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OpenBB/examples/v3/usdLiquidityIndex.ipynb
Danglewood c047cfeddd docs/new-working-docs: New working docs branch (#5768) 
* move old example notebooks to v3 folder, update loadHistoricalPriceData and add findSymbols

* Create README.md

* Update README.md

* Update README.md

* Update README.md

* Vertical diagram instead

* important classes

* core dependencies

* folder structure

* devtools

* swap order

* fix github pages deploy issue

* development index

* delete expectations for developers

* devtools

* obbject stuff

* add

* installation

* hub sync

* idk if i committed already

* add usage page for financial statements

* add link to reference page

* syntax

* numbers

* nearly --> more than

* easier to read code blocks

* codespell

* removing mention to provider

* add 'orientation' for to_dict

* info card for output user preference

* put it in a code block

* correct user_settings.json example

* syntax corrections

* fix sentence

* get rid of floating point mathc

* get rid of other floating point math

* add companion example notebook for financial statements, and new section

* historical prices usage page

* codespell

* add commitment of traders section to usage

* add a note

* codespell

* get rid of equals sign

* Cleanup docs. with @the-praxs

* install

* obbject extend

* spell

* HeadTitle thingies

* Update obbject_extensions.md

* Update obbject_extensions.md

* typo

* Update architectural_considerations.md

* point to the TET pattern

* optional vs required

* reviewing the  patterns. removed the --pre flag under the assumption this will be the final docs for the release cc @deeleeramone

* add a walkthrough

* spelling

* Add more in the introduction

* validator examples

* update Copper-to-Gold notebook example for V4

* Update README.md

* QA Caveats w @the-praxs

* info on class outputs

* merge add data sources and add data points

* disclaimer on cookiecutter

* add missing import to example

* slight syntax change

* missing code block

* missing import

* update intrinio example syntax

* intrinio syntax update

* urls

* core and provider merged

* example syntax

* grammar

* Update API banner link

* quick-fix-docs cc @minhhoang1023

* Update pip install command for v3

* remove --pre note block

* update landing image

---------

Co-authored-by: Henrique Joaquim <h.joaquim@campus.fct.unl.pt>
Co-authored-by: Luqman <luqazino@gmail.com>
Co-authored-by: James Maslek <jmaslek11@gmail.com>
Co-authored-by: Igor Radovanovic <74266147+IgorWounds@users.noreply.github.com>
Co-authored-by: montezdesousa <79287829+montezdesousa@users.noreply.github.com>
Co-authored-by: Theodore Aptekarev <aptekarev@gmail.com>
Co-authored-by: Pratyush Shukla <ps4534@nyu.edu>
2023-12-01 01:09:25 +00:00

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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Calculating the USD Liquidity Index with the OpenBB SDK\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 `fred` function in the `economy` module. First thing is to import the SDK, and we will also import Pandas to conduct some DataFrame operations."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from openbb_terminal.sdk import openbb"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"data = openbb.economy.fred([\"WALCL\", \"WLRRAL\", \"WDTGAL\", \"SP500\"])"
]
},
{
"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:\n",
"\n",
"```python\n",
"openbb.economy.fred_notes(\"Wednesday Levels\")\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
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" <th>notes</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>WALCL</td>\n",
" <td>2023-05-22</td>\n",
" <td>2023-05-22</td>\n",
" <td>Assets: Total Assets: Total Assets (Less\\nElim...</td>\n",
" <td>2002-12-18</td>\n",
" <td>2023-05-17</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>2023-05-18 15:34:02-05</td>\n",
" <td>97</td>\n",
" <td>97</td>\n",
" <td>No description provided.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>TREAST</td>\n",
" <td>2023-05-22</td>\n",
" <td>2023-05-22</td>\n",
" <td>Assets: Securities Held Outright: U.S. Treasur...</td>\n",
" <td>2002-12-18</td>\n",
" <td>2023-05-17</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>2023-05-18 15:35:02-05</td>\n",
" <td>73</td>\n",
" <td>73</td>\n",
" <td>The total face value of U.S. Treasury securiti...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>WSHOMCB</td>\n",
" <td>2023-05-22</td>\n",
" <td>2023-05-22</td>\n",
" <td>Assets: Securities Held Outright: Mortgage-Bac...</td>\n",
" <td>2002-12-18</td>\n",
" <td>2023-05-17</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>2023-05-18 15:35:04-05</td>\n",
" <td>72</td>\n",
" <td>72</td>\n",
" <td>The current face value of mortgage-backed obli...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id realtime_start realtime_end \\\n",
"0 WALCL 2023-05-22 2023-05-22 \n",
"1 TREAST 2023-05-22 2023-05-22 \n",
"2 WSHOMCB 2023-05-22 2023-05-22 \n",
"\n",
" title observation_start \\\n",
"0 Assets: Total Assets: Total Assets (Less\\nElim... 2002-12-18 \n",
"1 Assets: Securities Held Outright: U.S. Treasur... 2002-12-18 \n",
"2 Assets: Securities Held Outright: Mortgage-Bac... 2002-12-18 \n",
"\n",
" observation_end frequency frequency_short \\\n",
"0 2023-05-17 Weekly, As of Wednesday W \n",
"1 2023-05-17 Weekly, As of Wednesday W \n",
"2 2023-05-17 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 2023-05-18 15:34:02-05 97 \n",
"1 NSA 2023-05-18 15:35:02-05 73 \n",
"2 NSA 2023-05-18 15:35:04-05 72 \n",
"\n",
" group_popularity notes \n",
"0 97 No description provided. \n",
"1 73 The total face value of U.S. Treasury securiti... \n",
"2 72 The current face value of mortgage-backed obli... "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The first result is the series we are looking for as the starting value.\n",
"\n",
"openbb.economy.fred_notes(\"Wednesday Levels\").head(3)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
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" <th>0</th>\n",
" <td>WLRRAL</td>\n",
" <td>2023-05-22</td>\n",
" <td>2023-05-22</td>\n",
" <td>Liabilities and Capital: Liabilities: Reverse\\...</td>\n",
" <td>2002-12-18</td>\n",
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" <td>Reverse repurchase agreements are transactions...</td>\n",
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" <th>1</th>\n",
" <td>WLRRAFOIAL</td>\n",
" <td>2023-05-22</td>\n",
" <td>2023-05-22</td>\n",
" <td>Liabilities and Capital: Liabilities: Reverse\\...</td>\n",
" <td>2002-12-18</td>\n",
" <td>2023-05-17</td>\n",
" <td>Weekly, As of Wednesday</td>\n",
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" <td>NSA</td>\n",
" <td>2023-05-18 15:34:14-05</td>\n",
" <td>43</td>\n",
" <td>43</td>\n",
" <td>Reverse repurchase agreements are transactions...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>WLRRAOL</td>\n",
" <td>2023-05-22</td>\n",
" <td>2023-05-22</td>\n",
" <td>Liabilities and Capital: Liabilities: Reverse\\...</td>\n",
" <td>2002-12-18</td>\n",
" <td>2023-05-17</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>2023-05-18 15:34:17-05</td>\n",
" <td>20</td>\n",
" <td>20</td>\n",
" <td>No description provided.</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id realtime_start realtime_end \\\n",
"0 WLRRAL 2023-05-22 2023-05-22 \n",
"1 WLRRAFOIAL 2023-05-22 2023-05-22 \n",
"2 WLRRAOL 2023-05-22 2023-05-22 \n",
"\n",
" title observation_start \\\n",
"0 Liabilities and Capital: Liabilities: Reverse\\... 2002-12-18 \n",
"1 Liabilities and Capital: Liabilities: Reverse\\... 2002-12-18 \n",
"2 Liabilities and Capital: Liabilities: Reverse\\... 2002-12-18 \n",
"\n",
" observation_end frequency frequency_short \\\n",
"0 2023-05-17 Weekly, As of Wednesday W \n",
"1 2023-05-17 Weekly, As of Wednesday W \n",
"2 2023-05-17 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 2023-05-18 15:34:11-05 57 \n",
"1 NSA 2023-05-18 15:34:14-05 43 \n",
"2 NSA 2023-05-18 15:34:17-05 20 \n",
"\n",
" group_popularity notes \n",
"0 57 Reverse repurchase agreements are transactions... \n",
"1 43 Reverse repurchase agreements are transactions... \n",
"2 20 No description provided. "
]
},
"execution_count": 25,
"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",
"openbb.economy.fred_notes(\"Wednesday Levels Reverse Repo\").head(3)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
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" <th>0</th>\n",
" <td>WDTGAL</td>\n",
" <td>2023-05-24</td>\n",
" <td>2023-05-24</td>\n",
" <td>Liabilities and Capital: Liabilities: Deposits...</td>\n",
" <td>2002-12-18</td>\n",
" <td>2023-05-17</td>\n",
" <td>Weekly, As of Wednesday</td>\n",
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" <td>Millions of U.S. Dollars</td>\n",
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" <td>NSA</td>\n",
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" <td>68</td>\n",
" <td>This account is the primary operational accoun...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>D2WLTGAL</td>\n",
" <td>2023-05-24</td>\n",
" <td>2023-05-24</td>\n",
" <td>Liabilities and Capital: Liabilities: Deposits...</td>\n",
" <td>2002-12-18</td>\n",
" <td>2023-05-17</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>2023-05-18 15:34:12-05</td>\n",
" <td>64</td>\n",
" <td>64</td>\n",
" <td>No description provided.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>WLDLCL</td>\n",
" <td>2023-05-24</td>\n",
" <td>2023-05-24</td>\n",
" <td>Liabilities and Capital: Liabilities: Deposits...</td>\n",
" <td>2002-12-18</td>\n",
" <td>2023-05-17</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>2023-05-18 15:34:18-05</td>\n",
" <td>29</td>\n",
" <td>29</td>\n",
" <td>This item is the sum of \"Term deposits held by...</td>\n",
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" </tbody>\n",
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"text/plain": [
" id realtime_start realtime_end \\\n",
"0 WDTGAL 2023-05-24 2023-05-24 \n",
"1 D2WLTGAL 2023-05-24 2023-05-24 \n",
"2 WLDLCL 2023-05-24 2023-05-24 \n",
"\n",
" title observation_start \\\n",
"0 Liabilities and Capital: Liabilities: Deposits... 2002-12-18 \n",
"1 Liabilities and Capital: Liabilities: Deposits... 2002-12-18 \n",
"2 Liabilities and Capital: Liabilities: Deposits... 2002-12-18 \n",
"\n",
" observation_end frequency frequency_short \\\n",
"0 2023-05-17 Weekly, As of Wednesday W \n",
"1 2023-05-17 Weekly, As of Wednesday W \n",
"2 2023-05-17 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",
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"1 NSA 2023-05-18 15:34:12-05 64 \n",
"2 NSA 2023-05-18 15:34:18-05 29 \n",
"\n",
" group_popularity notes \n",
"0 68 This account is the primary operational accoun... \n",
"1 64 No description provided. \n",
"2 29 This item is the sum of \"Term deposits held by... "
]
},
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"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",
"openbb.economy.fred_notes(\"Wednesday Levels Treasury General\").head(3)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
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" <td>2023-05-24</td>\n",
" <td>2023-05-24</td>\n",
" <td>S&amp;P 500</td>\n",
" <td>2013-05-24</td>\n",
" <td>2023-05-23</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>2023-05-23 19:10:47-05</td>\n",
" <td>83</td>\n",
" <td>83</td>\n",
" <td>The observations for the S&amp;P 500 represent the...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>VXVCLS</td>\n",
" <td>2023-05-24</td>\n",
" <td>2023-05-24</td>\n",
" <td>CBOE S&amp;P 500 3-Month Volatility Index</td>\n",
" <td>2007-12-04</td>\n",
" <td>2023-05-23</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>2023-05-24 08:36:05-05</td>\n",
" <td>55</td>\n",
" <td>55</td>\n",
" <td>Copyright, 2016, Chicago Board Options Exchang...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id realtime_start realtime_end title \\\n",
"0 SP500 2023-05-24 2023-05-24 S&P 500 \n",
"1 VXVCLS 2023-05-24 2023-05-24 CBOE S&P 500 3-Month Volatility Index \n",
"\n",
" observation_start observation_end frequency frequency_short units \\\n",
"0 2013-05-24 2023-05-23 Daily, Close D Index \n",
"1 2007-12-04 2023-05-23 Daily, Close D Index \n",
"\n",
" units_short seasonal_adjustment seasonal_adjustment_short \\\n",
"0 Index Not Seasonally Adjusted NSA \n",
"1 Index Not Seasonally Adjusted NSA \n",
"\n",
" last_updated popularity group_popularity \\\n",
"0 2023-05-23 19:10:47-05 83 83 \n",
"1 2023-05-24 08:36:05-05 55 55 \n",
"\n",
" notes \n",
"0 The observations for the S&P 500 represent the... \n",
"1 Copyright, 2016, Chicago Board Options Exchang... "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Several major equity indices are published to FRED, S&P 500 is one of them.\n",
"\n",
"openbb.economy.fred_notes(\"S&P 500 Index\").head(2)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's inspect the object that was returned containing all four series. Notice that the object is a Tuple, and it contains the time series data as the first element and the second element is a dictionary of meta data. 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": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'WALCL': {'title': 'Assets: Total Assets: Total Assets (Less Eliminations from Consolidation): Wednesday Level',\n",
" 'units': 'Mil. of U.S. $'},\n",
" 'WLRRAL': {'title': 'Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level',\n",
" 'units': 'Mil. of U.S. $'},\n",
" 'WDTGAL': {'title': 'Liabilities and Capital: Liabilities: Deposits with F.R. Banks, Other Than Reserve Balances: U.S. Treasury, General Account: Wednesday Level',\n",
" 'units': 'Mil. of U.S. $'},\n",
" 'SP500': {'title': 'S&P 500', 'units': 'Index'}}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[1]"
]
},
{
"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": 26,
"metadata": {},
"outputs": [
{
"data": {
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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",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2013-05-29</th>\n",
" <td>3385128.0</td>\n",
" <td>88177.0</td>\n",
" <td>14298.0</td>\n",
" <td>1648.36</td>\n",
" <td>3282653.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-06-05</th>\n",
" <td>3400183.0</td>\n",
" <td>94279.0</td>\n",
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" <td>1608.90</td>\n",
" <td>3264879.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-06-12</th>\n",
" <td>3410842.0</td>\n",
" <td>86277.0</td>\n",
" <td>39284.0</td>\n",
" <td>1612.52</td>\n",
" <td>3285281.0</td>\n",
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" <th>2013-06-19</th>\n",
" <td>3470530.0</td>\n",
" <td>88367.0</td>\n",
" <td>117287.0</td>\n",
" <td>1628.93</td>\n",
" <td>3264876.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" WALCL WLRRAL WDTGAL SP500 USD Liquidity Index\n",
"2013-05-29 3385128.0 88177.0 14298.0 1648.36 3282653.0\n",
"2013-06-05 3400183.0 94279.0 41025.0 1608.90 3264879.0\n",
"2013-06-12 3410842.0 86277.0 39284.0 1612.52 3285281.0\n",
"2013-06-19 3470530.0 88367.0 117287.0 1628.93 3264876.0"
]
},
"execution_count": 26,
"metadata": {},
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],
"source": [
"data = data[0].dropna()\n",
"\n",
"data.head(4)"
]
},
{
"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": 24,
"metadata": {},
"outputs": [
{
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" <th>WLRRAL</th>\n",
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" <th>USD Liquidity Index</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2023-04-26</th>\n",
" <td>8562768.0</td>\n",
" <td>2638805.0</td>\n",
" <td>296209.0</td>\n",
" <td>4055.99</td>\n",
" <td>5627754.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-05-03</th>\n",
" <td>8503994.0</td>\n",
" <td>2640951.0</td>\n",
" <td>188309.0</td>\n",
" <td>4090.75</td>\n",
" <td>5674734.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-05-10</th>\n",
" <td>8503017.0</td>\n",
" <td>2618178.0</td>\n",
" <td>154808.0</td>\n",
" <td>4137.64</td>\n",
" <td>5730031.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-05-17</th>\n",
" <td>8456760.0</td>\n",
" <td>2598627.0</td>\n",
" <td>68332.0</td>\n",
" <td>4158.77</td>\n",
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"</div>"
],
"text/plain": [
" WALCL WLRRAL WDTGAL SP500 USD Liquidity Index\n",
"2023-04-26 8562768.0 2638805.0 296209.0 4055.99 5627754.0\n",
"2023-05-03 8503994.0 2640951.0 188309.0 4090.75 5674734.0\n",
"2023-05-10 8503017.0 2618178.0 154808.0 4137.64 5730031.0\n",
"2023-05-17 8456760.0 2598627.0 68332.0 4158.77 5789801.0"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"liquidity_index = pd.DataFrame(data)\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": 23,
"metadata": {},
"outputs": [
{
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"source": [
"# Import a Terminal function for plotting with two y-axis. Each axis will require its own DataFrame.\n",
"\n",
"from openbb_terminal.economy.plot_view import show_plot\n",
"\n",
"y_axis1 = pd.DataFrame(liquidity_index[[\"USD Liquidity Index\"]])\n",
"y_axis2 = pd.DataFrame(liquidity_index[[\"SP500\"]])\n",
"\n",
"show_plot(y_axis1, y_axis2)"
]
},
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"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": 27,
"metadata": {},
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\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",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2023-05-03</th>\n",
" <td>0.689465</td>\n",
" <td>0.779826</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-05-10</th>\n",
" <td>0.704974</td>\n",
" <td>0.794526</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-05-17</th>\n",
" <td>0.721738</td>\n",
" <td>0.801151</td>\n",
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"text/plain": [
" USD Liquidity Index SP500\n",
"2023-05-03 0.689465 0.779826\n",
"2023-05-10 0.704974 0.794526\n",
"2023-05-17 0.721738 0.801151"
]
},
"execution_count": 27,
"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: pd.DataFrame, method: str = \"z\") -> pd.DataFrame: \n",
" for col in data.columns:\n",
" data[col] = methods[f\"{method}\"](data[col])\n",
"\n",
" return data\n",
"\n",
"normalized = normalize(y_axis, method = \"m\")\n",
"\n",
"normalized.tail(3)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use `openbb.forecast.plot` to make a fast chart of multiple columns on the same y-axis."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
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"title": {
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"x": 0.5,
"y": 0.98
},
"xaxis": {
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null,
604800000
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},
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],
"source": [
"fig = openbb.forecast.plot(normalize(y_axis, method = \"m\"), columns = normalized.columns, external_axes = True)\n",
"\n",
"fig.update({'layout': \n",
" {'yaxis': {'title': 'Min/Max Normalized'},\n",
" 'title': 'USD Liquidity Index vs. S&P 500',\n",
"}})\n",
"fig.update_layout(\n",
" {\n",
" 'title_y':0.98,\n",
" 'title_x':0.5,\n",
" },\n",
" legend=dict(\n",
" yanchor=\"top\",\n",
" y=1,\n",
" xanchor=\"left\",\n",
" x=0\n",
" )\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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"display_name": "obb",
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