{ "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": 8, "metadata": {}, "outputs": [], "source": [ "import json\n", "\n", "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": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "OBBject\n", "\n", "id: 06580cab-5bdc-7e28-8000-632a5d8dc92f\n", "results: [{'date': datetime.date(2002, 12, 18), 'WALCL': 719542.0, 'WLRRAL': 21905....\n", "provider: fred\n", "warnings: [{'category': 'UserWarning', 'message': '{\"WALCL\": {\"title\": \"Assets: Tot...\n", "chart: None\n", "extra: {'metadata': {'arguments': {'provider_choices': {'provider': None}, 'standar..." ] }, "execution_count": 4, "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": 12, "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 = json.loads(data.warnings[0].message)\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": [ "
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series_idtitleobservation_startobservation_endfrequencyfrequency_shortunitsunits_shortseasonal_adjustmentseasonal_adjustment_shortlast_updatednotespopularitygroup_popularity
0WALCLAssets: Total Assets: Total Assets (Less Elimi...2002-12-182023-12-13Weekly, As of WednesdayWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2023-12-14 15:34:03-06:00None9594
1H41RESPPALDKNWWAssets: Liquidity and Credit Facilities: Loans...2002-12-182023-12-13WeeklyWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2023-12-14 15:35:03-06:00None7373
2TREASTAssets: Securities Held Outright: U.S. Treasur...2002-12-182023-12-13Weekly, As of WednesdayWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2023-12-14 15:35:02-06:00The total face value of U.S. Treasury securiti...7272
\n", "
" ], "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": [ "
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series_idtitleobservation_startobservation_endfrequencyfrequency_shortunitsunits_shortseasonal_adjustmentseasonal_adjustment_shortlast_updatednotespopularitygroup_popularity
0WLRRALLiabilities and Capital: Liabilities: Reverse ...2002-12-182023-12-13Weekly, As of WednesdayWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2023-12-14 15:34:09-06:00Reverse repurchase agreements are transactions...6464
1WLRRAFOIALLiabilities and Capital: Liabilities: Reverse ...2002-12-182023-12-13Weekly, As of WednesdayWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2023-12-14 15:34:12-06:00Reverse repurchase agreements are transactions...4040
2WLRRAOLLiabilities and Capital: Liabilities: Reverse ...2002-12-182023-12-13Weekly, As of WednesdayWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2023-12-14 15:34:16-06:00None2424
\n", "
" ], "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": [ "
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series_idtitleobservation_startobservation_endfrequencyfrequency_shortunitsunits_shortseasonal_adjustmentseasonal_adjustment_shortlast_updatednotespopularitygroup_popularity
0WDTGALLiabilities and Capital: Liabilities: Deposits...2002-12-182023-12-13Weekly, As of WednesdayWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2023-12-14 15:34:08-06:00This account is the primary operational accoun...6565
1D2WLTGALLiabilities and Capital: Liabilities: Deposits...2002-12-182023-12-13Weekly, As of WednesdayWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2023-12-14 15:34:10-06:00None6262
2WLDLCLLiabilities and Capital: Liabilities: Deposits...2002-12-182023-12-13Weekly, As of WednesdayWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2023-12-14 15:34:13-06:00This item is the sum of \"Term deposits held by...2323
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" ], "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": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
series_idtitleobservation_startobservation_endfrequencyfrequency_shortunitsunits_shortseasonal_adjustmentseasonal_adjustment_shortlast_updatednotespopularitygroup_popularity
0SP500S&P 5002013-12-162023-12-15Daily, CloseDIndexIndexNot Seasonally AdjustedNSA2023-12-15 19:11:01-06:00The observations for the S&P 500 represent the...8383
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" ], "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": 33, "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": { "text/html": [ "
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WALCLWLRRALWDTGALSP500
date
2002-12-18719542.021905.06595.0NaN
2002-12-25732059.020396.04662.0NaN
2003-01-01730994.021091.04420.0NaN
2003-01-08723762.018709.05490.0NaN
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" ], "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" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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WALCLWLRRALWDTGALSP500
date
2013-12-184008062.0111966.0116998.01810.65
2014-01-084028185.0150726.0110758.01837.49
2014-01-154071528.0156266.087926.01848.38
2014-01-224097914.0201954.096724.01844.86
\n", "
" ], "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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WALCLWLRRALWDTGALSP500USD Liquidity Index
date
2023-11-227810814.01271679.0693007.04556.625846128.0
2023-11-297796145.01254156.0753570.04550.585788419.0
2023-12-067737385.01184528.0667678.04549.345885179.0
2023-12-137739566.01152232.0630618.04707.095956716.0
\n", "
" ], "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" }, "data": [ { "name": "USD Liquidity Index (Billions)", "type": "scatter", "x": [ 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"side": "right", "tick0": 0.5, "ticks": "outside", "title": { "standoff": 20 }, "zeroline": false, "zerolinecolor": "#283442", "zerolinewidth": 2 } } }, "title": { "text": "USD Liquidity Index vs. S&P 500 Index", "x": 0.5, "y": 0.9 }, "yaxis": { "position": 0, "showgrid": false, "side": "left", "title": { "font": { "size": 12 }, "text": "USD Liquidity Index (Billions)" } }, "yaxis2": { "overlaying": "y", "position": 1, "side": "right", "title": { "font": { "size": 12 }, "text": "S&P 500 Index" } } } } }, "metadata": {}, "output_type": "display_data" } ], "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": [ "
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USD Liquidity IndexSP500
date
2023-11-290.7213510.920274
2023-12-060.7484890.919866
2023-12-130.7685530.971734
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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" } ], "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
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"display_data" } ], "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." ] } ], "metadata": { "kernelspec": { "display_name": "obb", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.4" }, 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