diff --git "a/examples/usdLiquidityIndex.ipynb" "b/examples/usdLiquidityIndex.ipynb" new file mode 100644--- /dev/null +++ "b/examples/usdLiquidityIndex.ipynb" @@ -0,0 +1,10053 @@ +{ + "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" + ] + }, + { + "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: 066c7874-2012-7189-8000-1e79898a8a3c\n", + "results: [{'date': datetime.date(2002, 12, 18), 'WALCL': 719542.0, 'WLRRAL': 21905....\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": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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series_idtitleobservation_startobservation_endfrequencyfrequency_shortunitsunits_shortseasonal_adjustmentseasonal_adjustment_shortlast_updatedpopularitygroup_popularitynotes
0WALCLAssets: Total Assets: Total Assets (Less Elimi...2002-12-182024-08-14Weekly, As of WednesdayWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2024-08-15 15:37:22-05:009494NaN
1H41RESPPALDKNWWAssets: Liquidity and Credit Facilities: Loans...2002-12-182024-08-14WeeklyWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2024-08-15 15:37:01-05:007676NaN
2TREASTAssets: Securities Held Outright: U.S. Treasur...2002-12-182024-08-14Weekly, As of WednesdayWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2024-08-15 15:36:54-05:007171The total face value of U.S. Treasury securiti...
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" + ], + "text/plain": [ + " series_id title \\\n", + "0 WALCL Assets: Total Assets: Total Assets (Less Elimi... \n", + "1 H41RESPPALDKNWW Assets: Liquidity and Credit Facilities: Loans... \n", + "2 TREAST Assets: Securities Held Outright: U.S. Treasur... \n", + "\n", + " observation_start observation_end frequency frequency_short \\\n", + "0 2002-12-18 2024-08-14 Weekly, As of Wednesday W \n", + "1 2002-12-18 2024-08-14 Weekly W \n", + "2 2002-12-18 2024-08-14 Weekly, As of Wednesday W \n", + "\n", + " units units_short seasonal_adjustment \\\n", + "0 Millions of U.S. Dollars Mil. of U.S. $ Not Seasonally Adjusted \n", + "1 Millions of U.S. Dollars Mil. of U.S. $ Not Seasonally Adjusted \n", + "2 Millions of U.S. Dollars Mil. of U.S. $ Not Seasonally Adjusted \n", + "\n", + " seasonal_adjustment_short last_updated popularity \\\n", + "0 NSA 2024-08-15 15:37:22-05:00 94 \n", + "1 NSA 2024-08-15 15:37:01-05:00 76 \n", + "2 NSA 2024-08-15 15:36:54-05:00 71 \n", + "\n", + " group_popularity notes \n", + "0 94 NaN \n", + "1 76 NaN \n", + "2 71 The total face value of U.S. Treasury securiti... " + ] + }, + "execution_count": 4, + "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": 5, + "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 ...2002-12-182024-08-14Weekly, As of WednesdayWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2024-08-15 15:37:49-05:00Reverse repurchase agreements are transactions...6363
1WLRRAFOIALLiabilities and Capital: Liabilities: Reverse ...2002-12-182024-08-14Weekly, As of WednesdayWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2024-08-15 15:37:36-05:00Reverse repurchase agreements are transactions...4040
2WLRRAOLLiabilities and Capital: Liabilities: Reverse ...2002-12-182024-08-14Weekly, As of WednesdayWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2024-08-15 15:37:40-05:00NaN2929
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" + ], + "text/plain": [ + " series_id title \\\n", + "0 WLRRAL Liabilities and Capital: Liabilities: Reverse ... \n", + "1 WLRRAFOIAL Liabilities and Capital: Liabilities: Reverse ... \n", + "2 WLRRAOL Liabilities and Capital: Liabilities: Reverse ... \n", + "\n", + " observation_start observation_end frequency frequency_short \\\n", + "0 2002-12-18 2024-08-14 Weekly, As of Wednesday W \n", + "1 2002-12-18 2024-08-14 Weekly, As of Wednesday W \n", + "2 2002-12-18 2024-08-14 Weekly, As of Wednesday W \n", + "\n", + " units units_short seasonal_adjustment \\\n", + "0 Millions of U.S. Dollars Mil. of U.S. $ Not Seasonally Adjusted \n", + "1 Millions of U.S. Dollars Mil. of U.S. $ Not Seasonally Adjusted \n", + "2 Millions of U.S. Dollars Mil. of U.S. $ Not Seasonally Adjusted \n", + "\n", + " seasonal_adjustment_short last_updated \\\n", + "0 NSA 2024-08-15 15:37:49-05:00 \n", + "1 NSA 2024-08-15 15:37:36-05:00 \n", + "2 NSA 2024-08-15 15:37:40-05:00 \n", + "\n", + " notes popularity \\\n", + "0 Reverse repurchase agreements are transactions... 63 \n", + "1 Reverse repurchase agreements are transactions... 40 \n", + "2 NaN 29 \n", + "\n", + " group_popularity \n", + "0 63 \n", + "1 40 \n", + "2 29 " + ] + }, + "execution_count": 5, + "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": 6, + "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...2002-12-182024-08-14Weekly, As of WednesdayWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2024-08-15 15:38:33-05:00This account is the primary operational accoun...6464
1D2WLTGALLiabilities and Capital: Liabilities: Deposits...2002-12-182024-08-14Weekly, As of WednesdayWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2024-08-15 15:38:37-05:00NaN6060
2WLDLCLLiabilities and Capital: Liabilities: Deposits...2002-12-182024-08-14Weekly, As of WednesdayWMillions of U.S. DollarsMil. of U.S. $Not Seasonally AdjustedNSA2024-08-15 15:36:57-05:00This item is the sum of \"Term deposits held by...2727
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" + ], + "text/plain": [ + " series_id title \\\n", + "0 WDTGAL Liabilities and Capital: Liabilities: Deposits... \n", + "1 D2WLTGAL Liabilities and Capital: Liabilities: Deposits... \n", + "2 WLDLCL Liabilities and Capital: Liabilities: Deposits... \n", + "\n", + " observation_start observation_end frequency frequency_short \\\n", + "0 2002-12-18 2024-08-14 Weekly, As of Wednesday W \n", + "1 2002-12-18 2024-08-14 Weekly, As of Wednesday W \n", + "2 2002-12-18 2024-08-14 Weekly, As of Wednesday W \n", + "\n", + " units units_short seasonal_adjustment \\\n", + "0 Millions of U.S. Dollars Mil. of U.S. $ Not Seasonally Adjusted \n", + "1 Millions of U.S. Dollars Mil. of U.S. $ Not Seasonally Adjusted \n", + "2 Millions of U.S. Dollars Mil. of U.S. $ Not Seasonally Adjusted \n", + "\n", + " seasonal_adjustment_short last_updated \\\n", + "0 NSA 2024-08-15 15:38:33-05:00 \n", + "1 NSA 2024-08-15 15:38:37-05:00 \n", + "2 NSA 2024-08-15 15:36:57-05:00 \n", + "\n", + " notes popularity \\\n", + "0 This account is the primary operational accoun... 64 \n", + "1 NaN 60 \n", + "2 This item is the sum of \"Term deposits held by... 27 \n", + "\n", + " group_popularity \n", + "0 64 \n", + "1 60 \n", + "2 27 " + ] + }, + "execution_count": 6, + "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": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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series_idtitleobservation_startobservation_endfrequencyfrequency_shortunitsunits_shortseasonal_adjustmentseasonal_adjustment_shortlast_updatednotespopularitygroup_popularity
0SP500S&P 5002014-08-222024-08-21Daily, CloseDIndexIndexNot Seasonally AdjustedNSA2024-08-21 19:21:03-05:00The observations for the S&P 500 represent the...8383
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" + ], + "text/plain": [ + " series_id title observation_start observation_end frequency \\\n", + "0 SP500 S&P 500 2014-08-22 2024-08-21 Daily, Close \n", + "\n", + " frequency_short units units_short seasonal_adjustment \\\n", + "0 D Index Index Not Seasonally Adjusted \n", + "\n", + " seasonal_adjustment_short last_updated \\\n", + "0 NSA 2024-08-21 19:21:03-05:00 \n", + "\n", + " notes popularity \\\n", + "0 The observations for the S&P 500 represent the... 83 \n", + "\n", + " group_popularity \n", + "0 83 " + ] + }, + "execution_count": 7, + "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": 8, + "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']}\")" + ] + }, + { + "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": 9, + "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
2014-08-274413736.0282002.029547.02000.12
2014-09-034415587.0250306.021036.02000.72
2014-09-104421408.0267602.031872.01995.69
2014-09-174449588.0252224.0123965.02001.57
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" + ], + "text/plain": [ + " WALCL WLRRAL WDTGAL SP500\n", + "date \n", + "2014-08-27 4413736.0 282002.0 29547.0 2000.12\n", + "2014-09-03 4415587.0 250306.0 21036.0 2000.72\n", + "2014-09-10 4421408.0 267602.0 31872.0 1995.69\n", + "2014-09-17 4449588.0 252224.0 123965.0 2001.57" + ] + }, + "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": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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WALCLWLRRALWDTGALSP500USD Liquidity Index
date
2024-07-247205455.0805967.0767419.05427.135632069.0
2024-07-317178391.0813261.0854001.05522.305511129.0
2024-08-077175256.0681881.0785233.05199.505708142.0
2024-08-147177688.0722198.0788823.05455.215666667.0
\n", + "
" + ], + "text/plain": [ + " WALCL WLRRAL WDTGAL SP500 USD Liquidity Index\n", + "date \n", + "2024-07-24 7205455.0 805967.0 767419.0 5427.13 5632069.0\n", + "2024-07-31 7178391.0 813261.0 854001.0 5522.30 5511129.0\n", + "2024-08-07 7175256.0 681881.0 785233.0 5199.50 5708142.0\n", + "2024-08-14 7177688.0 722198.0 788823.0 5455.21 5666667.0" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "liquidity_index[\"USD Liquidity Index\"] = (\n", + " liquidity_index[\"WALCL\"] - liquidity_index[\"WLRRAL\"] - liquidity_index[\"WDTGAL\"]\n", + ")\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": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" 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y=liquidity_index[\"USD Liquidity Index\"] / 1000,\n", + " name=\"USD Liquidity Index (Billions)\",\n", + " yaxis=\"y1\",\n", + ")\n", + "\n", + "fig.add_scatter(\n", + " x=liquidity_index.index,\n", + " y=liquidity_index[\"SP500\"],\n", + " name=\"S&P 500 Index\",\n", + " yaxis=\"y2\",\n", + ")\n", + "\n", + "fig.update_layout(\n", + " yaxis=dict(\n", + " title=\"USD Liquidity Index (Billions)\",\n", + " side=\"left\",\n", + " position=0,\n", + " titlefont=dict(size=12),\n", + " showgrid=False,\n", + " ),\n", + " yaxis2=dict(\n", + " title=\"S&P 500 Index\",\n", + " side=\"right\",\n", + " overlaying=\"y\",\n", + " position=1,\n", + " titlefont=dict(size=12),\n", + " ),\n", + " title=\"USD Liquidity Index vs. S&P 500 Index\",\n", + " title_y=0.90,\n", + " title_x=0.5,\n", + " autosize=True,\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": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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USD Liquidity IndexSP500
date
2024-07-310.6435780.970490
2024-08-070.6988350.885139
2024-08-140.6872020.952750
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" + ], + "text/plain": [ + " USD Liquidity Index SP500\n", + "date \n", + "2024-07-31 0.643578 0.970490\n", + "2024-08-07 0.698835 0.885139\n", + "2024-08-14 0.687202 0.952750" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_axis = liquidity_index[[\"USD Liquidity Index\", \"SP500\"]]\n", + "\n", + "\n", + "def absolute_maximum_scale(series):\n", + " return series / series.abs().max()\n", + "\n", + "\n", + "def min_max_scaling(series):\n", + " return (series - series.min()) / (series.max() - series.min())\n", + "\n", + "\n", + "def z_score_standardization(series):\n", + " return (series - series.mean()) / series.std()\n", + "\n", + "\n", + "methods = {\n", + " \"z\": z_score_standardization,\n", + " \"m\": min_max_scaling,\n", + " \"a\": absolute_maximum_scale,\n", + "}\n", + "\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", + "\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": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "variable=USD Liquidity Index
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"title": { + "text": "USD Liquidity Index vs. S&P 500 Index (Normalized)", + "x": 0.5, + "y": 0.9 + } + } + } + }, + "metadata": {}, + "output_type": "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", + "\n", + "fig.add_scatter(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": { + 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