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OpenBB/examples/usdLiquidityIndex.ipynb
Danglewood 45998938ed update example notebook syntax for metadata change (#6287) 
Co-authored-by: Igor Radovanovic <74266147+IgorWounds@users.noreply.github.com>
2024-04-04 15:02:56 +00:00

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{
"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": {
"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>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": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
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"\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>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": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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"</style>\n",
"<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>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": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" vertical-align: top;\n",
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" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<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>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": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>WALCL</th>\n",
" <th>WLRRAL</th>\n",
" <th>WDTGAL</th>\n",
" <th>SP500</th>\n",
" </tr>\n",
" <tr>\n",
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" <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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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <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>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",
" </tr>\n",
" <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",
" </tr>\n",
" <tr>\n",
" <th>2014-01-15</th>\n",
" <td>4071528.0</td>\n",
" <td>156266.0</td>\n",
" <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": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <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",
" </tr>\n",
" <tr>\n",
" <th>2023-12-06</th>\n",
" <td>7737385.0</td>\n",
" <td>1184528.0</td>\n",
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" <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": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
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"data": [
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"x": 0.5,
"y": 0.9
},
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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": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"</style>\n",
"<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",
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" <tr>\n",
" <th>2023-11-29</th>\n",
" <td>0.721351</td>\n",
" <td>0.920274</td>\n",
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" <tr>\n",
" <th>2023-12-06</th>\n",
" <td>0.748489</td>\n",
" <td>0.919866</td>\n",
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" <tr>\n",
" <th>2023-12-13</th>\n",
" <td>0.768553</td>\n",
" <td>0.971734</td>\n",
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"</table>\n",
"</div>"
],
"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"
}
],
"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": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"hovertemplate": "variable=USD Liquidity Index<br>date=%{x}<br>value=%{y}<extra></extra>",
"legendgroup": "USD Liquidity Index",
"line": {
"color": "#ffed00",
"dash": "solid"
},
"marker": {
"symbol": "circle"
},
"mode": "lines",
"name": "USD Liquidity Index",
"showlegend": true,
"type": "scattergl",
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},
"metadata": {},
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],
"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": {
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},
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"title": {
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"x": 0.5,
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},
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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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