yield_curve_animation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ccf3ce53-1983-4c5d-aaff-0f754364ecc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#interactive plot (like hvplot)\n",
    "%matplotlib widget \n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt   #makes matplotlib work like MATLAB\n",
    "import matplotlib.animation as animation   #animates by calling a function over and over\n",
    "\n",
    "from openbb_terminal.sdk import openbb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4a6c80cc-eba1-4896-9532-76342b0f311e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# imports for the API key retrevial\n",
    "import requests\n",
    "from dotenv import load_dotenv\n",
    "import os \n",
    "\n",
    "# Load .env enviroment variables\n",
    "from dotenv import load_dotenv\n",
    "load_dotenv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "096466cb-5e24-491c-9bd5-64cc5992df5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the os.getenv function to retrieve the environment variable named FRED_API_KEY\n",
    "# Store as a Python variable named api_key\n",
    "api_key = os.getenv(\"FRED_API_KEY\")\n",
    "type(api_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "58fedfd4-4a92-48c2-bcc7-e66dc3e77f47",
   "metadata": {},
   "outputs": [],
   "source": [
    "# test that key works \n",
    "# openbb.keys.fred(\n",
    "#     key = api_key,\n",
    "#     persist=True\n",
    "\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9e27ee1b-61d6-4990-948d-aa8691ea00de",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nominal_10-year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIME_PERIOD</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1985-01-01</th>\n",
       "      <td>11.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-02-01</th>\n",
       "      <td>11.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-03-01</th>\n",
       "      <td>11.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-04-01</th>\n",
       "      <td>11.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-05-01</th>\n",
       "      <td>10.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "             Nominal_10-year\n",
       "TIME_PERIOD                 \n",
       "1985-01-01             11.38\n",
       "1985-02-01             11.51\n",
       "1985-03-01             11.86\n",
       "1985-04-01             11.43\n",
       "1985-05-01             10.85"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pull bond data --> note the default returns just the 10yr\n",
    "data_look = openbb.economy.treasury(\n",
    "    instruments=[\"nominal\"],\n",
    "    start_date=\"1985-01-01\"\n",
    ")\n",
    "data_look.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dad51ae6-5fed-47f3-ac83-3cf34c732088",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Nominal_3-month</th>\n",
       "      <th>Nominal_6-month</th>\n",
       "      <th>Nominal_1-year</th>\n",
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       "      <th>TIME_PERIOD</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1985-01-01</th>\n",
       "      <td>8.02</td>\n",
       "      <td>8.45</td>\n",
       "      <td>9.02</td>\n",
       "      <td>9.93</td>\n",
       "      <td>10.43</td>\n",
       "      <td>10.93</td>\n",
       "      <td>11.27</td>\n",
       "      <td>11.38</td>\n",
       "      <td>11.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-02-01</th>\n",
       "      <td>8.56</td>\n",
       "      <td>8.87</td>\n",
       "      <td>9.29</td>\n",
       "      <td>10.17</td>\n",
       "      <td>10.55</td>\n",
       "      <td>11.13</td>\n",
       "      <td>11.44</td>\n",
       "      <td>11.51</td>\n",
       "      <td>11.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-03-01</th>\n",
       "      <td>8.83</td>\n",
       "      <td>9.45</td>\n",
       "      <td>9.86</td>\n",
       "      <td>10.71</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.52</td>\n",
       "      <td>11.82</td>\n",
       "      <td>11.86</td>\n",
       "      <td>11.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-04-01</th>\n",
       "      <td>8.22</td>\n",
       "      <td>8.71</td>\n",
       "      <td>9.14</td>\n",
       "      <td>10.09</td>\n",
       "      <td>10.49</td>\n",
       "      <td>11.01</td>\n",
       "      <td>11.34</td>\n",
       "      <td>11.43</td>\n",
       "      <td>11.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-05-01</th>\n",
       "      <td>7.73</td>\n",
       "      <td>8.07</td>\n",
       "      <td>8.46</td>\n",
       "      <td>9.39</td>\n",
       "      <td>9.75</td>\n",
       "      <td>10.34</td>\n",
       "      <td>10.72</td>\n",
       "      <td>10.85</td>\n",
       "      <td>11.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "             Nominal_3-month  Nominal_6-month  Nominal_1-year  Nominal_2-year  \\\n",
       "TIME_PERIOD                                                                     \n",
       "1985-01-01              8.02             8.45            9.02            9.93   \n",
       "1985-02-01              8.56             8.87            9.29           10.17   \n",
       "1985-03-01              8.83             9.45            9.86           10.71   \n",
       "1985-04-01              8.22             8.71            9.14           10.09   \n",
       "1985-05-01              7.73             8.07            8.46            9.39   \n",
       "\n",
       "             Nominal_3-year  Nominal_5-year  Nominal_7-year  Nominal_10-year  \\\n",
       "TIME_PERIOD                                                                    \n",
       "1985-01-01            10.43           10.93           11.27            11.38   \n",
       "1985-02-01            10.55           11.13           11.44            11.51   \n",
       "1985-03-01            11.05           11.52           11.82            11.86   \n",
       "1985-04-01            10.49           11.01           11.34            11.43   \n",
       "1985-05-01             9.75           10.34           10.72            10.85   \n",
       "\n",
       "             Nominal_30-year  \n",
       "TIME_PERIOD                   \n",
       "1985-01-01             11.45  \n",
       "1985-02-01             11.47  \n",
       "1985-03-01             11.81  \n",
       "1985-04-01             11.47  \n",
       "1985-05-01             11.05  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the maturities that want to pull\n",
    "maturities = ['3m', '6m', '1y', '2y', '3y', '5y', '7y', '10y', '30y']\n",
    "\n",
    "#Now pull all maturities of interst --> using maturities parm as list maturities\n",
    "data = openbb.economy.treasury(\n",
    "    instruments=[\"nominal\"],\n",
    "    maturities=maturities,\n",
    "    start_date=\"1985-01-01\"\n",
    ")\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6dda6ccd-e695-4a42-a60a-8a574a470e91",
   "metadata": {},
   "outputs": [
    {
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       "      <td>10.17</td>\n",
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       "    <tr>\n",
       "      <th>1985-03-01</th>\n",
       "      <td>8.83</td>\n",
       "      <td>9.45</td>\n",
       "      <td>9.86</td>\n",
       "      <td>10.71</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.52</td>\n",
       "      <td>11.82</td>\n",
       "      <td>11.86</td>\n",
       "      <td>11.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-04-01</th>\n",
       "      <td>8.22</td>\n",
       "      <td>8.71</td>\n",
       "      <td>9.14</td>\n",
       "      <td>10.09</td>\n",
       "      <td>10.49</td>\n",
       "      <td>11.01</td>\n",
       "      <td>11.34</td>\n",
       "      <td>11.43</td>\n",
       "      <td>11.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-05-01</th>\n",
       "      <td>7.73</td>\n",
       "      <td>8.07</td>\n",
       "      <td>8.46</td>\n",
       "      <td>9.39</td>\n",
       "      <td>9.75</td>\n",
       "      <td>10.34</td>\n",
       "      <td>10.72</td>\n",
       "      <td>10.85</td>\n",
       "      <td>11.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               3m    6m    1y     2y     3y     5y     7y    10y    30y\n",
       "TIME_PERIOD                                                            \n",
       "1985-01-01   8.02  8.45  9.02   9.93  10.43  10.93  11.27  11.38  11.45\n",
       "1985-02-01   8.56  8.87  9.29  10.17  10.55  11.13  11.44  11.51  11.47\n",
       "1985-03-01   8.83  9.45  9.86  10.71  11.05  11.52  11.82  11.86  11.81\n",
       "1985-04-01   8.22  8.71  9.14  10.09  10.49  11.01  11.34  11.43  11.47\n",
       "1985-05-01   7.73  8.07  8.46   9.39   9.75  10.34  10.72  10.85  11.05"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename columns so easier to work with  ---> use already defined maturities list\n",
    "data.columns = maturities\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5469a2de-6ba5-4d60-b499-bf215566ac1a",
   "metadata": {},
   "outputs": [
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       "      <th>1985-01-01</th>\n",
       "      <td>8.02</td>\n",
       "      <td>8.45</td>\n",
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       "      <td>False</td>\n",
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       "      <th>1985-02-01</th>\n",
       "      <td>8.56</td>\n",
       "      <td>8.87</td>\n",
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       "      <th>1985-03-01</th>\n",
       "      <td>8.83</td>\n",
       "      <td>9.45</td>\n",
       "      <td>9.86</td>\n",
       "      <td>10.71</td>\n",
       "      <td>11.05</td>\n",
       "      <td>11.52</td>\n",
       "      <td>11.82</td>\n",
       "      <td>11.86</td>\n",
       "      <td>11.81</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1985-04-01</th>\n",
       "      <td>8.22</td>\n",
       "      <td>8.71</td>\n",
       "      <td>9.14</td>\n",
       "      <td>10.09</td>\n",
       "      <td>10.49</td>\n",
       "      <td>11.01</td>\n",
       "      <td>11.34</td>\n",
       "      <td>11.43</td>\n",
       "      <td>11.47</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1985-05-01</th>\n",
       "      <td>7.73</td>\n",
       "      <td>8.07</td>\n",
       "      <td>8.46</td>\n",
       "      <td>9.39</td>\n",
       "      <td>9.75</td>\n",
       "      <td>10.34</td>\n",
       "      <td>10.72</td>\n",
       "      <td>10.85</td>\n",
       "      <td>11.05</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               3m    6m    1y     2y     3y     5y     7y    10y    30y  \\\n",
       "TIME_PERIOD                                                               \n",
       "1985-01-01   8.02  8.45  9.02   9.93  10.43  10.93  11.27  11.38  11.45   \n",
       "1985-02-01   8.56  8.87  9.29  10.17  10.55  11.13  11.44  11.51  11.47   \n",
       "1985-03-01   8.83  9.45  9.86  10.71  11.05  11.52  11.82  11.86  11.81   \n",
       "1985-04-01   8.22  8.71  9.14  10.09  10.49  11.01  11.34  11.43  11.47   \n",
       "1985-05-01   7.73  8.07  8.46   9.39   9.75  10.34  10.72  10.85  11.05   \n",
       "\n",
       "             inverted  \n",
       "TIME_PERIOD            \n",
       "1985-01-01      False  \n",
       "1985-02-01      False  \n",
       "1985-03-01      False  \n",
       "1985-04-01      False  \n",
       "1985-05-01      False  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set yld curve as inverted if longer tem rates are LESS than shorter term\n",
    "# here you can use any 2 maturities for comparison, here we'll use 3mo and 30yr\n",
    "\n",
    "data[\"inverted\"] = data[\"3m\"] > data[\"30y\"]  #--> this adds a bool col called inverted\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c77c2e3-6144-4e21-b65e-665189faf3ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# inital function def\n",
    "def init_func():\n",
    "    line.set_data([], [])\n",
    "    plt.title(\"U.S. Treasury Bond Yield Curve\")\n",
    "    return line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d8d1438-c636-4529-8f92-f8535fe1f085",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!jupyter labextension list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c43c988b-3b2f-4ce1-960f-5ad9dbd2c4ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6178b8974eec401682cb7a5a369a0206",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "line, = ax.plot([], [])\n",
    "\n",
    "# Set the range of ticks\n",
    "ax.set_xlim(0, 7)\n",
    "ax.set_ylim(0, 14)\n",
    "\n",
    "# Set the tick locations\n",
    "ax.set_xticks(range(8))\n",
    "ax.set_yticks([2, 4, 6, 8, 10, 12])\n",
    "\n",
    "# Set the axis labels\n",
    "ax.set_xticklabels([\"1m\",\"3m\",\"6m\",\"1y\",\"5y\",\"10y\",\"20y\",\"30y\"])\n",
    "ax.set_yticklabels([2, 4, 6, 8, 10, 12])\n",
    "\n",
    "# Foce the y-axis labels to the left\n",
    "ax.yaxis.set_label_position(\"left\")\n",
    "ax.yaxis.tick_left()\n",
    "\n",
    "# Add the axis lables\n",
    "plt.ylabel(\"Yield (%)\")\n",
    "plt.xlabel(\"Time to maturty\")\n",
    "\n",
    "# add in click fiunction to start & stop animation\n",
    "def run_animation():\n",
    "    anim_running = True\n",
    "\n",
    "    def onClick(event):\n",
    "        nonlocal anim_running\n",
    "        if anim_running:\n",
    "            anim.event_source.stop()\n",
    "            anim_running = False\n",
    "        else:\n",
    "            anim.event_source.start()\n",
    "            anim_running = True\n",
    "\n",
    "    def animFunc(i):\n",
    "        x = range(0, len(maturities))\n",
    "        y = data[maturities].iloc[i]\n",
    "        dt_ = data.index[i].strftime(\"%Y-%m-%d\")\n",
    "    \n",
    "        if data.inverted.iloc[i]:\n",
    "            line.set_color(\"r\")\n",
    "        else:\n",
    "            line.set_color(\"y\")\n",
    "    \n",
    "        line.set_data(x, y)\n",
    "    \n",
    "        plt.title(f\"U.S. Treasury Bond Yield Curve ({dt_})\")\n",
    "        return line,\n",
    "        # Animation update function here\n",
    "\n",
    "    fig.canvas.mpl_connect('button_press_event', onClick)\n",
    "\n",
    "    anim = animation.FuncAnimation(\n",
    "    fig, \n",
    "    animFunc,\n",
    "    init_func=init_func,\n",
    "    frames=len(data.index),\n",
    "    interval=5,\n",
    "    blit=True)\n",
    "\n",
    "run_animation()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "593f3287-3f40-4474-96b7-e6dd36289ce1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Old code from PQN#30\n",
    "\n",
    "# setup plot using matplotlib\n",
    "# this code is template and added in specific values for this ex\n",
    "\n",
    "# Initialize figure\n",
    "# fig = plt.figure()\n",
    "# ax = fig.add_subplot(1, 1, 1)\n",
    "# line, = ax.plot([], [])\n",
    "\n",
    "# # Set the range of ticks\n",
    "# ax.set_xlim(0, 7)\n",
    "# ax.set_ylim(0, 20)\n",
    "\n",
    "# # Set the tick locations\n",
    "# ax.set_xticks(range(8))\n",
    "# ax.set_yticks([2, 4, 6, 8, 10, 12, 14, 16, 18])\n",
    "\n",
    "# # Set the axis labels\n",
    "# ax.set_xticklabels([\"1m\",\"3m\",\"6m\",\"1y\",\"5y\",\"10y\",\"20y\",\"30y\"])\n",
    "# ax.set_yticklabels([2, 4, 6, 8, 10, 12, 14, 16, 18])\n",
    "\n",
    "# # Foce the y-axis labels to the left\n",
    "# ax.yaxis.set_label_position(\"left\")\n",
    "# ax.yaxis.tick_left()\n",
    "\n",
    "# # Add the axis lables\n",
    "# plt.ylabel(\"Yield (%)\")\n",
    "# plt.xlabel(\"Time to maturty\")\n",
    "#-----------------------------------------------\n",
    "# def init_func():\n",
    "#     line.set_data([], [])\n",
    "#     plt.title(\"U.S. Treasury Bond Yield Curve\")\n",
    "#     return line\n",
    "\n",
    "# def animate(i):\n",
    "#     x = range(0, len(maturities))\n",
    "#     y = data[maturities].iloc[i]\n",
    "#     dt_ = data.index[i].strftime(\"%Y-%m-%d\")\n",
    "    \n",
    "#     if data.inverted.iloc[i]:\n",
    "#         line.set_color(\"r\")\n",
    "#     else:\n",
    "#         line.set_color(\"y\")\n",
    "    \n",
    "#     line.set_data(x, y)\n",
    "    \n",
    "#     plt.title(f\"U.S. Treasury Bond Yield Curve ({dt_})\")\n",
    "#     return line,\n",
    "\n",
    "# ani = animation.FuncAnimation(\n",
    "#     fig, \n",
    "#     animate,\n",
    "#     init_func=init_func,\n",
    "#     frames=len(data.index),\n",
    "#     interval=5,\n",
    "#     blit=True\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d055fdc9-86a7-4dd1-8f2f-c7cd0004debd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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