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OpenBB/examples/copperToGoldRatio.ipynb
Danglewood 7c193656a1 Creates examples folder for SDK Notebook examples (#5057) 
* Creates examples folder for SDK Notebook examples 

This commit creates a folder and readme file for SDK examples as Jupyter Notebooks.

* Four notebook files added.

This commit adds four notebook examples.

* historical prices draft 2

* removes spaces from file name.

* fix one mistake

* rename file

* renames file

* updates realized vol notebook

* Renaming file.

* Delete old file name.

* updates usdLiquidtyIndex

* update copperToGoldRatio

* updates README.md

* spelling

* updates copperToGold load data cell

---------

Co-authored-by: James Maslek <jmaslek11@gmail.com>
2023-05-27 20:06:09 +00:00

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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# The Copper-to-Gold Ratio Using the OpenBB SDK\n",
"\n",
"The copper-to-gold ratio is known as a leading economic indicator. It is most commonly paired as a time series with the ten-year US Treasury yield. The notable events are the divergences in directional movement, signaling a fundamental regime change that will unfold over months and years. Not something to go YOLO into, but a metric to shape a long-term view of global economic conditions.\n",
"\n",
"The ratio is defined as dividing the spot price of one ounce of copper by an ounce gold. How much copper is bought with one ouce of gold. Sounds simple enough, divide the price of copper by the price of gold, done. The OpenBB SDK can make quick work out of this task, really quick. Let's explore."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# Import the SDK and Pandas for some calculations.\n",
"from datetime import datetime\n",
"import pandas as pd\n",
"from openbb_terminal.sdk import openbb\n",
"\n",
"from openbb_terminal import OpenBBFigure"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"The most accessible data is going to be the continuous front-month futures contracts for physical delivery, listed on the CME. We'll create a Pandas Series for each asset, requesting all the daily historical data that is available."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"data = pd.DataFrame(\n",
" openbb.futures.historical(\n",
" symbols = [\"GC\", \"HG\"],\n",
" start_date=\"2000-01-01\",\n",
" end_date = str(datetime.now().date())\n",
" )[\"Close\"]\n",
").rename(\n",
" columns={\"GC\": \"Gold\", \"HG\": \"Copper\"}\n",
")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's inspect the results."
]
},
{
"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",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Gold</th>\n",
" <th>Copper</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>2000-08-30</th>\n",
" <td>273.899994</td>\n",
" <td>0.885</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-08-31</th>\n",
" <td>278.299988</td>\n",
" <td>0.885</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Gold Copper\n",
"Date \n",
"2000-08-30 273.899994 0.885\n",
"2000-08-31 278.299988 0.885"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head(2)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"To get the copper-to-gold ratio, divide the two columns along each row."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Gold</th>\n",
" <th>Copper</th>\n",
" <th>Copper/Gold Ratio</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
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" <tr>\n",
" <th>2023-05-24</th>\n",
" <td>1962.800049</td>\n",
" <td>3.5535</td>\n",
" <td>0.001810</td>\n",
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" <tr>\n",
" <th>2023-05-25</th>\n",
" <td>1943.099976</td>\n",
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"text/plain": [
" Gold Copper Copper/Gold Ratio\n",
"Date \n",
"2023-05-24 1962.800049 3.5535 0.001810\n",
"2023-05-25 1943.099976 3.5805 0.001843"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"data[\"Copper/Gold Ratio\"] = data['Copper']/data['Gold']\n",
"\n",
"data.tail(2)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Because the numbers are so small, the ratio is often be presented as a % value. 0.2% is a popular way to display the value. However, to plot it on the same y-axis as a Treasury yield, it needs to be multiplied by 1000. Let's alter the block above to include this."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Gold</th>\n",
" <th>Copper</th>\n",
" <th>Copper/Gold Ratio</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
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" <th>2023-05-24</th>\n",
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" <th>2023-05-25</th>\n",
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"text/plain": [
" Gold Copper Copper/Gold Ratio\n",
"Date \n",
"2023-05-24 1962.800049 3.5535 1.810424\n",
"2023-05-25 1943.099976 3.5805 1.842674"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"data[\"Copper/Gold Ratio\"] = (data['Copper']/data['Gold']) * 1000\n",
"\n",
"data.tail(2)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's add a column for the daily 10 Year US Treasury Yield. This can be requested using the `fred` function within the `economy` module. The first line in the block below requests the data, the second assigns it to a column in the target DataFrame."
]
},
{
"cell_type": "code",
"execution_count": 16,
"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>Gold</th>\n",
" <th>Copper</th>\n",
" <th>Copper/Gold Ratio</th>\n",
" <th>US 10-Year Constant Maturity</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
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" <td>273.899994</td>\n",
" <td>0.885</td>\n",
" <td>3.231106</td>\n",
" <td>5.81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-08-31</th>\n",
" <td>278.299988</td>\n",
" <td>0.885</td>\n",
" <td>3.180022</td>\n",
" <td>5.73</td>\n",
" </tr>\n",
" </tbody>\n",
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],
"text/plain": [
" Gold Copper Copper/Gold Ratio \\\n",
"Date \n",
"2000-08-30 273.899994 0.885 3.231106 \n",
"2000-08-31 278.299988 0.885 3.180022 \n",
"\n",
" US 10-Year Constant Maturity \n",
"Date \n",
"2000-08-30 5.81 \n",
"2000-08-31 5.73 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"us10year = openbb.economy.fred(['DGS10'])[0]\n",
"data['US 10-Year Constant Maturity'] = us10year[\"2000-08-30\":]\n",
"\n",
"data.head(2)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"With all the data collected, let's draw the chart to visualize the relationship."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
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2.030145471613463,
2.0067626607659914,
2.0713128598852055,
2.0718413217236735,
2.062204315864726,
2.076347288257348,
2.101784804818361,
2.0853952247836034,
2.065797514305323,
2.0648234084637664,
2.1161392395617837,
2.133858268871804,
2.1104507541471844,
2.087669354735668,
2.181189851118968,
2.183115596048544,
2.113930307220218,
2.0866189984895485,
2.1334346666886814,
2.1300967261408053,
2.1240705153883,
2.220926726701905,
2.1649268787724205,
2.1643011157676617,
2.1878837227255725,
2.168771009494187,
2.237259329107853,
2.1735453722026152,
2.16456186983732,
2.1364918820143957,
2.0953543793433993,
2.0731776464796696,
2.0556578869319355,
2.0793303481830323,
2.073184718976641,
2.0683852109084335,
2.0748808389926157,
2.07618397606545,
2.1360252321380933,
2.1175947715175276,
2.1426582756724044,
2.144109961381813,
2.154241772052033,
2.1593391110802926,
2.169452778068768,
2.1556086870295963,
2.1333895316119946,
2.1167097958715755,
2.1438450899031816,
2.1173757116990646,
2.101396581980103,
2.116126920454839,
2.0912494322594477,
2.095930361312726,
2.0993284226531213,
2.1176011590833967,
2.1123538711180223,
2.1195862411739737,
2.100027534587595,
2.091278855102931,
2.0470729642766,
2.0182966036413688,
2.0808807371897697,
2.0966098371979984,
2.1458321973641103,
2.173007034256194,
2.216206156906001,
2.2083882196255833,
2.1932339071000646,
2.1653418433076537,
2.2100462775274257,
2.2203842864761496,
2.198897085487897,
2.2054089578785825,
2.2074621144794166,
2.195304842102346,
2.1842158859310374,
2.211912421526114,
2.1901897908240415,
2.186020979061155,
2.1915003313675974,
2.1324826003268127,
2.136408578173602,
2.1810081855878924,
2.166702417956411,
2.1843779075424807,
2.1537764429593085,
2.1999251573629097,
2.1620678800827626,
2.2069225332407427,
2.216828550978643,
2.2091375824034527,
2.26140074051682,
2.2353834976215063,
2.3101472620487997,
2.293940975155893,
2.2403740122242204,
2.1865324065357017,
2.206384053122689,
2.240417624098865,
2.2680526375345265,
2.2274339299843033,
2.2070683424969206,
2.2190053859173045,
2.1977506869946026,
2.2295471950225316,
2.216421579168598,
2.1726636958301513,
2.1287336263140104,
2.1039240672302486,
2.0017128408722917,
2.0247524513932427,
1.9867499104287878,
2.008639958008107,
2.0735293349983523,
2.089839706142755,
2.0794461866344705,
2.069270009006389,
2.1022842478842616,
2.0847697919581263,
2.092975851796047,
2.065596009226131,
2.0845605977117985,
2.0462221722270018,
1.9698843338940044,
1.9783264180518243,
2.0010934808058862,
2.0041727785056214,
2.0074320606391325,
2.0311799623942823,
2.0202811805382384,
2.0514934454422753,
2.0389128895283846,
2.0372122649558175,
2.0408982189034255,
2.003387071567461,
2.010608749216209,
1.9873310126104138,
1.9287863370767209,
1.9388628691663428,
1.9420573889958574,
1.9446258645471581,
1.9748916038610382,
1.9103410397223322,
1.8887410527051187,
1.8798827659338713,
1.919054212554218,
1.9335734532996574,
1.9099302780783607,
1.8852499722553808,
1.8352608726240156,
1.844874680500878,
1.85208124495356,
1.837909858558916,
1.8897360139293402,
1.881676441624498,
1.8812857824280314,
1.8604415302580823,
1.8462279208636072,
1.8104238071417849,
1.8426740427428285
]
},
{
"name": "US 10-Year Constant Maturity %",
"type": "scatter",
"x": [
"2000-08-30T00:00:00",
"2000-08-31T00:00:00",
"2000-09-01T00:00:00",
"2000-09-05T00:00:00",
"2000-09-06T00:00:00",
"2000-09-07T00:00:00",
"2000-09-08T00:00:00",
"2000-09-11T00:00:00",
"2000-09-12T00:00:00",
"2000-09-13T00:00:00",
"2000-09-14T00:00:00",
"2000-09-15T00:00:00",
"2000-09-18T00:00:00",
"2000-09-19T00:00:00",
"2000-09-20T00:00:00",
"2000-09-21T00:00:00",
"2000-09-22T00:00:00",
"2000-09-25T00:00:00",
"2000-09-26T00:00:00",
"2000-09-27T00:00:00",
"2000-09-28T00:00:00",
"2000-09-29T00:00:00",
"2000-10-02T00:00:00",
"2000-10-03T00:00:00",
"2000-10-04T00:00:00",
"2000-10-05T00:00:00",
"2000-10-06T00:00:00",
"2000-10-09T00:00:00",
"2000-10-10T00:00:00",
"2000-10-11T00:00:00",
"2000-10-12T00:00:00",
"2000-10-13T00:00:00",
"2000-10-16T00:00:00",
"2000-10-17T00:00:00",
"2000-10-18T00:00:00",
"2000-10-19T00:00:00",
"2000-10-20T00:00:00",
"2000-10-23T00:00:00",
"2000-10-24T00:00:00",
"2000-10-25T00:00:00",
"2000-10-26T00:00:00",
"2000-10-27T00:00:00",
"2000-10-30T00:00:00",
"2000-10-31T00:00:00",
"2000-11-01T00:00:00",
"2000-11-02T00:00:00",
"2000-11-03T00:00:00",
"2000-11-06T00:00:00",
"2000-11-07T00:00:00",
"2000-11-08T00:00:00",
"2000-11-09T00:00:00",
"2000-11-10T00:00:00",
"2000-11-13T00:00:00",
"2000-11-14T00:00:00",
"2000-11-15T00:00:00",
"2000-11-16T00:00:00",
"2000-11-17T00:00:00",
"2000-11-20T00:00:00",
"2000-11-21T00:00:00",
"2000-11-22T00:00:00",
"2000-11-27T00:00:00",
"2000-11-28T00:00:00",
"2000-11-29T00:00:00",
"2000-11-30T00:00:00",
"2000-12-01T00:00:00",
"2000-12-04T00:00:00",
"2000-12-05T00:00:00",
"2000-12-06T00:00:00",
"2000-12-07T00:00:00",
"2000-12-08T00:00:00",
"2000-12-11T00:00:00",
"2000-12-12T00:00:00",
"2000-12-13T00:00:00",
"2000-12-14T00:00:00",
"2000-12-15T00:00:00",
"2000-12-18T00:00:00",
"2000-12-19T00:00:00",
"2000-12-20T00:00:00",
"2000-12-21T00:00:00",
"2000-12-22T00:00:00",
"2000-12-26T00:00:00",
"2000-12-27T00:00:00",
"2000-12-28T00:00:00",
"2000-12-29T00:00:00",
"2001-01-02T00:00:00",
"2001-01-03T00:00:00",
"2001-01-04T00:00:00",
"2001-01-05T00:00:00",
"2001-01-08T00:00:00",
"2001-01-09T00:00:00",
"2001-01-10T00:00:00",
"2001-01-11T00:00:00",
"2001-01-12T00:00:00",
"2001-01-16T00:00:00",
"2001-01-17T00:00:00",
"2001-01-18T00:00:00",
"2001-01-19T00:00:00",
"2001-01-22T00:00:00",
"2001-01-23T00:00:00",
"2001-01-24T00:00:00",
"2001-01-25T00:00:00",
"2001-01-26T00:00:00",
"2001-01-29T00:00:00",
"2001-01-30T00:00:00",
"2001-01-31T00:00:00",
"2001-02-01T00:00:00",
"2001-02-02T00:00:00",
"2001-02-05T00:00:00",
"2001-02-06T00:00:00",
"2001-02-07T00:00:00",
"2001-02-08T00:00:00",
"2001-02-09T00:00:00",
"2001-02-12T00:00:00",
"2001-02-13T00:00:00",
"2001-02-14T00:00:00",
"2001-02-15T00:00:00",
"2001-02-16T00:00:00",
"2001-02-20T00:00:00",
"2001-02-21T00:00:00",
"2001-02-22T00:00:00",
"2001-02-23T00:00:00",
"2001-02-26T00:00:00",
"2001-02-27T00:00:00",
"2001-02-28T00:00:00",
"2001-03-01T00:00:00",
"2001-03-02T00:00:00",
"2001-03-05T00:00:00",
"2001-03-06T00:00:00",
"2001-03-07T00:00:00",
"2001-03-08T00:00:00",
"2001-03-09T00:00:00",
"2001-03-12T00:00:00",
"2001-03-13T00:00:00",
"2001-03-14T00:00:00",
"2001-03-15T00:00:00",
"2001-03-16T00:00:00",
"2001-03-19T00:00:00",
"2001-03-20T00:00:00",
"2001-03-21T00:00:00",
"2001-03-22T00:00:00",
"2001-03-23T00:00:00",
"2001-03-26T00:00:00",
"2001-03-27T00:00:00",
"2001-03-28T00:00:00",
"2001-03-29T00:00:00",
"2001-03-30T00:00:00",
"2001-04-02T00:00:00",
"2001-04-03T00:00:00",
"2001-04-04T00:00:00",
"2001-04-05T00:00:00",
"2001-04-06T00:00:00",
"2001-04-09T00:00:00",
"2001-04-10T00:00:00",
"2001-04-11T00:00:00",
"2001-04-12T00:00:00",
"2001-04-16T00:00:00",
"2001-04-17T00:00:00",
"2001-04-18T00:00:00",
"2001-04-19T00:00:00",
"2001-04-20T00:00:00",
"2001-04-23T00:00:00",
"2001-04-24T00:00:00",
"2001-04-25T00:00:00",
"2001-04-26T00:00:00",
"2001-04-27T00:00:00",
"2001-04-30T00:00:00",
"2001-05-01T00:00:00",
"2001-05-02T00:00:00",
"2001-05-03T00:00:00",
"2001-05-04T00:00:00",
"2001-05-07T00:00:00",
"2001-05-08T00:00:00",
"2001-05-09T00:00:00",
"2001-05-10T00:00:00",
"2001-05-11T00:00:00",
"2001-05-14T00:00:00",
"2001-05-15T00:00:00",
"2001-05-16T00:00:00",
"2001-05-17T00:00:00",
"2001-05-18T00:00:00",
"2001-05-21T00:00:00",
"2001-05-22T00:00:00",
"2001-05-23T00:00:00",
"2001-05-24T00:00:00",
"2001-05-25T00:00:00",
"2001-05-29T00:00:00",
"2001-05-30T00:00:00",
"2001-05-31T00:00:00",
"2001-06-01T00:00:00",
"2001-06-04T00:00:00",
"2001-06-05T00:00:00",
"2001-06-06T00:00:00",
"2001-06-07T00:00:00",
"2001-06-08T00:00:00",
"2001-06-11T00:00:00",
"2001-06-12T00:00:00",
"2001-06-13T00:00:00",
"2001-06-14T00:00:00",
"2001-06-15T00:00:00",
"2001-06-18T00:00:00",
"2001-06-19T00:00:00",
"2001-06-20T00:00:00",
"2001-06-21T00:00:00",
"2001-06-22T00:00:00",
"2001-06-25T00:00:00",
"2001-06-26T00:00:00",
"2001-06-27T00:00:00",
"2001-06-28T00:00:00",
"2001-06-29T00:00:00",
"2001-07-02T00:00:00",
"2001-07-03T00:00:00",
"2001-07-05T00:00:00",
"2001-07-06T00:00:00",
"2001-07-09T00:00:00",
"2001-07-10T00:00:00",
"2001-07-11T00:00:00",
"2001-07-12T00:00:00",
"2001-07-13T00:00:00",
"2001-07-16T00:00:00",
"2001-07-17T00:00:00",
"2001-07-18T00:00:00",
"2001-07-19T00:00:00",
"2001-07-20T00:00:00",
"2001-07-23T00:00:00",
"2001-07-24T00:00:00",
"2001-07-25T00:00:00",
"2001-07-26T00:00:00",
"2001-07-27T00:00:00",
"2001-07-30T00:00:00",
"2001-07-31T00:00:00",
"2001-08-01T00:00:00",
"2001-08-02T00:00:00",
"2001-08-03T00:00:00",
"2001-08-06T00:00:00",
"2001-08-07T00:00:00",
"2001-08-08T00:00:00",
"2001-08-09T00:00:00",
"2001-08-10T00:00:00",
"2001-08-13T00:00:00",
"2001-08-14T00:00:00",
"2001-08-15T00:00:00",
"2001-08-16T00:00:00",
"2001-08-17T00:00:00",
"2001-08-20T00:00:00",
"2001-08-21T00:00:00",
"2001-08-22T00:00:00",
"2001-08-23T00:00:00",
"2001-08-24T00:00:00",
"2001-08-27T00:00:00",
"2001-08-28T00:00:00",
"2001-08-29T00:00:00",
"2001-08-30T00:00:00",
"2001-08-31T00:00:00",
"2001-09-04T00:00:00",
"2001-09-05T00:00:00",
"2001-09-06T00:00:00",
"2001-09-07T00:00:00",
"2001-09-10T00:00:00",
"2001-09-11T00:00:00",
"2001-09-14T00:00:00",
"2001-09-17T00:00:00",
"2001-09-18T00:00:00",
"2001-09-19T00:00:00",
"2001-09-20T00:00:00",
"2001-09-21T00:00:00",
"2001-09-24T00:00:00",
"2001-09-25T00:00:00",
"2001-09-26T00:00:00",
"2001-09-27T00:00:00",
"2001-09-28T00:00:00",
"2001-10-01T00:00:00",
"2001-10-02T00:00:00",
"2001-10-03T00:00:00",
"2001-10-04T00:00:00",
"2001-10-05T00:00:00",
"2001-10-08T00:00:00",
"2001-10-09T00:00:00",
"2001-10-10T00:00:00",
"2001-10-11T00:00:00",
"2001-10-12T00:00:00",
"2001-10-15T00:00:00",
"2001-10-16T00:00:00",
"2001-10-17T00:00:00",
"2001-10-18T00:00:00",
"2001-10-19T00:00:00",
"2001-10-22T00:00:00",
"2001-10-23T00:00:00",
"2001-10-24T00:00:00",
"2001-10-25T00:00:00",
"2001-10-26T00:00:00",
"2001-10-29T00:00:00",
"2001-10-30T00:00:00",
"2001-10-31T00:00:00",
"2001-11-01T00:00:00",
"2001-11-02T00:00:00",
"2001-11-05T00:00:00",
"2001-11-06T00:00:00",
"2001-11-07T00:00:00",
"2001-11-08T00:00:00",
"2001-11-09T00:00:00",
"2001-11-12T00:00:00",
"2001-11-13T00:00:00",
"2001-11-14T00:00:00",
"2001-11-15T00:00:00",
"2001-11-16T00:00:00",
"2001-11-19T00:00:00",
"2001-11-20T00:00:00",
"2001-11-21T00:00:00",
"2001-11-26T00:00:00",
"2001-11-27T00:00:00",
"2001-11-28T00:00:00",
"2001-11-29T00:00:00",
"2001-11-30T00:00:00",
"2001-12-03T00:00:00",
"2001-12-04T00:00:00",
"2001-12-05T00:00:00",
"2001-12-06T00:00:00",
"2001-12-07T00:00:00",
"2001-12-10T00:00:00",
"2001-12-11T00:00:00",
"2001-12-12T00:00:00",
"2001-12-13T00:00:00",
"2001-12-14T00:00:00",
"2001-12-17T00:00:00",
"2001-12-18T00:00:00",
"2001-12-19T00:00:00",
"2001-12-20T00:00:00",
"2001-12-21T00:00:00",
"2001-12-26T00:00:00",
"2001-12-27T00:00:00",
"2001-12-28T00:00:00",
"2001-12-31T00:00:00",
"2002-01-02T00:00:00",
"2002-01-03T00:00:00",
"2002-01-04T00:00:00",
"2002-01-07T00:00:00",
"2002-01-08T00:00:00",
"2002-01-09T00:00:00",
"2002-01-10T00:00:00",
"2002-01-11T00:00:00",
"2002-01-14T00:00:00",
"2002-01-15T00:00:00",
"2002-01-16T00:00:00",
"2002-01-17T00:00:00",
"2002-01-18T00:00:00",
"2002-01-22T00:00:00",
"2002-01-23T00:00:00",
"2002-01-24T00:00:00",
"2002-01-25T00:00:00",
"2002-01-28T00:00:00",
"2002-01-29T00:00:00",
"2002-01-30T00:00:00",
"2002-01-31T00:00:00",
"2002-02-01T00:00:00",
"2002-02-04T00:00:00",
"2002-02-05T00:00:00",
"2002-02-06T00:00:00",
"2002-02-07T00:00:00",
"2002-02-08T00:00:00",
"2002-02-11T00:00:00",
"2002-02-12T00:00:00",
"2002-02-13T00:00:00",
"2002-02-14T00:00:00",
"2002-02-15T00:00:00",
"2002-02-19T00:00:00",
"2002-02-20T00:00:00",
"2002-02-21T00:00:00",
"2002-02-22T00:00:00",
"2002-02-25T00:00:00",
"2002-02-26T00:00:00",
"2002-02-27T00:00:00",
"2002-02-28T00:00:00",
"2002-03-01T00:00:00",
"2002-03-04T00:00:00",
"2002-03-05T00:00:00",
"2002-03-06T00:00:00",
"2002-03-07T00:00:00",
"2002-03-08T00:00:00",
"2002-03-11T00:00:00",
"2002-03-12T00:00:00",
"2002-03-13T00:00:00",
"2002-03-14T00:00:00",
"2002-03-15T00:00:00",
"2002-03-18T00:00:00",
"2002-03-19T00:00:00",
"2002-03-20T00:00:00",
"2002-03-21T00:00:00",
"2002-03-22T00:00:00",
"2002-03-25T00:00:00",
"2002-03-26T00:00:00",
"2002-03-27T00:00:00",
"2002-03-28T00:00:00",
"2002-04-01T00:00:00",
"2002-04-02T00:00:00",
"2002-04-03T00:00:00",
"2002-04-04T00:00:00",
"2002-04-05T00:00:00",
"2002-04-08T00:00:00",
"2002-04-09T00:00:00",
"2002-04-10T00:00:00",
"2002-04-11T00:00:00",
"2002-04-12T00:00:00",
"2002-04-15T00:00:00",
"2002-04-16T00:00:00",
"2002-04-17T00:00:00",
"2002-04-18T00:00:00",
"2002-04-19T00:00:00",
"2002-04-22T00:00:00",
"2002-04-23T00:00:00",
"2002-04-24T00:00:00",
"2002-04-25T00:00:00",
"2002-04-26T00:00:00",
"2002-04-29T00:00:00",
"2002-04-30T00:00:00",
"2002-05-01T00:00:00",
"2002-05-02T00:00:00",
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"x": 0.5,
"y": 0.9
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"text": "Date"
}
},
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"source": [
"fig = OpenBBFigure()\n",
"fig.add_scatter(x=data.index, y=data[\"Copper/Gold Ratio\"], name=\"Copper/Gold Ratio (x1000) %\")\n",
"fig.add_scatter(x=data.index, y=data[\"US 10-Year Constant Maturity\"], name=\"US 10-Year Constant Maturity %\")\n",
"fig.update({'layout': \n",
" {'xaxis': {'title': 'Date'},\n",
" 'yaxis': {'title': '%'},\n",
" 'title': 'Copper/Gold Ratio vs. US 10-Year Constant Maturity',\n",
" 'title_y':0.90,\n",
" 'title_x':0.5,\n",
"}})\n",
"fig.update_layout(\n",
" legend=dict(\n",
" yanchor=\"top\",\n",
" y=1,\n",
" xanchor=\"right\",\n",
" x=1.0\n",
" )\n",
")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"There is another way to create the figure object for the chart. Let's use the `plot` function from the `forecast` module to demonstrate."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
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"source": [
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},
{
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"source": [
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},
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},
"title": {
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},
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],
"source": [
"fig.update({'layout': \n",
" {'xaxis': {'title': 'Date'},\n",
" 'yaxis': {'title': '%'},\n",
" 'title': 'Copper/Gold Ratio vs. US 10-Year Constant Maturity',\n",
" 'title_y':0.98,\n",
" 'title_x':0.5,\n",
"}})\n",
"fig.update_layout(\n",
" legend=dict(\n",
" yanchor=\"top\",\n",
" y=1,\n",
" xanchor=\"right\",\n",
" x=1.0\n",
" ) \n",
")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"What we have currently is the price relationship between one Troy ounce of gold and one pound of copper. As we described the copper-to-gold ratio as the price-per-ounce of each, some adjustments are required to be true to the definition.\n",
"\n",
"- 1 ounce = 0.911458 Troy ounces\n",
"- 1 pound = 16 ounces\n",
" \n",
"To adjust the gold price as USD/ounce, multiply each row by 0.911458. To adjust the copper price, divide each row by 16."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Gold</th>\n",
" <th>Copper</th>\n",
" <th>Copper/Gold Ratio</th>\n",
" <th>US 10-Year Constant Maturity</th>\n",
" <th>Copper/Gold Ratio per Ounce (x1000) %</th>\n",
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" <th>2023-05-24</th>\n",
" <td>1962.800049</td>\n",
" <td>3.5535</td>\n",
" <td>1.810424</td>\n",
" <td>3.73</td>\n",
" <td>0.124143</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-05-25</th>\n",
" <td>1943.099976</td>\n",
" <td>3.5805</td>\n",
" <td>1.842674</td>\n",
" <td>NaN</td>\n",
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"text/plain": [
" Gold Copper Copper/Gold Ratio \\\n",
"Date \n",
"2023-05-24 1962.800049 3.5535 1.810424 \n",
"2023-05-25 1943.099976 3.5805 1.842674 \n",
"\n",
" US 10-Year Constant Maturity \\\n",
"Date \n",
"2023-05-24 3.73 \n",
"2023-05-25 NaN \n",
"\n",
" Copper/Gold Ratio per Ounce (x1000) % \n",
"Date \n",
"2023-05-24 0.124143 \n",
"2023-05-25 0.126355 "
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Copper/Gold Ratio per Ounce (x1000) %'] = (\n",
" ((data['Copper']/16)/(data['Gold']*0.911458))*1000\n",
")\n",
"\n",
"data.tail(2)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"The conversion to a per-ounce relationship has decreased the number where it wouldn't compare well on the same y-axis. Let's import the plotting function from the `/economy` menu in the OpenBB Terminal."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"from openbb_terminal.economy.plot_view import show_plot "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[0;31mSignature:\u001b[0m\n",
"\u001b[0mshow_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mdataset_yaxis_1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mdataset_yaxis_2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mexport\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0msheet_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0mexternal_axes\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mDocstring:\u001b[0m\n",
"The ability to plot any data coming from EconDB, FRED or Yahoo Finance.\n",
"\n",
"Parameters\n",
"----------\n",
"dataset_yaxis_1: pd.DataFrame\n",
" Data you wish to plot on the first y-axis.\n",
"dataset_yaxis_2 : pd.DataFrame\n",
" Data you wish to plot on the second y-axis.\n",
"external_axes : bool, optional\n",
" Whether to return the figure object or not, by default False\n",
"\n",
"Returns\n",
"-------\n",
"Plots the data.\n",
"\u001b[0;31mFile:\u001b[0m ~/GitHub/OpenBBTerminal/openbb_terminal/economy/plot_view.py\n",
"\u001b[0;31mType:\u001b[0m function"
]
}
],
"source": [
"show_plot?"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Each y-axis requires its own DataFrame, so let's make one for each next."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"y1 = pd.DataFrame(data[\"Copper/Gold Ratio per Ounce (x1000) %\"])\n",
"y2 = pd.DataFrame(data[\"US 10-Year Constant Maturity\"])\n",
"fig = show_plot(y1, y2, external_axes = True)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
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4.47,
4.46,
4.39,
4.45,
4.54,
4.6,
4.57,
4.58,
4.57,
4.58,
4.61,
4.65,
4.66,
4.65,
4.57,
4.64,
4.55,
4.61,
4.56,
4.49,
4.46,
4.5,
4.46,
4.43,
4.47,
4.41,
4.48,
4.49,
4.52,
4.52,
4.57,
4.49,
4.52,
4.47,
4.54,
4.56,
4.54,
4.45,
4.47,
4.45,
4.45,
4.47,
4.49,
4.44,
4.38,
4.34,
4.38,
4.37,
4.39,
4.37,
4.36,
4.36,
4.38,
4.38,
4.43,
4.46,
4.42,
4.36,
4.34,
4.34,
4.38,
4.37,
4.36,
4.4,
4.49,
4.53,
4.52,
4.54,
4.53,
4.57,
4.57,
4.54,
4.55,
4.57,
4.56,
4.54,
4.59,
4.58,
4.62,
4.61,
4.59,
4.54,
4.57,
4.53,
4.56,
4.58,
4.59,
4.55,
4.59,
4.64,
4.68,
4.74,
4.74,
4.73,
4.74,
4.76,
4.77,
4.71,
4.73,
4.65,
4.68,
4.66,
4.71,
4.7,
4.73,
4.67,
4.7,
4.79,
4.81,
4.86,
4.86,
4.88,
4.87,
4.84,
4.9,
4.97,
4.97,
4.93,
4.98,
5.05,
5.01,
4.99,
5.04,
5.04,
5.01,
4.99,
5.07,
5.12,
5.09,
5.07,
5.14,
5.12,
5.15,
5.16,
5.12,
5.12,
5.13,
5.13,
5.14,
5.19,
5.15,
5.1,
5.16,
5.08,
5.05,
5.04,
5.07,
5.03,
5.07,
5.06,
5.09,
5.12,
5.11,
5,
5.02,
5.01,
5.02,
5,
4.98,
4.99,
4.97,
5.05,
5.1,
5.13,
5.14,
5.15,
5.16,
5.2,
5.23,
5.25,
5.21,
5.25,
5.22,
5.15,
5.23,
5.19,
5.14,
5.13,
5.1,
5.1,
5.08,
5.07,
5.07,
5.13,
5.06,
5.03,
5.05,
5.05,
5.07,
5.04,
5.07,
5,
4.99,
4.99,
4.96,
4.96,
4.91,
4.93,
4.93,
4.92,
4.93,
4.97,
5,
4.93,
4.87,
4.87,
4.84,
4.82,
4.82,
4.82,
4.81,
4.79,
4.8,
4.79,
4.76,
4.74,
4.73,
4.78,
4.8,
4.8,
4.78,
4.8,
4.78,
4.77,
4.79,
4.8,
4.81,
4.74,
4.73,
4.65,
4.6,
4.56,
4.59,
4.6,
4.63,
4.64,
4.62,
4.62,
4.57,
4.61,
4.7,
4.75,
4.78,
4.79,
4.81,
4.79,
4.78,
4.77,
4.79,
4.79,
4.83,
4.83,
4.78,
4.73,
4.68,
4.68,
4.61,
4.57,
4.6,
4.72,
4.71,
4.66,
4.64,
4.62,
4.59,
4.61,
4.57,
4.61,
4.66,
4.61,
4.6,
4.58,
4.57,
4.55,
4.54,
4.51,
4.52,
4.46,
4.43,
4.43,
4.45,
4.48,
4.49,
4.56,
4.52,
4.49,
4.58,
4.6,
4.6,
4.6,
4.6,
4.6,
4.55,
4.63,
4.61,
4.66,
4.7,
4.71,
4.68,
4.67,
4.62,
4.65,
4.66,
4.66,
4.69,
4.74,
4.77,
4.75,
4.79,
4.75,
4.78,
4.76,
4.81,
4.81,
4.87,
4.88,
4.9,
4.88,
4.83,
4.84,
4.83,
4.81,
4.77,
4.74,
4.73,
4.79,
4.8,
4.82,
4.74,
4.7,
4.69,
4.68,
4.69,
4.73,
4.68,
4.63,
4.5,
4.56,
4.56,
4.52,
4.51,
4.53,
4.5,
4.51,
4.59,
4.56,
4.5,
4.53,
4.54,
4.55,
4.58,
4.56,
4.53,
4.6,
4.62,
4.6,
4.62,
4.62,
4.64,
4.65,
4.65,
4.67,
4.66,
4.68,
4.75,
4.73,
4.74,
4.74,
4.76,
4.74,
4.69,
4.66,
4.68,
4.68,
4.66,
4.63,
4.66,
4.69,
4.71,
4.63,
4.64,
4.65,
4.68,
4.65,
4.64,
4.63,
4.67,
4.65,
4.67,
4.69,
4.71,
4.71,
4.76,
4.81,
4.79,
4.83,
4.86,
4.86,
4.86,
4.88,
4.88,
4.9,
4.95,
4.93,
4.98,
4.97,
5.11,
5.12,
5.14,
5.26,
5.2,
5.23,
5.16,
5.15,
5.09,
5.14,
5.16,
5.14,
5.09,
5.1,
5.09,
5.12,
5.03,
5,
5.05,
5.16,
5.19,
5.16,
5.03,
5.09,
5.13,
5.11,
5.05,
5.08,
5.02,
5.04,
4.96,
4.97,
4.94,
4.92,
4.79,
4.8,
4.82,
4.78,
4.76,
4.77,
4.71,
4.72,
4.77,
4.84,
4.79,
4.81,
4.78,
4.73,
4.69,
4.6,
4.68,
4.64,
4.6,
4.63,
4.62,
4.63,
4.6,
4.53,
4.57,
4.51,
4.54,
4.56,
4.48,
4.51,
4.38,
4.34,
4.37,
4.41,
4.49,
4.47,
4.48,
4.5,
4.53,
4.69,
4.64,
4.63,
4.63,
4.63,
4.58,
4.59,
4.56,
4.54,
4.55,
4.54,
4.65,
4.67,
4.65,
4.66,
4.7,
4.69,
4.66,
4.57,
4.52,
4.41,
4.42,
4.41,
4.36,
4.37,
4.41,
4.39,
4.4,
4.48,
4.36,
4.31,
4.35,
4.38,
4.34,
4.28,
4.23,
4.26,
4.28,
4.17,
4.15,
4.07,
4.06,
4,
4.01,
3.83,
3.95,
4.03,
3.94,
3.97,
3.89,
3.89,
3.92,
4.02,
4.12,
4.15,
3.98,
4.05,
4.18,
4.24,
4.2,
4.14,
4.06,
4.04,
4.18,
4.23,
4.3,
4.21,
4.11,
4.04,
3.91,
3.91,
3.88,
3.86,
3.86,
3.82,
3.91,
3.82,
3.81,
3.72,
3.74,
3.66,
3.66,
3.52,
3.51,
3.68,
3.61,
3.61,
3.69,
3.78,
3.67,
3.62,
3.68,
3.61,
3.61,
3.74,
3.64,
3.62,
3.66,
3.7,
3.85,
3.76,
3.89,
3.93,
3.77,
3.79,
3.91,
3.88,
3.85,
3.71,
3.53,
3.54,
3.63,
3.7,
3.62,
3.56,
3.46,
3.6,
3.49,
3.56,
3.44,
3.34,
3.48,
3.38,
3.34,
3.56,
3.51,
3.51,
3.56,
3.47,
3.45,
3.57,
3.6,
3.61,
3.5,
3.57,
3.58,
3.49,
3.55,
3.49,
3.53,
3.6,
3.72,
3.75,
3.77,
3.75,
3.74,
3.77,
3.87,
3.91,
3.86,
3.85,
3.77,
3.78,
3.89,
3.88,
3.93,
3.87,
3.79,
3.77,
3.78,
3.9,
3.92,
3.83,
3.85,
3.83,
3.78,
3.81,
3.92,
3.85,
3.93,
4.03,
4.08,
4.06,
3.98,
3.92,
3.98,
4.06,
3.94,
4.02,
4.11,
4.1,
4.23,
4.27,
4.25,
4.23,
4.16,
4.22,
4.16,
4.19,
4.1,
4.12,
4.07,
3.99,
3.99,
4.01,
3.99,
3.99,
3.95,
3.91,
3.85,
3.83,
3.96,
3.9,
3.87,
3.97,
4.07,
4.11,
4.09,
4.14,
4.16,
4.03,
4.13,
4.06,
4.09,
4.07,
3.99,
3.97,
3.98,
4.04,
4.06,
3.92,
3.94,
3.99,
3.91,
3.94,
3.89,
3.84,
3.82,
3.83,
3.79,
3.84,
3.87,
3.79,
3.79,
3.77,
3.79,
3.83,
3.74,
3.71,
3.64,
3.66,
3.66,
3.62,
3.65,
3.64,
3.74,
3.47,
3.48,
3.41,
3.54,
3.78,
3.83,
3.85,
3.8,
3.88,
3.85,
3.61,
3.85,
3.77,
3.66,
3.63,
3.48,
3.5,
3.72,
3.84,
3.89,
4.08,
4.04,
3.99,
3.98,
3.91,
3.76,
3.65,
3.63,
3.76,
3.79,
3.89,
3.93,
4,
4.01,
3.96,
3.81,
3.73,
3.75,
3.83,
3.82,
3.75,
3.84,
3.72,
3.68,
3.53,
3.38,
3.1,
3.2,
3.35,
3.11,
2.99,
2.93,
2.72,
2.68,
2.67,
2.55,
2.67,
2.77,
2.67,
2.69,
2.64,
2.6,
2.53,
2.37,
2.2,
2.08,
2.13,
2.16,
2.18,
2.2,
2.16,
2.13,
2.11,
2.25,
2.46,
2.49,
2.51,
2.52,
2.47,
2.43,
2.34,
2.33,
2.24,
2.23,
2.36,
2.4,
2.56,
2.62,
2.65,
2.7,
2.59,
2.71,
2.87,
2.87,
2.76,
2.89,
2.95,
2.95,
3.05,
3.07,
2.9,
2.78,
2.75,
2.89,
2.64,
2.74,
2.85,
2.78,
2.78,
2.8,
2.95,
2.98,
3.02,
2.91,
2.93,
3.01,
2.83,
2.83,
2.89,
2.99,
2.95,
2.89,
2.89,
2.97,
3.02,
2.51,
2.61,
2.65,
2.68,
2.68,
2.81,
2.76,
2.78,
2.73,
2.71,
2.68,
2.77,
2.91,
2.95,
2.93,
2.86,
2.96,
2.88,
2.8,
2.82,
2.86,
2.98,
2.88,
2.94,
2.98,
2.96,
3.03,
2.95,
3.05,
3.12,
3.16,
3.21,
3.19,
3.2,
3.18,
3.29,
3.29,
3.17,
3.17,
3.11,
3.1,
3.14,
3.22,
3.25,
3.19,
3.35,
3.45,
3.5,
3.71,
3.67,
3.47,
3.71,
3.65,
3.56,
3.72,
3.84,
3.91,
3.86,
3.98,
3.88,
3.81,
3.76,
3.67,
3.68,
3.86,
3.79,
3.72,
3.65,
3.72,
3.55,
3.52,
3.51,
3.53,
3.55,
3.51,
3.52,
3.47,
3.33,
3.44,
3.32,
3.38,
3.5,
3.63,
3.59,
3.67,
3.61,
3.5,
3.58,
3.72,
3.7,
3.75,
3.72,
3.69,
3.67,
3.52,
3.66,
3.7,
3.8,
3.79,
3.89,
3.8,
3.71,
3.72,
3.59,
3.55,
3.48,
3.51,
3.45,
3.42,
3.56,
3.48,
3.45,
3.44,
3.47,
3.46,
3.4,
3.38,
3.29,
3.33,
3.45,
3.47,
3.48,
3.36,
3.34,
3.42,
3.47,
3.48,
3.42,
3.49,
3.49,
3.46,
3.44,
3.4,
3.34,
3.31,
3.31,
3.31,
3.21,
3.24,
3.24,
3.27,
3.21,
3.27,
3.4,
3.34,
3.45,
3.49,
3.43,
3.41,
3.35,
3.42,
3.44,
3.51,
3.59,
3.49,
3.44,
3.53,
3.41,
3.45,
3.5,
3.57,
3.57,
3.54,
3.52,
3.5,
3.45,
3.43,
3.33,
3.33,
3.36,
3.35,
3.36,
3.37,
3.32,
3.28,
3.21,
3.21,
3.28,
3.32,
3.39,
3.48,
3.44,
3.4,
3.45,
3.49,
3.55,
3.56,
3.6,
3.61,
3.5,
3.55,
3.69,
3.76,
3.77,
3.82,
3.85,
3.82,
3.8,
3.85,
3.85,
3.77,
3.85,
3.85,
3.83,
3.85,
3.74,
3.8,
3.76,
3.7,
3.73,
3.68,
3.62,
3.62,
3.66,
3.65,
3.66,
3.68,
3.63,
3.68,
3.67,
3.73,
3.62,
3.59,
3.62,
3.67,
3.72,
3.73,
3.69,
3.66,
3.74,
3.79,
3.78,
3.8,
3.69,
3.7,
3.64,
3.61,
3.61,
3.62,
3.63,
3.61,
3.69,
3.72,
3.71,
3.73,
3.73,
3.71,
3.71,
3.66,
3.65,
3.68,
3.7,
3.67,
3.69,
3.84,
3.91,
3.86,
3.88,
3.88,
3.84,
3.89,
4.01,
3.98,
3.89,
3.91,
3.9,
3.87,
3.84,
3.88,
3.86,
3.79,
3.83,
3.82,
3.77,
3.8,
3.84,
3.83,
3.71,
3.8,
3.76,
3.69,
3.72,
3.63,
3.58,
3.41,
3.45,
3.57,
3.56,
3.56,
3.55,
3.44,
3.47,
3.38,
3.36,
3.25,
3.2,
3.23,
3.18,
3.21,
3.34,
3.31,
3.29,
3.35,
3.39,
3.2,
3.17,
3.18,
3.2,
3.33,
3.24,
3.28,
3.32,
3.27,
3.21,
3.24,
3.26,
3.18,
3.13,
3.14,
3.12,
3.05,
2.97,
2.97,
2.96,
3,
2.95,
3,
3.04,
3.07,
3.08,
3.15,
3.07,
3,
2.96,
2.99,
2.98,
2.9,
2.96,
3.02,
3.03,
3.08,
3.03,
3.03,
2.94,
2.99,
2.94,
2.98,
2.94,
2.86,
2.86,
2.79,
2.72,
2.74,
2.68,
2.58,
2.64,
2.64,
2.58,
2.62,
2.6,
2.5,
2.54,
2.5,
2.66,
2.54,
2.47,
2.58,
2.63,
2.72,
2.61,
2.66,
2.77,
2.81,
2.74,
2.68,
2.74,
2.77,
2.75,
2.72,
2.61,
2.56,
2.56,
2.62,
2.54,
2.48,
2.52,
2.53,
2.54,
2.5,
2.5,
2.41,
2.41,
2.41,
2.44,
2.46,
2.52,
2.59,
2.52,
2.5,
2.51,
2.57,
2.59,
2.59,
2.67,
2.75,
2.69,
2.63,
2.66,
2.63,
2.67,
2.53,
2.58,
2.6,
2.72,
2.65,
2.76,
2.92,
2.85,
2.89,
2.9,
2.88,
2.8,
2.77,
2.93,
2.87,
2.84,
2.81,
2.97,
3.01,
3.03,
2.95,
3.15,
3.26,
3.23,
3.32,
3.29,
3.49,
3.53,
3.47,
3.33,
3.36,
3.35,
3.36,
3.41,
3.36,
3.5,
3.35,
3.38,
3.3,
3.36,
3.36,
3.5,
3.44,
3.34,
3.32,
3.37,
3.4,
3.34,
3.35,
3.39,
3.37,
3.47,
3.44,
3.43,
3.35,
3.45,
3.42,
3.36,
3.42,
3.48,
3.52,
3.58,
3.68,
3.68,
3.75,
3.65,
3.7,
3.64,
3.62,
3.61,
3.62,
3.58,
3.59,
3.46,
3.49,
3.46,
3.42,
3.42,
3.41,
3.46,
3.58,
3.49,
3.51,
3.56,
3.48,
3.37,
3.4,
3.36,
3.33,
3.22,
3.25,
3.28,
3.34,
3.34,
3.36,
3.42,
3.46,
3.47,
3.5,
3.47,
3.47,
3.46,
3.45,
3.5,
3.56,
3.58,
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1.69,
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1.77,
1.79,
1.75,
1.8,
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1.75,
1.76,
1.76,
1.74,
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1.77,
1.79,
1.85,
1.86,
1.84,
1.83,
1.81,
1.82,
1.79,
1.83,
1.88,
2.07,
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2.23,
2.23,
2.22,
2.29,
2.34,
2.33,
2.31,
2.36,
2.36,
2.32,
2.3,
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2.45,
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2.39,
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2.34,
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2.49,
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2.54,
2.6,
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2.57,
2.55,
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2.55,
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2.37,
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2.28,
2.24,
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2.18,
2.21,
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2.28,
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2.3,
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2.33,
2.22,
2.23,
2.23,
2.25,
2.29,
2.26,
2.25,
2.25,
2.21,
2.21,
2.21,
2.15,
2.18,
2.14,
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2.19,
2.21,
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2.15,
2.16,
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2.19,
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2.3,
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2.24,
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2.29,
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2.2,
2.19,
2.22,
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2.23,
2.19,
2.19,
2.18,
2.22,
2.17,
2.19,
2.17,
2.16,
2.13,
2.15,
2.12,
2.16,
2.07,
2.1,
2.05,
2.06,
2.14,
2.17,
2.2,
2.2,
2.2,
2.23,
2.24,
2.28,
2.27,
2.26,
2.22,
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2.31,
2.31,
2.33,
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2.8,
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2.83,
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2.82,
2.87,
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3,
3.03,
3,
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2.97,
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2.95,
2.95,
2.97,
3,
2.97,
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3.08,
3.09,
3.11,
3.06,
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2.98,
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3,
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2.99,
2.99,
3.05,
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3.08,
3.1,
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3.06,
3.05,
3.09,
3.05,
3.15,
3.19,
3.23,
3.21,
3.22,
3.14,
3.15,
3.16,
3.16,
3.19,
3.17,
3.2,
3.2,
3.17,
3.1,
3.14,
3.08,
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3.15,
3.14,
3.22,
3.2,
3.22,
3.22,
3.24,
3.19,
3.14,
3.12,
3.11,
3.08,
3.06,
3.06,
3.06,
3.05,
3.07,
3.06,
3.06,
3.03,
3.01,
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2.69,
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2.7,
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2.68,
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2.69,
2.65,
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2.64,
2.69,
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2.72,
2.72,
2.69,
2.64,
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2.61,
2.61,
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2.59,
2.6,
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2.54,
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2.44,
2.43,
2.41,
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2.41,
2.49,
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2.52,
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2.56,
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2.22,
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2.12,
2.09,
2.15,
2.15,
2.13,
2.1,
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2.09,
2.06,
2.03,
2.01,
2.07,
2.02,
2,
2.05,
2.01,
2,
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1.98,
1.96,
2.04,
2.05,
2.07,
2.07,
2.13,
2.12,
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2.13,
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2.04,
2.05,
2.05,
2.08,
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2.08,
2.08,
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2.06,
2.02,
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1.6,
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0.7,
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0.9,
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0.88,
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0.9,
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0.95,
0.93,
0.96,
0.94,
0.94,
0.94,
0.93,
0.93,
0.93,
0.96,
1.04,
1.08,
1.13,
1.15,
1.15,
1.1,
1.15,
1.11,
1.1,
1.1,
1.12,
1.1,
1.05,
1.05,
1.04,
1.07,
1.11,
1.09,
1.12,
1.15,
1.15,
1.19,
1.19,
1.18,
1.15,
1.16,
1.2,
1.3,
1.29,
1.29,
1.34,
1.37,
1.37,
1.38,
1.54,
1.44,
1.45,
1.42,
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1.56,
1.59,
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1.64,
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1.63,
1.67,
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1.73,
1.74,
1.69,
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1.67,
1.68,
1.64,
1.67,
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1.64,
1.56,
1.59,
1.61,
1.58,
1.57,
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1.63,
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1.61,
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1.58,
1.6,
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1.64,
1.69,
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1.5,
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1.49,
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1.48,
1.44,
1.37,
1.33,
1.3,
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1.38,
1.42,
1.37,
1.31,
1.31,
1.19,
1.23,
1.3,
1.27,
1.3,
1.29,
1.25,
1.26,
1.28,
1.24,
1.2,
1.19,
1.19,
1.23,
1.31,
1.33,
1.36,
1.35,
1.36,
1.29,
1.26,
1.26,
1.27,
1.24,
1.26,
1.25,
1.29,
1.35,
1.34,
1.31,
1.29,
1.3,
1.31,
1.29,
1.33,
1.38,
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1.3,
1.35,
1.33,
1.28,
1.31,
1.34,
1.37,
1.31,
1.33,
1.32,
1.41,
1.47,
1.48,
1.54,
1.55,
1.52,
1.48,
1.49,
1.54,
1.53,
1.58,
1.61,
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1.56,
1.52,
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1.59,
1.65,
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1.68,
1.66,
1.64,
1.63,
1.54,
1.57,
1.55,
1.58,
1.56,
1.6,
1.53,
1.45,
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1.46,
1.56,
1.58,
1.63,
1.63,
1.6,
1.59,
1.54,
1.63,
1.67,
1.64,
1.48,
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1.43,
1.43,
1.44,
1.35,
1.43,
1.48,
1.52,
1.49,
1.48,
1.42,
1.44,
1.47,
1.44,
1.41,
1.43,
1.48,
1.46,
1.5,
1.48,
1.49,
1.55,
1.52,
1.52,
1.63,
1.66,
1.71,
1.73,
1.76,
1.78,
1.75,
1.74,
1.7,
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1.87,
1.83,
1.83,
1.75,
1.75,
1.78,
1.85,
1.81,
1.78,
1.79,
1.81,
1.78,
1.82,
1.93,
1.92,
1.96,
1.94,
2.03,
1.92,
1.98,
2.05,
2.03,
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1.92,
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1.99,
1.96,
1.97,
1.83,
1.72,
1.86,
1.86,
1.74,
1.78,
1.86,
1.94,
1.98,
2,
2.14,
2.15,
2.19,
2.2,
2.14,
2.32,
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"There you have it, folks! The OpenBB SDK provides endless possibilities for creating unique indicators and analysis with the wide variety of data available at your fingertips. We love seeing the creations of users, so be sure to tag us on social media and show off your work."
]
}
],
"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": "3.10.8"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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