OpenBB/examples/usdLiquidityIndex.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculating the USD Liquidity Index with the OpenBB Platform\n",
    "\n",
    "This popular indicator is made from a simple subtraction of three FRED series that are published every Wednesday, and is often overlayed with risk assets like the S&P 500 Index or Bitcoin.  The OpenBB SDK is well suited for this task, let's take a look to create this index.\n",
    "\n",
    "The formula is defined as:\n",
    "\n",
    "```console\n",
    "WALCL (All Liabilities) – WLRRAL (RRP) – WDTGAL (TGA)\n",
    "```\n",
    "\n",
    "To get these data series, we will use the `openbb-fred` data extension and the `economy` module.  First thing is to import the Python interface, and we will also import Pandas to conduct some DataFrame operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from openbb import obb\n",
    "from pandas import DataFrame\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two `fred` functions in the `openbb-economy` router:\n",
    "\n",
    "- `obb.economy.fred_search()`\n",
    "- `obb.economy.fred_series()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OBBject\n",
       "\n",
       "id: 0660e135-3511-73d7-8000-b792f7e418bb\n",
       "results: [{'date': datetime.date(2002, 12, 18), 'provider': 'fred', 'WALCL': 719542...\n",
       "provider: fred\n",
       "warnings: None\n",
       "chart: None\n",
       "extra: {'results_metadata': {'WALCL': {'title': 'Assets: Total Assets: Total Assets..."
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = obb.economy.fred_series([\"WALCL\", \"WLRRAL\", \"WDTGAL\", \"SP500\"])\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is metadata from each series in the warnings of the response object.  It can be recovered as a JSON dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['WALCL', 'WLRRAL', 'WDTGAL', 'SP500'])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Assets: Total Assets: Total Assets (Less Eliminations from Consolidation): Wednesday Level'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Millions of U.S. Dollars'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metadata = data.extra[\"results_metadata\"]\n",
    "\n",
    "display(metadata.keys())\n",
    "display(metadata[\"WALCL\"].get(\"title\"))\n",
    "display(metadata[\"WALCL\"].get(\"units\"))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Querying FRED\n",
    "\n",
    "If we didn't already know the ID for the series, we can search with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>series_id</th>\n",
       "      <th>title</th>\n",
       "      <th>observation_start</th>\n",
       "      <th>observation_end</th>\n",
       "      <th>frequency</th>\n",
       "      <th>frequency_short</th>\n",
       "      <th>units</th>\n",
       "      <th>units_short</th>\n",
       "      <th>seasonal_adjustment</th>\n",
       "      <th>seasonal_adjustment_short</th>\n",
       "      <th>last_updated</th>\n",
       "      <th>notes</th>\n",
       "      <th>popularity</th>\n",
       "      <th>group_popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WALCL</td>\n",
       "      <td>Assets: Total Assets: Total Assets (Less Elimi...</td>\n",
       "      <td>2002-12-18</td>\n",
       "      <td>2023-12-13</td>\n",
       "      <td>Weekly, As of Wednesday</td>\n",
       "      <td>W</td>\n",
       "      <td>Millions of U.S. Dollars</td>\n",
       "      <td>Mil. of U.S. $</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2023-12-14 15:34:03-06:00</td>\n",
       "      <td>None</td>\n",
       "      <td>95</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>H41RESPPALDKNWW</td>\n",
       "      <td>Assets: Liquidity and Credit Facilities: Loans...</td>\n",
       "      <td>2002-12-18</td>\n",
       "      <td>2023-12-13</td>\n",
       "      <td>Weekly</td>\n",
       "      <td>W</td>\n",
       "      <td>Millions of U.S. Dollars</td>\n",
       "      <td>Mil. of U.S. $</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2023-12-14 15:35:03-06:00</td>\n",
       "      <td>None</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TREAST</td>\n",
       "      <td>Assets: Securities Held Outright: U.S. Treasur...</td>\n",
       "      <td>2002-12-18</td>\n",
       "      <td>2023-12-13</td>\n",
       "      <td>Weekly, As of Wednesday</td>\n",
       "      <td>W</td>\n",
       "      <td>Millions of U.S. Dollars</td>\n",
       "      <td>Mil. of U.S. $</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2023-12-14 15:35:02-06:00</td>\n",
       "      <td>The total face value of U.S. Treasury securiti...</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         series_id  ... group_popularity\n",
       "0            WALCL  ...               94\n",
       "1  H41RESPPALDKNWW  ...               73\n",
       "2           TREAST  ...               72\n",
       "\n",
       "[3 rows x 14 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The first result is the series we are looking for as the starting value.\n",
    "\n",
    "obb.economy.fred_search(\"Wednesday Levels\").to_df().head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>series_id</th>\n",
       "      <th>title</th>\n",
       "      <th>observation_start</th>\n",
       "      <th>observation_end</th>\n",
       "      <th>frequency</th>\n",
       "      <th>frequency_short</th>\n",
       "      <th>units</th>\n",
       "      <th>units_short</th>\n",
       "      <th>seasonal_adjustment</th>\n",
       "      <th>seasonal_adjustment_short</th>\n",
       "      <th>last_updated</th>\n",
       "      <th>notes</th>\n",
       "      <th>popularity</th>\n",
       "      <th>group_popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WLRRAL</td>\n",
       "      <td>Liabilities and Capital: Liabilities: Reverse ...</td>\n",
       "      <td>2002-12-18</td>\n",
       "      <td>2023-12-13</td>\n",
       "      <td>Weekly, As of Wednesday</td>\n",
       "      <td>W</td>\n",
       "      <td>Millions of U.S. Dollars</td>\n",
       "      <td>Mil. of U.S. $</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2023-12-14 15:34:09-06:00</td>\n",
       "      <td>Reverse repurchase agreements are transactions...</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WLRRAFOIAL</td>\n",
       "      <td>Liabilities and Capital: Liabilities: Reverse ...</td>\n",
       "      <td>2002-12-18</td>\n",
       "      <td>2023-12-13</td>\n",
       "      <td>Weekly, As of Wednesday</td>\n",
       "      <td>W</td>\n",
       "      <td>Millions of U.S. Dollars</td>\n",
       "      <td>Mil. of U.S. $</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2023-12-14 15:34:12-06:00</td>\n",
       "      <td>Reverse repurchase agreements are transactions...</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WLRRAOL</td>\n",
       "      <td>Liabilities and Capital: Liabilities: Reverse ...</td>\n",
       "      <td>2002-12-18</td>\n",
       "      <td>2023-12-13</td>\n",
       "      <td>Weekly, As of Wednesday</td>\n",
       "      <td>W</td>\n",
       "      <td>Millions of U.S. Dollars</td>\n",
       "      <td>Mil. of U.S. $</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2023-12-14 15:34:16-06:00</td>\n",
       "      <td>None</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    series_id  ... group_popularity\n",
       "0      WLRRAL  ...               64\n",
       "1  WLRRAFOIAL  ...               40\n",
       "2     WLRRAOL  ...               24\n",
       "\n",
       "[3 rows x 14 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding \"Reverse Repo\" to the search returns the second series in the equation, as the first result.\n",
    "\n",
    "obb.economy.fred_search(\"Wednesday Levels Reverse Repo\").to_df().head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>series_id</th>\n",
       "      <th>title</th>\n",
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       "      <th>seasonal_adjustment_short</th>\n",
       "      <th>last_updated</th>\n",
       "      <th>notes</th>\n",
       "      <th>popularity</th>\n",
       "      <th>group_popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WDTGAL</td>\n",
       "      <td>Liabilities and Capital: Liabilities: Deposits...</td>\n",
       "      <td>2002-12-18</td>\n",
       "      <td>2023-12-13</td>\n",
       "      <td>Weekly, As of Wednesday</td>\n",
       "      <td>W</td>\n",
       "      <td>Millions of U.S. Dollars</td>\n",
       "      <td>Mil. of U.S. $</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2023-12-14 15:34:08-06:00</td>\n",
       "      <td>This account is the primary operational accoun...</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D2WLTGAL</td>\n",
       "      <td>Liabilities and Capital: Liabilities: Deposits...</td>\n",
       "      <td>2002-12-18</td>\n",
       "      <td>2023-12-13</td>\n",
       "      <td>Weekly, As of Wednesday</td>\n",
       "      <td>W</td>\n",
       "      <td>Millions of U.S. Dollars</td>\n",
       "      <td>Mil. of U.S. $</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2023-12-14 15:34:10-06:00</td>\n",
       "      <td>None</td>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WLDLCL</td>\n",
       "      <td>Liabilities and Capital: Liabilities: Deposits...</td>\n",
       "      <td>2002-12-18</td>\n",
       "      <td>2023-12-13</td>\n",
       "      <td>Weekly, As of Wednesday</td>\n",
       "      <td>W</td>\n",
       "      <td>Millions of U.S. Dollars</td>\n",
       "      <td>Mil. of U.S. $</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2023-12-14 15:34:13-06:00</td>\n",
       "      <td>This item is the sum of \"Term deposits held by...</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  series_id  ... group_popularity\n",
       "0    WDTGAL  ...               65\n",
       "1  D2WLTGAL  ...               62\n",
       "2    WLDLCL  ...               23\n",
       "\n",
       "[3 rows x 14 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Refining the search for the Treasury General Account, returns the final series in the equation, as the first result.\n",
    "\n",
    "obb.economy.fred_search(\"Wednesday Levels Treasury General\").to_df().head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>series_id</th>\n",
       "      <th>title</th>\n",
       "      <th>observation_start</th>\n",
       "      <th>observation_end</th>\n",
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       "      <th>frequency_short</th>\n",
       "      <th>units</th>\n",
       "      <th>units_short</th>\n",
       "      <th>seasonal_adjustment</th>\n",
       "      <th>seasonal_adjustment_short</th>\n",
       "      <th>last_updated</th>\n",
       "      <th>notes</th>\n",
       "      <th>popularity</th>\n",
       "      <th>group_popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SP500</td>\n",
       "      <td>S&amp;P 500</td>\n",
       "      <td>2013-12-16</td>\n",
       "      <td>2023-12-15</td>\n",
       "      <td>Daily, Close</td>\n",
       "      <td>D</td>\n",
       "      <td>Index</td>\n",
       "      <td>Index</td>\n",
       "      <td>Not Seasonally Adjusted</td>\n",
       "      <td>NSA</td>\n",
       "      <td>2023-12-15 19:11:01-06:00</td>\n",
       "      <td>The observations for the S&amp;P 500 represent the...</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  series_id    title  ... popularity group_popularity\n",
       "0     SP500  S&P 500  ...         83               83\n",
       "\n",
       "[1 rows x 14 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Several major equity indices are published to FRED, S&P 500 is one of them.\n",
    "\n",
    "obb.economy.fred_search(\"SP500\").to_df().head(2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By looking at the descriptions, we can confirm that all three Federal Reserve series are numbers as `Millions of USD`.  If they were not all equivalent, some adjustments would need to be made before applying the equation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'WALCL: Millions of U.S. Dollars'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'WLRRAL: Millions of U.S. Dollars'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'WDTGAL: Millions of U.S. Dollars'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'SP500: Index'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for id in metadata:\n",
    "    display(f\"{id}: {metadata[id]['units']}\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspecting the time series element shows that the S&P 500 data (as published to FRED) does not extend as far back as the others.  Let's drop the NaN values and start the time series at a common starting point, which is approximately ten years ago."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>WALCL</th>\n",
       "      <th>WLRRAL</th>\n",
       "      <th>WDTGAL</th>\n",
       "      <th>SP500</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-12-18</th>\n",
       "      <td>719542.0</td>\n",
       "      <td>21905.0</td>\n",
       "      <td>6595.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-12-25</th>\n",
       "      <td>732059.0</td>\n",
       "      <td>20396.0</td>\n",
       "      <td>4662.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-01</th>\n",
       "      <td>730994.0</td>\n",
       "      <td>21091.0</td>\n",
       "      <td>4420.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-08</th>\n",
       "      <td>723762.0</td>\n",
       "      <td>18709.0</td>\n",
       "      <td>5490.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               WALCL   WLRRAL  WDTGAL  SP500\n",
       "date                                        \n",
       "2002-12-18  719542.0  21905.0  6595.0    NaN\n",
       "2002-12-25  732059.0  20396.0  4662.0    NaN\n",
       "2003-01-01  730994.0  21091.0  4420.0    NaN\n",
       "2003-01-08  723762.0  18709.0  5490.0    NaN"
      ]
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       "      <th>WALCL</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-12-18</th>\n",
       "      <td>4008062.0</td>\n",
       "      <td>111966.0</td>\n",
       "      <td>116998.0</td>\n",
       "      <td>1810.65</td>\n",
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       "    <tr>\n",
       "      <th>2014-01-08</th>\n",
       "      <td>4028185.0</td>\n",
       "      <td>150726.0</td>\n",
       "      <td>110758.0</td>\n",
       "      <td>1837.49</td>\n",
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       "    <tr>\n",
       "      <th>2014-01-15</th>\n",
       "      <td>4071528.0</td>\n",
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       "      <td>87926.0</td>\n",
       "      <td>1848.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-22</th>\n",
       "      <td>4097914.0</td>\n",
       "      <td>201954.0</td>\n",
       "      <td>96724.0</td>\n",
       "      <td>1844.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                WALCL    WLRRAL    WDTGAL    SP500\n",
       "date                                              \n",
       "2013-12-18  4008062.0  111966.0  116998.0  1810.65\n",
       "2014-01-08  4028185.0  150726.0  110758.0  1837.49\n",
       "2014-01-15  4071528.0  156266.0   87926.0  1848.38\n",
       "2014-01-22  4097914.0  201954.0   96724.0  1844.86"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data.to_df().head(4))\n",
    "display(data.to_df().dropna().head(4))\n",
    "\n",
    "# We'll create a new DataFrame object with the dropped rows.\n",
    "liquidity_index = DataFrame(data.to_df().dropna())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying the formula will simply be a matter of subtracting the first, three, columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>WALCL</th>\n",
       "      <th>WLRRAL</th>\n",
       "      <th>WDTGAL</th>\n",
       "      <th>SP500</th>\n",
       "      <th>USD Liquidity Index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</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>2023-11-22</th>\n",
       "      <td>7810814.0</td>\n",
       "      <td>1271679.0</td>\n",
       "      <td>693007.0</td>\n",
       "      <td>4556.62</td>\n",
       "      <td>5846128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-29</th>\n",
       "      <td>7796145.0</td>\n",
       "      <td>1254156.0</td>\n",
       "      <td>753570.0</td>\n",
       "      <td>4550.58</td>\n",
       "      <td>5788419.0</td>\n",
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       "    <tr>\n",
       "      <th>2023-12-06</th>\n",
       "      <td>7737385.0</td>\n",
       "      <td>1184528.0</td>\n",
       "      <td>667678.0</td>\n",
       "      <td>4549.34</td>\n",
       "      <td>5885179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-13</th>\n",
       "      <td>7739566.0</td>\n",
       "      <td>1152232.0</td>\n",
       "      <td>630618.0</td>\n",
       "      <td>4707.09</td>\n",
       "      <td>5956716.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                WALCL     WLRRAL    WDTGAL    SP500  USD Liquidity Index\n",
       "date                                                                    \n",
       "2023-11-22  7810814.0  1271679.0  693007.0  4556.62            5846128.0\n",
       "2023-11-29  7796145.0  1254156.0  753570.0  4550.58            5788419.0\n",
       "2023-12-06  7737385.0  1184528.0  667678.0  4549.34            5885179.0\n",
       "2023-12-13  7739566.0  1152232.0  630618.0  4707.09            5956716.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "liquidity_index[\"USD Liquidity Index\"] = (liquidity_index[\"WALCL\"] - liquidity_index[\"WLRRAL\"] - liquidity_index[\"WDTGAL\"])\n",
    "\n",
    "liquidity_index.tail(4)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that there are two items to compare, let's draw it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
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   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "fig = go.Figure()\n",
    "\n",
    "fig.add_scatter(\n",
    "    x=liquidity_index.index, y=liquidity_index[\"USD Liquidity Index\"]/1000, name=\"USD Liquidity Index (Billions)\", yaxis=\"y1\")\n",
    "\n",
    "fig.add_scatter(\n",
    "    x=liquidity_index.index, y=liquidity_index[\"SP500\"], name=\"S&P 500 Index\", yaxis=\"y2\")\n",
    "\n",
    "fig.update_layout(\n",
    "    yaxis=dict(title=\"USD Liquidity Index (Billions)\",side=\"left\", position=0, titlefont=dict(size=12), showgrid=False),\n",
    "    yaxis2=dict(title=\"S&P 500 Index\", side=\"right\", overlaying=\"y\", position=1, titlefont=dict(size=12)),\n",
    "    title=\"USD Liquidity Index vs. S&P 500 Index\",\n",
    "    title_y=0.90,\n",
    "    title_x=0.5,\n",
    "    autosize=True,\n",
    ")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To draw them both on the same y-axis, they will need to be normalized.  There are several methods for normalizing a series, the fourth function in the block below paramaterizes a few of them, making it easy to A/B them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USD Liquidity Index</th>\n",
       "      <th>SP500</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-11-29</th>\n",
       "      <td>0.721351</td>\n",
       "      <td>0.920274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-06</th>\n",
       "      <td>0.748489</td>\n",
       "      <td>0.919866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-13</th>\n",
       "      <td>0.768553</td>\n",
       "      <td>0.971734</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "            USD Liquidity Index     SP500\n",
       "date                                     \n",
       "2023-11-29             0.721351  0.920274\n",
       "2023-12-06             0.748489  0.919866\n",
       "2023-12-13             0.768553  0.971734"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "y_axis = liquidity_index[[\"USD Liquidity Index\", \"SP500\"]]\n",
    "\n",
    "def absolute_maximum_scale(series):\n",
    "    return series / series.abs().max()\n",
    "\n",
    "def min_max_scaling(series):\n",
    "    return (series - series.min()) / (series.max() - series.min())\n",
    "\n",
    "def z_score_standardization(series):\n",
    "    return (series - series.mean()) / series.std()\n",
    "\n",
    "methods = {\"z\": z_score_standardization, \"m\": min_max_scaling, \"a\": absolute_maximum_scale}\n",
    "\n",
    "def normalize(data: DataFrame, method: str = \"z\") -> DataFrame:\n",
    "    for col in data.columns:\n",
    "        data.loc[:, col] = methods[f\"{method}\"](data.loc[:, col])\n",
    "\n",
    "    return data\n",
    "\n",
    "normalized = normalize(y_axis, method = \"m\")\n",
    "\n",
    "normalized.tail(3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now they can be easily plotted using the built-in `DataFrame.plot` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
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           "x": 0.01,
           "xanchor": "left",
           "y": 0.99,
           "yanchor": "top"
          },
          "legend2": {
           "bgcolor": "rgba(0, 0, 0, 0.5)",
           "font": {
            "size": 15
           }
          },
          "legend3": {
           "bgcolor": "rgba(0, 0, 0, 0.5)",
           "font": {
            "size": 15
           }
          },
          "legend4": {
           "bgcolor": "rgba(0, 0, 0, 0.5)",
           "font": {
            "size": 15
           }
          },
          "legend5": {
           "bgcolor": "rgba(0, 0, 0, 0.5)",
           "font": {
            "size": 15
           }
          },
          "mapbox": {
           "style": "dark"
          },
          "paper_bgcolor": "#000000",
          "plot_bgcolor": "#000000",
          "polar": {
           "angularaxis": {
            "gridcolor": "#506784",
            "linecolor": "#506784",
            "ticks": ""
           },
           "bgcolor": "rgb(17,17,17)",
           "radialaxis": {
            "gridcolor": "#506784",
            "linecolor": "#506784",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "rgb(17,17,17)",
            "gridcolor": "#506784",
            "gridwidth": 2,
            "linecolor": "#506784",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "#C8D4E3"
           },
           "yaxis": {
            "backgroundcolor": "rgb(17,17,17)",
            "gridcolor": "#506784",
            "gridwidth": 2,
            "linecolor": "#506784",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "#C8D4E3"
           },
           "zaxis": {
            "backgroundcolor": "rgb(17,17,17)",
            "gridcolor": "#506784",
            "gridwidth": 2,
            "linecolor": "#506784",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "#C8D4E3"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#f2f5fa"
           }
          },
          "sliderdefaults": {
           "bgcolor": "#C8D4E3",
           "bordercolor": "rgb(17,17,17)",
           "borderwidth": 1,
           "tickwidth": 0
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "#506784",
            "linecolor": "#506784",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "#506784",
            "linecolor": "#506784",
            "ticks": ""
           },
           "bgcolor": "rgb(17,17,17)",
           "caxis": {
            "gridcolor": "#506784",
            "linecolor": "#506784",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "updatemenudefaults": {
           "bgcolor": "#506784",
           "borderwidth": 0
          },
          "xaxis": {
           "automargin": true,
           "autorange": true,
           "gridcolor": "#283442",
           "linecolor": "#F5EFF3",
           "mirror": true,
           "rangeslider": {
            "visible": false
           },
           "showgrid": true,
           "showline": true,
           "tick0": 1,
           "tickfont": {
            "size": 14
           },
           "ticks": "outside",
           "title": {
            "standoff": 20
           },
           "zeroline": false,
           "zerolinecolor": "#283442",
           "zerolinewidth": 2
          },
          "yaxis": {
           "anchor": "x",
           "automargin": true,
           "fixedrange": false,
           "gridcolor": "#283442",
           "linecolor": "#F5EFF3",
           "mirror": true,
           "showgrid": true,
           "showline": true,
           "side": "right",
           "tick0": 0.5,
           "ticks": "outside",
           "title": {
            "standoff": 20
           },
           "zeroline": false,
           "zerolinecolor": "#283442",
           "zerolinewidth": 2
          }
         }
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "date"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "value"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.options.plotting.backend = \"plotly\"\n",
    "normalized.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you just want to visualize the results, this is a fast way of doing it. However, titles are other customizatons clean things up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "name": "USD Liquidity Index",
         "type": "scatter",
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        "title": {
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         "y": 0.9
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       }
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     },
     "metadata": {},
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   ],
   "source": [
    "fig = go.Figure()\n",
    "\n",
    "fig.add_scatter(\n",
    "    x=normalized.index, y=normalized[\"USD Liquidity Index\"], name=\"USD Liquidity Index\")\n",
    "\n",
    "fig.add_scatter(\n",
    "    x=normalized.index, y=normalized[\"SP500\"], name=\"S&P 500 Index\")\n",
    "\n",
    "fig.update_layout(\n",
    "    title=\"USD Liquidity Index vs. S&P 500 Index (Normalized)\",\n",
    "    title_y=0.90,\n",
    "    title_x=0.5,\n",
    "    autosize=True,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The combinations are endless and we love seeing your creations, tag us on social media with your custom indexes and indicators."
   ]
  }
 ],
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