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>

5 files changed, 207752 insertions, 0 deletions

diff --git a/examples/README.md b/examples/README.md
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+++ b/examples/README.md
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+# Jupyter Notebook Examples Using the OpenBB SDK
+
+This folder is a collection of example notebooks that demonstrate some of the ways to get started using the OpenBB SDK.  To run them, ensure that the active kernel selected is the same Python virtual environment where OpenBB was installed.
+
+## Table of Contents
+
+### loadHistoricalPriceData
+
+This notebook walks through collecting historical price data using a variety of methods and sources.
+
+- Loading data with different intervals, and changing sources.
+- A brief explanation of ticker symbology.
+- Using other functions and modules to load data with besides `openbb.stocks.load()`.
+- Gang-loading a list of tickers' price data in a single call.
+- Drawing candle and line charts.
+
+### copperToGoldRatio
+
+This notebook explains how to calculate and plot the Copper-to-Gold ratio.
+
+- Loading historical front-month futures prices.
+- Getting the historical series from FRED for the 10-year constant maturity US treasury bill.
+- Performing basic DataFrame operations.
+- Creating OpenBB Figure objects, and plotting on two y-axis.
+
+### realizedVolatilityModels
+
+This notebook demonstrates the six, realized volatility models with in the Technical Analysis module.
+
+- Explore differences between:
+  - Standard Deviation
+  - Parkinson
+  - Hodges-Tompkins
+  - Garman-Klass
+  - Rogers-Satchell
+  - Yang-Zhang
+- Creating and plotting realized volatility cones.
+- Overlaying multiple time series.
+- Use the calculated outputs as inputs to a forecast model.
+
+### usdLiquidityIndex
+
+This notebook demonstrates how to query the Federal Reserve Economic Database and recreate the USD Liquidity Index.
+
+- Search FRED for series IDs.
+- Load multiple series as a single call.
+- Unpacking the data response from the FRED query.
+- Perform arithmetic operations on a DataFrame.
+- Normalization methods for a series or DataFrame.
+- Simple processes for creating charts.
diff --git a/examples/copperToGoldRatio.ipynb b/examples/copperToGoldRatio.ipynb
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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",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Gold</th>\n",
+       "      <th>Copper</th>\n",
+       "      <th>Copper/Gold Ratio</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2023-05-24</th>\n",
+       "      <td>1962.800049</td>\n",
+       "      <td>3.5535</td>\n",
+       "      <td>0.001810</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-05-25</th>\n",
+       "      <td>1943.099976</td>\n",
+       "      <td>3.5805</td>\n",
+       "      <td>0.001843</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "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"
+    }
+   ],
+   "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": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Gold</th>\n",
+       "      <th>Copper</th>\n",
+       "      <th>Copper/Gold Ratio</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2023-05-24</th>\n",
+       "      <td>1962.800049</td>\n",
+       "      <td>3.5535</td>\n",
+       "      <td>1.810424</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",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "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"
+    }
+   ],
+   "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": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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",
+       "      <th></th>\n",
+       "      <th></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",
+       "      <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",
+       "</table>\n",
+       "</div>"
+      ],
+      "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": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "name": "Copper/Gold Ratio (x1000) %",
+         "type": "scatter",
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