "execution_count": 121,
      "outputs": []
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      "source": [
        "# Get RWA data\n",
        "countries = ['ES', 'DE', 'FR', 'IT', 'EE', 'SE', \n",
        "             'IT', 'NL', 'CH', 'FI', 'BE']\n",
        "data_items = ['EW24R']\n",
        "\n",
        "# Request\n",
        "response = sup_ecb_data(countries, data_items, report_type='risk_weights')\n",
        "\n",
        "# Read the response as a file into a Pandas DataFrame\n",
        "rwa_data = pd.read_csv(io.StringIO(response.text))\n",
        "\n",
        "rwa_data = rwa_data[['TIME_PERIOD', 'TITLE', 'TITLE_COMPL', 'REF_AREA', 'OBS_VALUE']]\n",
        "rwa_data.columns = ['SnapshotDate', 'Item', 'Description', 'Area', 'Value']"
      ],
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      "execution_count": 122,
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        "# Join datasets\n",
        "full_ds = pd.concat([est_data, rwa_data])\n",
        "\n",
        "# Filter dataframe by date and create the dataset for plotting\n",
        "snapshot_date = '2022-Q3'\n",
        "full_dataset = full_ds[full_ds['SnapshotDate'] == snapshot_date]\n",
        "\n",
        "# Prepare data for plotting\n",
        "summary = full_ds.groupby(['Area', 'Item']).agg({'Value': ['max']}).unstack().reset_index()\n",
        "summary.columns = ['Area', 'LGD', 'PD', 'RWA']\n",
        "\n",
        "# Enrich data\n",
        "summary['EL'] = summary['PD'] * summary['LGD'] / 100\n",
        "\n",
        "summary"
      ],
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      "execution_count": 123,
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            "text/plain": [
              "  Area    LGD    PD    RWA        EL\n",
              "0   BE  14.23  0.99  12.78  0.140877\n",
              "1   CH  20.80  0.88    NaN  0.183040\n",
              "2   DE  18.79  1.03  16.78  0.193537\n",
              "3   EE    NaN   NaN    NaN       NaN\n",
              "4   ES  21.69  1.31  17.79  0.284139\n",
              "5   FI  21.59  0.99   7.98  0.213741\n",
              "6   FR  13.18  1.09  15.46  0.143662\n",
              "7   IT  20.70  1.58  19.19  0.327060\n",
              "8   NL  16.08  0.62  14.77  0.099696"
            ],
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              "      <th></th>\n",
              "      <th>Area</th>\n",
              "      <th>LGD</th>\n",
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              "      <th>EL</th>\n",
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              "      <td>BE</td>\n",
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              "      <td>0.140877</td>\n",
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              "      <th>1</th>\n",
              "      <td>CH</td>\n",
              "      <td>20.80</td>\n",
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              "      <th>2</th>\n",
              "      <td>DE</td>\n",
              "      <td>18.79</td>\n",
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              "      <td>0.193537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>EE</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ES</td>\n",
              "      <td>21.69</td>\n",
              "      <td>1.31</td>\n",
              "      <td>17.79</td>\n",
              "      <td>0.284139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>FI</td>\n",
              "      <td>21.59</td>\n",
              "      <td>0.99</td>\n",
              "      <td>7.98</td>\n",
              "      <td>0.213741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>FR</td>\n",
              "      <td>13.18</td>\n",
              "      <td>1.09</td>\n",
              "      <td>15.46</td>\n",
              "      <td>0.143662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>IT</td>\n",
              "      <td>20.70</td>\n",
              "      <td>1.58</td>\n",
              "      <td>19.19</td>\n",
              "      <td>0.327060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>NL</td>\n",
              "      <td>16.08</td>\n",
              "      <td>0.62</td>\n",
              "      <td>14.77</td>\n",
              "      <td>0.099696</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a5bdeff5-768f-4f59-aa34-bb05f63c379f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a5bdeff5-768f-4f59-aa34-bb05f63c379f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a5bdeff5-768f-4f59-aa34-bb05f63c379f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### D. Create graphs"
      ],
      "metadata": {
        "id": "XIH188JxvI7Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create graphs\n",
        "plt.figure(figsize=(16, 6)) \n",
        "plt.subplot(1, 2, 1) # row 1, col 2 index 1\n",
        "\n",
        "# PD/LGD graph\n",
        "group_label = 'Area'\n",
        "first_dim = 'LGD'\n",
        "second_dim = 'PD'\n",
        "title = f'PD vs LGD for \"Retail immovable property\", {snapshot_date}'\n",
        "\n",
        "createPlots(summary, group_label, first_dim, second_dim, title)\n",
        "\n",
        "plt.subplot(1, 2, 2) # row 1, col 2 index 1\n",
        "\n",
        "# RWA graph\n",
        "group_label = 'Area'\n",
        "first_dim = 'RWA'\n",
        "second_dim = 'EL'\n",
        "title = f'RWA vs EL for \"Retail immovable property, {snapshot_date}'\n",
        "\n",
        "createPlots(summary, group_label, first_dim, second_dim, title)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "XpvZQQwgqbsl",
        "outputId": "d2514bc9-8163-4f5b-a940-73505eeac47e"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}