aarongilman/inverted_yield_curve.ipynb

## inverted_yield_curve.ipynb
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   "source": [
    "# Putting Recession Indicators to the Test\n",
    "## Part 1: Exploring the shape of the yield curve as a tool for guiding asset allocation decisions and risk management?\n",
    "### Created by IFP Asset Management 8/20/2019\n",
    "*Data Sources: Tiingo (for daily prices used in backtest) and FRED, Federal Reserve Bank of St. Louis (for economic data points used in analysis)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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    },
    "id": "yHyji9AF1a1O"
   },
   "outputs": [],
   "source": [
    "from pandas_datareader import data\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.dates import YearLocator, MonthLocator\n",
    "import os\n",
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "datapoints = ['T10Y2Y', 'USREC']\n",
    "chart_data_panel = data.DataReader(datapoints, 'fred', start=datetime(1977,1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "chart_data_panel = chart_data_panel.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "extensions": {
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   },
   "outputs": [],
   "source": [
    "chart_data_panel['peak_trough'] = chart_data_panel['USREC'].diff()\n",
    "daily_peaks = list(chart_data_panel[chart_data_panel['peak_trough'] == 1].index)\n",
    "daily_troughs = list(chart_data_panel[chart_data_panel['peak_trough'] == -1].index)\n",
    "daily_dictionary = dict(zip(daily_peaks, daily_troughs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
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   },
   "source": [
    "**Let's start our analysis with a long-term chart of the indicator for context, and overlay the periods that the US experienced a recession. This is always a great starting point before diving deeper into an analysis of the usefulness of an indicator in forecasting or investing.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.dates import YearLocator, MonthLocator\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "growth_regime_data = chart_data_panel.copy()\n",
    "labels = list(set(growth_regime_data.index.year))\n",
    "fig = plt.figure(figsize=(20,15))\n",
    "ax = fig.add_subplot(1,1,1)  \n",
    "#plt.figure(figsize=(25,15))\n",
    "# Plot the VX1, not the VIX since we're going to trade the future and not the index directly\n",
    "plt.plot(growth_regime_data.index, growth_regime_data['T10Y2Y'], label='10 Year Treasury Minus 2 Year Treasury')\n",
    "plt.axhline(y=growth_regime_data['T10Y2Y'].values[-1], color='r', linestyle='-', label='current spread')\n",
    "\n",
    "for key, value in daily_dictionary.items():\n",
    "  plt.axvspan(key, value, alpha=0.5, color='grey')\n",
    "  \n",
    "\n",
    "plt.ylabel('Spread')\n",
    "plt.xlabel('Date')\n",
    "#plt.xticks(YearLocator(), labels, rotation='vertical')\n",
    "ax.xaxis.set_major_locator(YearLocator())\n",
    "plt.legend(loc=0)\n",
    "plt.title('Historical slope of the yield curve. Recessions shaded in grey.')\n",
    "plt.xticks(rotation=90)   \n",
    "# Display everything\n",
    "plt.show()\n",
    "#fig.savefig('Yield Curve Slope.svg', format='svg', dpi=1200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "**When looking at this chart it is easy to see why it is constantly touted as a great leading indicator for recessions. However, unless it is possible to translate this over to a practical investing strategy, knowing a recession is coming in the future has limited value when you are professionally investing other people's money and is less than half the battle. From our perspective, we only care about leading indicators if it helps us avoid market drawdowns and improve return and risk relative to investing in a strategic asset allocation model. The rest of this thought experiment will focus on attempting to translate the slope of the yield curve into a practical investing strategy that adds value.** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
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      }
     }
    },
    "id": "pWoBlDmi1a1h"
   },
   "source": [
    "Now that we have visually explored the yield curve slope relative to recessions and understood the potential rationale for citing it as a leading indicator of recessions, we will perform the following steps of analysis to dig in further:\n",
    "\n",
    "1. Divide the data set (Russell 3000 total return index and the 10-Year Treasury Constant Maturity Minus 2-Year Treasury Constant Maturity) into three groups: inverted yield curve (negative 10-2 spread), normal yield curve (positive 10-2 spread), and similar to today (todays 10-2 spread plus or minus 5%).\n",
    "2. Calculate 3, 6, and 12 month forward returns (future returns after one of the yield curve environments was recognized historically)\n",
    "3. Examining the characteristics of the forward returns grouped by each yield curve environment by looking at descriptive statistics (distribution, mean, etc.)\n",
    "4. Backtest a strategy based on the rules defined above based on each yield curve environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
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       "grid_default": {},
       "report_default": {
        "hidden": true
       }
      }
     }
    },
    "id": "nODzoGmf1a1j"
   },
   "outputs": [],
   "source": [
    "##lots of great historical index data here: https://fred.stlouisfed.org/categories/32255\n",
    "datapoints = ['RU3000TR', 'T10Y2Y']\n",
    "econ_data_panel = data.DataReader(datapoints, 'fred', start=datetime(1980,1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": true
       }
      }
     }
    },
    "id": "y9QcWma5Bmsa"
   },
   "outputs": [],
   "source": [
    "econ_data_panel['RU3000TR'].fillna(method='ffill', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": true
       }
      }
     }
    },
    "id": "d3RxS8B9CcP3"
   },
   "outputs": [],
   "source": [
    "daily_data = econ_data_panel[['RU3000TR', 'T10Y2Y']].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": true
       }
      }
     }
    },
    "id": "duBhrchQOirn"
   },
   "outputs": [],
   "source": [
    "datapoints = ['T10Y2Y','RU3000TR']\n",
    "growth_regime_data = daily_data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": true
       }
      }
     }
    },
    "id": "Doavy1lWOpfO"
   },
   "outputs": [],
   "source": [
    "from math import sqrt\n",
    "growth_regime_data['return'] = growth_regime_data['RU3000TR'].pct_change()\n",
    "growth_regime_data['one_year_rolling_return'] = growth_regime_data['RU3000TR'].pct_change(252)\n",
    "growth_regime_data['six_month_rolling_return'] = growth_regime_data['RU3000TR'].pct_change(126)\n",
    "growth_regime_data['three_month_rolling_return'] = growth_regime_data['RU3000TR'].pct_change(63)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": true
       }
      }
     }
    },
    "id": "Joq0U1nYPPk7"
   },
   "outputs": [],
   "source": [
    "growth_regime_data['forward_one_year_rolling_return'] = growth_regime_data['one_year_rolling_return'].shift(-252)\n",
    "growth_regime_data['forward_six_month_rolling_return'] = growth_regime_data['six_month_rolling_return'].shift(-126)\n",
    "growth_regime_data['forward_three_month_rolling_return'] = growth_regime_data['three_month_rolling_return'].shift(-63)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": true
       }
      }
     }
    },
    "id": "i6bYR5cLPuIs"
   },
   "outputs": [],
   "source": [
    "current_spread = growth_regime_data['T10Y2Y'].iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": true
       }
      }
     }
    },
    "id": "H9ICuvm6QuCb"
   },
   "outputs": [],
   "source": [
    "from math import sqrt\n",
    "reduced_dataframe = growth_regime_data[['T10Y2Y', 'forward_one_year_rolling_return', 'forward_six_month_rolling_return', 'forward_three_month_rolling_return']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": true
       }
      }
     }
    },
    "id": "MteT2VhvP9aL"
   },
   "outputs": [],
   "source": [
    "comparable_times = reduced_dataframe[(reduced_dataframe['T10Y2Y'] >= (0.95 * current_spread)) & (reduced_dataframe['T10Y2Y'] <= (1.05 * current_spread))]\n",
    "better_times = reduced_dataframe[reduced_dataframe['T10Y2Y'] > 0.0]\n",
    "worse_times = reduced_dataframe[reduced_dataframe['T10Y2Y'] <= 0.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": true
       }
      }
     }
    },
    "id": "baFiZWKPQjak"
   },
   "outputs": [],
   "source": [
    "comparable_times = comparable_times.dropna()\n",
    "better_times = better_times.dropna()\n",
    "worse_times = worse_times.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "### Comparable Yield Curve Environments to where we are today and forward returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "colab_type": "code",
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
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      }
     }
    },
    "id": "PYsG00IIRQYB",
    "outputId": "9b0665a8-b8d7-4246-9e7a-b77ff77a87ac"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T10Y2Y</th>\n",
       "      <th>forward_one_year_rolling_return</th>\n",
       "      <th>forward_six_month_rolling_return</th>\n",
       "      <th>forward_three_month_rolling_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.600000e+01</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>0.132669</td>\n",
       "      <td>0.045094</td>\n",
       "      <td>0.006033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.759331e-18</td>\n",
       "      <td>0.167126</td>\n",
       "      <td>0.104376</td>\n",
       "      <td>0.077442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>-0.105397</td>\n",
       "      <td>-0.102594</td>\n",
       "      <td>-0.196288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>-0.037899</td>\n",
       "      <td>-0.027490</td>\n",
       "      <td>-0.033049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>0.146784</td>\n",
       "      <td>0.030413</td>\n",
       "      <td>0.030293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>0.177304</td>\n",
       "      <td>0.079258</td>\n",
       "      <td>0.053273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>0.643555</td>\n",
       "      <td>0.393607</td>\n",
       "      <td>0.210678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             T10Y2Y  forward_one_year_rolling_return  \\\n",
       "count  3.600000e+01                        36.000000   \n",
       "mean   1.000000e-02                         0.132669   \n",
       "std    1.759331e-18                         0.167126   \n",
       "min    1.000000e-02                        -0.105397   \n",
       "25%    1.000000e-02                        -0.037899   \n",
       "50%    1.000000e-02                         0.146784   \n",
       "75%    1.000000e-02                         0.177304   \n",
       "max    1.000000e-02                         0.643555   \n",
       "\n",
       "       forward_six_month_rolling_return  forward_three_month_rolling_return  \n",
       "count                         36.000000                           36.000000  \n",
       "mean                           0.045094                            0.006033  \n",
       "std                            0.104376                            0.077442  \n",
       "min                           -0.102594                           -0.196288  \n",
       "25%                           -0.027490                           -0.033049  \n",
       "50%                            0.030413                            0.030293  \n",
       "75%                            0.079258                            0.053273  \n",
       "max                            0.393607                            0.210678  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparable_times.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
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      }
     }
    }
   },
   "source": [
    "With only 40 time periods falling into the \"comparable times\" environment, the sample size is much too small to place any confidence in the validity of any conclusions drawn from it. It is a good step in the process, but our analysis of this segment of the data ends here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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      "version": 1,
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      }
     }
    }
   },
   "source": [
    "### Normal Yield Curve Environments and forward returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "colab_type": "code",
    "extensions": {
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     }
    },
    "id": "UyINRzdlRfFZ",
    "outputId": "11fa9a30-f251-44af-86da-75d30a123dd4"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T10Y2Y</th>\n",
       "      <th>forward_one_year_rolling_return</th>\n",
       "      <th>forward_six_month_rolling_return</th>\n",
       "      <th>forward_three_month_rolling_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8473.000000</td>\n",
       "      <td>8473.000000</td>\n",
       "      <td>8473.000000</td>\n",
       "      <td>8473.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.220469</td>\n",
       "      <td>0.136705</td>\n",
       "      <td>0.065761</td>\n",
       "      <td>0.033287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.765884</td>\n",
       "      <td>0.162750</td>\n",
       "      <td>0.115435</td>\n",
       "      <td>0.080043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.010000</td>\n",
       "      <td>-0.482778</td>\n",
       "      <td>-0.469567</td>\n",
       "      <td>-0.422588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.570000</td>\n",
       "      <td>0.054471</td>\n",
       "      <td>0.012109</td>\n",
       "      <td>-0.004261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.120000</td>\n",
       "      <td>0.152788</td>\n",
       "      <td>0.072596</td>\n",
       "      <td>0.037517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.840000</td>\n",
       "      <td>0.236582</td>\n",
       "      <td>0.131377</td>\n",
       "      <td>0.079539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.910000</td>\n",
       "      <td>0.778532</td>\n",
       "      <td>0.540925</td>\n",
       "      <td>0.414889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            T10Y2Y  forward_one_year_rolling_return  \\\n",
       "count  8473.000000                      8473.000000   \n",
       "mean      1.220469                         0.136705   \n",
       "std       0.765884                         0.162750   \n",
       "min       0.010000                        -0.482778   \n",
       "25%       0.570000                         0.054471   \n",
       "50%       1.120000                         0.152788   \n",
       "75%       1.840000                         0.236582   \n",
       "max       2.910000                         0.778532   \n",
       "\n",
       "       forward_six_month_rolling_return  forward_three_month_rolling_return  \n",
       "count                       8473.000000                         8473.000000  \n",
       "mean                           0.065761                            0.033287  \n",
       "std                            0.115435                            0.080043  \n",
       "min                           -0.469567                           -0.422588  \n",
       "25%                            0.012109                           -0.004261  \n",
       "50%                            0.072596                            0.037517  \n",
       "75%                            0.131377                            0.079539  \n",
       "max                            0.540925                            0.414889  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "better_times.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "**This sample size is large enough to draw conclusions from and we should be able to gather insights from examining further.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
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       }
      }
     }
    }
   },
   "source": [
    "### Inverted Yield Curve Environments and forward returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "colab_type": "code",
    "extensions": {
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      }
     }
    },
    "id": "a5i5RbAYRfIQ",
    "outputId": "8f2774e3-174c-4882-8474-e7a72d43b52d"
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T10Y2Y</th>\n",
       "      <th>forward_one_year_rolling_return</th>\n",
       "      <th>forward_six_month_rolling_return</th>\n",
       "      <th>forward_three_month_rolling_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1188.000000</td>\n",
       "      <td>1188.000000</td>\n",
       "      <td>1188.000000</td>\n",
       "      <td>1188.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.401869</td>\n",
       "      <td>0.066356</td>\n",
       "      <td>0.044246</td>\n",
       "      <td>0.016170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.462672</td>\n",
       "      <td>0.213448</td>\n",
       "      <td>0.120694</td>\n",
       "      <td>0.072972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.410000</td>\n",
       "      <td>-0.328112</td>\n",
       "      <td>-0.240422</td>\n",
       "      <td>-0.181112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.492500</td>\n",
       "      <td>-0.099959</td>\n",
       "      <td>-0.044587</td>\n",
       "      <td>-0.034746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.220000</td>\n",
       "      <td>0.030934</td>\n",
       "      <td>0.040781</td>\n",
       "      <td>0.020594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-0.090000</td>\n",
       "      <td>0.176245</td>\n",
       "      <td>0.108560</td>\n",
       "      <td>0.067414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.729776</td>\n",
       "      <td>0.419387</td>\n",
       "      <td>0.254906</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            T10Y2Y  forward_one_year_rolling_return  \\\n",
       "count  1188.000000                      1188.000000   \n",
       "mean     -0.401869                         0.066356   \n",
       "std       0.462672                         0.213448   \n",
       "min      -2.410000                        -0.328112   \n",
       "25%      -0.492500                        -0.099959   \n",
       "50%      -0.220000                         0.030934   \n",
       "75%      -0.090000                         0.176245   \n",
       "max       0.000000                         0.729776   \n",
       "\n",
       "       forward_six_month_rolling_return  forward_three_month_rolling_return  \n",
       "count                       1188.000000                         1188.000000  \n",
       "mean                           0.044246                            0.016170  \n",
       "std                            0.120694                            0.072972  \n",
       "min                           -0.240422                           -0.181112  \n",
       "25%                           -0.044587                           -0.034746  \n",
       "50%                            0.040781                            0.020594  \n",
       "75%                            0.108560                            0.067414  \n",
       "max                            0.419387                            0.254906  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "worse_times.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "This sample size is smaller than the normal yield curve environment but is still large enough to warrant further analysis. It lines up with what you would think, with the majority of historical time periods ocurring during a normal yield curve environment and only a small subset spent inverted. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "We will dive deeper into the statistics above, but it appears that inverted yield curve environments, on average, can be characterized by lower future returns (3, 6, and 12 months ahead) and higher volatility of those future returns. This does not mean they are always negative, and you can see that the minimum one year forward return for normal yield curve environments is actually much lower than inverted. This highlights one of the difficulties of translating leading economic indicators into actionable investing insights and we will explore this concept further below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "comparison_df = pd.DataFrame(index=['one_year_forward', 'six_month_forward', 'three_month_forward'], columns=['inverted_yield_curve', 'normal_yield_curve'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "comparison_df.loc['one_year_forward', 'inverted_yield_curve'] = worse_times.describe().loc['mean', 'forward_one_year_rolling_return'] / worse_times.describe().loc['std', 'forward_one_year_rolling_return']\n",
    "comparison_df.loc['six_month_forward', 'inverted_yield_curve'] = worse_times.describe().loc['mean', 'forward_six_month_rolling_return'] / worse_times.describe().loc['std', 'forward_six_month_rolling_return']\n",
    "comparison_df.loc['three_month_forward', 'inverted_yield_curve'] = worse_times.describe().loc['mean', 'forward_three_month_rolling_return'] / worse_times.describe().loc['std', 'forward_three_month_rolling_return']\n",
    "comparison_df.loc['one_year_forward', 'normal_yield_curve'] = better_times.describe().loc['mean', 'forward_one_year_rolling_return'] / better_times.describe().loc['std', 'forward_one_year_rolling_return']\n",
    "comparison_df.loc['six_month_forward', 'normal_yield_curve'] = better_times.describe().loc['mean', 'forward_six_month_rolling_return'] / better_times.describe().loc['std', 'forward_six_month_rolling_return']\n",
    "comparison_df.loc['three_month_forward', 'normal_yield_curve'] = better_times.describe().loc['mean', 'forward_three_month_rolling_return'] / better_times.describe().loc['std', 'forward_three_month_rolling_return']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "**What about the forward sharpe ratios of each time period?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "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>inverted_yield_curve</th>\n",
       "      <th>normal_yield_curve</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one_year_forward</th>\n",
       "      <td>0.310874</td>\n",
       "      <td>0.839966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>six_month_forward</th>\n",
       "      <td>0.366596</td>\n",
       "      <td>0.569681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three_month_forward</th>\n",
       "      <td>0.221593</td>\n",
       "      <td>0.415861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    inverted_yield_curve normal_yield_curve\n",
       "one_year_forward                0.310874           0.839966\n",
       "six_month_forward               0.366596           0.569681\n",
       "three_month_forward             0.221593           0.415861"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "No surprise here, but over all three future return periods, normal yield curve environments (on average) tend to result in a better risk/reward tradeoff than inverted yield curve environments. This does not mean that they will be negative returns, it just demonstrates that returns tend to be lower on average 3, 6, and 12 months after an inverted yield curve and more volatile but says nothing about the relative over/underperformance compared to other investible asset classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "forward_six_month_rolling_returns = [better_times['forward_six_month_rolling_return'].values,\n",
    "                                    worse_times['forward_six_month_rolling_return'].values]\n",
    "forward_three_month_rolling_returns = [better_times['forward_three_month_rolling_return'].values,\n",
    "                                    worse_times['forward_three_month_rolling_return'].values]\n",
    "forward_one_year_rolling_returns = [better_times['forward_one_year_rolling_return'].values,\n",
    "                                    worse_times['forward_one_year_rolling_return'].values]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "Finally, translating the different future returns into a series of visualizations through Box and Whisker plots to help us see the characteristics of each return distribution in chart form."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1, figsize=(20, 15))\n",
    "\n",
    "# Create an axes instance\n",
    "ax = fig.add_subplot(111)\n",
    "bp = ax.boxplot(forward_six_month_rolling_returns, patch_artist=True, showfliers=False)\n",
    "\n",
    "## change outline color, fill color and linewidth of the boxes\n",
    "for box in bp['boxes']:\n",
    "    # change outline color\n",
    "    box.set( color='#7570b3', linewidth=2)\n",
    "    # change fill color\n",
    "    box.set( facecolor = '#1b9e77' )\n",
    "\n",
    "## change color and linewidth of the whiskers\n",
    "for whisker in bp['whiskers']:\n",
    "    whisker.set(color='#7570b3', linewidth=2)\n",
    "\n",
    "## change color and linewidth of the caps\n",
    "for cap in bp['caps']:\n",
    "    cap.set(color='#7570b3', linewidth=2)\n",
    "\n",
    "## change color and linewidth of the medians\n",
    "for median in bp['medians']:\n",
    "    median.set(color='#b2df8a', linewidth=2)\n",
    "\n",
    "## change the style of fliers and their fill\n",
    "for flier in bp['fliers']:\n",
    "    flier.set(marker='o', color='#e7298a', alpha=0.5)\n",
    "ind = ['Normal Yield Curve', 'Inverted Yield Curve']\n",
    "#plt.boxplot(forward_one_year_rolling_returns)\n",
    "ax.set_xticklabels(ind)\n",
    "ax.get_xaxis().tick_bottom()\n",
    "ax.get_yaxis().tick_left()\n",
    "ax.set_title('Six Month Forward Returns for Each Time Period')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dr3/96znggAOSrPuHk9GjR6d169aZOnVqzefv0KFD069fvyxevLjmsw6ATx+HYwPwqXDRRRfV+sOlSZMmNSHtg4eoVbv00kvrBMj3338/t9xyS8rKyjJixIhaI0AaNWqUyy+/PCUlJfnVr35VM/3ee+/N8uXLc/HFF9f6QzNJ9tprr5x22mlZvHhxvYfAfZTTTz8977//fu66664kyauvvprHHnssJ5544gZHP82ePTv/7//9vxxwwAF1DtM89NBDc8wxx+Tvf/97HnnkkTr3PfDAA2sFyCQ59dRT06RJkw0+jx9l4MCBdQ4bP+2005Js+LXZkEWLFuWaa66p8/XYY4/VzLMl29+hQ4d6//iuHtG4/ms4ffr0NG/ePOedd16qqqpqbluzZk2eeOKJ7LvvvjVRMknKysrqHdXWoUOHdO/ePTNnzqz3cNWmTZvmqquuqjMKcfbs2Vm4cGEqKipqBcgk+d73vpcvf/nLdZa1MWbOnJkRI0ZkxIgRGTJkSHr27Jm77roru+66a4YPH15r3jfeeCP33HNPOnbsWCtAJsn222+fH/3oR0lS6+elSBt6/dZ3zTXX1Lq4RllZWfr27ZtVq1Zl4cKFteZt0qRJvaOyGuKCJnfeeWeS5Pzzz68JkElSUlKSK664IjvssEPGjx9f7z7xf//v/93s8zrecsst9f4MXXPNNbUOh91+++3rBMhk3UjOU045JStWrMh//dd/1UyfNGlS/vrXv+aII46oEyCTdefErG8k7eWXX14TIJN1//gyYMCAvP/++xs8/L9oF1xwQa3w3KZNm3Tr1i3/+Mc/8t3vfrcmQCbrno8+ffpkzZo1NSOjk+I+F6p/Nq+66qqceeaZ6d27d1avXp1+/fqlW7duSZLf/e53eeGFF/K9732vzsV7vvCFL+QHP/hB3n777Tz44IN1lr8xP0PV3n333dx7771p1qxZrrjiipoAmSS77bZbzj///FRVVWXcuHE106tPszB48OBaP0fbb799hg4dutHPAwDbJiMhAfhU2H///etM23333ZOk5nxWH1Tf6MgXXnghy5Yty5e+9KV6RyAlyQ477JC//OUvNd9Xny/xueeeq3eU3gsvvJBk3eiXI4444iO2pLb9998/HTt2zN13352LLrood955Z80olw155plnkmSDh+MeeuihmThxYp555pl84xvfqPN4H7TddtulVatWG3weN2YbPuijXpsNOfjggzd4nrFqW7L99e0Tybqrcn/5y1/OrFmzsnbt2jRp0iQzZszIQQcdlG7dumXHHXfMtGnTcuKJJ2bOnDl56623ah2KXe3RRx/NmDFjMm/evCxbtqzOlc2XLVuWL3zhC7WmtW7dus5opfW38+CDD65zW6NGjXLggQfmxRdfrHd7Pszjjz+exx9/vM46TJo0qU6Uevrpp7N27do0atSo3n2/evvW/3kp0oZev2rNmzevN87Wtz8OGDAg//7v/56Kior0798/Bx10UCoqKuq8Ppvrw/bTVq1aZd99983TTz+dF154Ifvss0+t2z9qOz/qcTc2YP7pT3/KjTfemFmzZmXJkiVZvXp1rdurL7qTrBuNnqRmRPLGasj3h4bSsWPHOtOqX/f6bquOyOuf57aoz4VrrrkmybpYvdNOO+VrX/ta/vmf/7nWZ0L1Y7/yyiv1Pnb1+8L60bTapuxbf/nLX/LWW2+lc+fO9Yb5Qw89NMn/v6+v///1vW8deOCB9YZqAD49vMsD8KlQ3+GF1aMY33vvvXrvU99hfsuXL0+y7hDB6j/2Pkr1fapHNm3IP/7xj41a3gedfvrpGTx4cB599NHcddddad++fc2hbfVZtWpVktQ5hLRa9XbXdxGADR2m2bhx4w0+jx9lc16bLbEl27+h+yTrRkPecccdmTt3bsrKyvLKK6/knHPOSdOmTdOtW7eaEU3V//1ghBw1alQuueSSlJaW5rDDDsvuu++eHXbYISUlJXn44Yfz7LPP5p133tnodarezvoC5Udty4e5+OKLc8kll6SqqiqvvfZabr/99lx33XX51re+lUcffbTWKMLqfX/evHkfeqGN+i6yUoSP2uYP27+T2vvjOeeck7KysowZMya33XZbRo8enWTdof8/+tGPPvQUBBujqP20ocyZMyfHHXdc1q5dm549e+aoo47KTjvtlEaNGmXBggV55JFHau2v1eu5/qjOjVHfxayKfH/YGM2bN68zrXqdPuy29UetFvW5sDFhtvqxJ0yYkAkTJmzSY2/KvrU5+/CHvW81btw4LVu23OjHB2DbI0IC8Jm1/qFj1ar/wOzTp0/uvffejVpO9X2mTZtW76ieLTVgwIAMHTo0F154YV599dU6h71uaH1ef/31em9fsmRJrfk+bbZk++vbJ6pVR8hp06bV/AFdHRp79uyZxx57LC+99FKmT5+eJk2a5KCDDqq579q1a3P11VenvLw806dPrzOabs6cORt83A2tU/X6L126tN7bN7T9G6ukpCS77rprhg4dmhUrVuT222/P8OHDc9VVV9VZh+9///u59tprt+jxqjVqtO5sQRsKUCtXrtxgTPyw129znHTSSTnppJOyatWqzJ07N5MmTcrYsWMzYMCAzJw5M1/5ylc2e9nr76f1hbjN3U8byn/8x3/k7bffzsSJE+sE1+uvv77O6QyqX5P1R0d+lhX9ubAxjz1u3Lgcd9xxm3TfTdm3Nue9dv33rQ/+HL/33ntZvnz5JodsALYdzgkJAOvZc889s/POO+e//uu/smbNmo26T5cuXZIkTzzxRCHr1Lx58/Tv3z+vvvpqmjVrlgEDBnzo/NVXWK6srKz39uqRelv6h/H6F+z5JClq+7t3756SkpJMnz69JiRWHyZbHSMfeuihmgshrX/+x2XLlmXlypXp2rVrnQD55ptv1jpccWNVb+cHD51O1r0mTz755CYvc0OGDh2a0tLSjB49Oi+//HLN9M6dO6dRo0abtO9/1Ci36iD3yiuv1Lntv//7v2tGUn2cmjdvnl69emXkyJE555xzsnr16vz+97/fomV+2H66dOnS/OlPf8qOO+5Yc0X6j9uLL76YFi1a1Dvis759rvqiKOufn/WzrOjPhU/CY++5555p1qxZnn/++Xov1lTfe+2HvW89+eSTdU5RAcCniwgJAOtp0qRJBg0alNdffz0XXHBB3nrrrTrzLFu2LPPnz6/5/tRTT01paWlGjhyZp556qs78VVVVeeKJJzY6atbnhz/8Ye66667853/+5wZHgVWrqKjIXnvtlaeffrrOBUGmT5+eiRMnZpdddknfvn03e32S1Bw2t2jRoi1aTkMravt32WWXtG/fPnPnzs306dNrncuvQ4cO2WWXXXLjjTdm7dq1dc7zV1ZWlmbNmmXevHm1Dk1+9913M2TIkI262nJ929muXbvMnj27zgUmbr/99s06H+SGlJaW5txzz827775b6xxzn//853PSSSdlwYIFGTFiRL0B4dVXX611Tsjq/aa+yJgkX/va19KoUaPcd999tZ6rf/zjHw12xe+NMX369FRVVdWZXj2664NXVt9Up556apJ1owqrl5mse7+47LLL8tZbb+Vb3/pWvRfG+Ti0bt06b7zxRp599tla08eNG5cpU6bUmf+oo45K69at87vf/a7eUeSvv/76ZyowfRyfCxvSt2/ffPnLX84dd9xR7wW4knXnZqw+bHtzbbfddjnppJPy1ltv5fLLL6/18/Laa6/lhhtuSElJSc2+nqTmYmHXXXddrcd/5513cuWVV27R+gDwyedwbAD4gAsvvDDPP/98xo0bl9/97nfp0aNHdtttt/z973/PSy+9lCeffDJnnnlmzQUKWrRokXHjxuXUU0/NEUcckR49emTvvffOdtttl1dffTVz587NK6+8kpdffjlNmzbdrHXabbfd6r1SbX1KSkoyatSoHH/88Rk0aFAeeOCB7LfffnnppZcyYcKENG3aNLfccssWR5TDDjssN954Y6644or86U9/qhnB9nGGovoUuf09e/bMggULsnLlylrnfCwpKUmPHj3ywAMP1My3vkaNGmXgwIG54YYbctBBB6Vv37559913U1lZmTfeeCPdu3ff4MjND9vOn/3sZ+nfv3/OOOOMHHvssWnbtm2ee+65TJ06NYcffniDjkobOHBgRo0alV//+tc577zzakaBXnvttXnxxRdzzTXX5Fe/+lUOOuiglJeXZ8mSJXnhhRcyZ86cDB8+PHvuuWeSpF27dtl9993zxBNP5F/+5V/Stm3bNG7cOEcddVTat2+fL3zhC/nmN7+Z8ePHp3v37jniiCOyevXqTJkyJa1bt/7YDtX89re/nR133DGdO3dO69atU1JSkqeffjpPPPFEvvSlL+X444/fouV37do1559/fq6//vp069Ytxx9/fJo3b56pU6fmmWeeyb777ltzdfGGNGrUqA3+Q0aXLl1qLixz1llnZcqUKTnqqKNq1u2Pf/xjnnzyyfTr169O+G7atGnGjh2bE044IYMGDcq4cePSpUuXrFmzJi+88EKmTZuWhQsX1nvo+afRx/G5sCHbbbdd7rrrrpxwwgk5+eST07lz53z1q1/NjjvumFdffTXz58/PwoULM2PGjC0+B+Nll12WJ554IuPGjcv8+fNz6KGHZuXKlfntb3+bN954IxdddFHNKNlk3cVnvv/97+fWW29Nt27dctxxx2X77bfPI488kp133rnBLvwEwCeTCAkAH9CkSZOMGzcuv/nNb3L33Xfn97//fd588820bNkye+yxR84///ycdNJJte7To0ePPP7447npppsyZcqUPPXUU2nSpEnKy8vTtWvXDBs27GM9B+MBBxyQadOmZeTIkZk2bVqmTJmSnXfeOUcffXQGDx5c7xVeN9Vhhx2Wq6++Or/85S9z22231VykYmtHyKS47e/Zs2duuummmv//4G0PPPBAdthhh3Tt2rXOfS+99NLssssuufPOO/PLX/4yzZs3z6GHHpp///d/r/cKthvjwAMPzKRJk3LllVdmypQpmTJlSjp16pSHHnooU6ZMadAI2axZs5x//vkZMmRIrrzyyowfPz5JstNOO+Whhx7KnXfemV//+td56KGHsnr16pSVlaVNmza57LLL0r9//5rlNG7cOHfddVeGDRuWRx99NP/7v/+bqqqq7Lrrrmnfvn2S5Cc/+UnKy8vz61//OmPGjEl5eXkGDBiQiy66qN7ntgjDhg3LH/7whyxYsCBTpkxJkyZNsvvuu+fiiy/OwIEDP3JE8sb40Y9+lI4dO+bWW2/Nr3/967zzzjtp06ZNLrjggpx77rm1DulvKLfccssGbxs0aFBNhDz88MNz77335j/+4z/ywAMPpFGjRunUqVMmTpyYl19+uU6ETNaNYq2srMxPf/rT/O53v8vcuXPTrFmz/NM//VPOP//87Ljjjg2+PZ9kW/NzYd99983jjz+eUaNG5ZFHHsk999yTqqqqlJeXZ++9984PfvCDBjnUv7S0NI8++mh++tOfZsKECbn55puz/fbbp2PHjhk4cGC956S85ppr8pWvfCW33XZbxo4dm5YtW+aYY47J0KFDc8ghh2zxOgHwyVWyYsWKuseZAAAAAAA0EOeEBAAAAAAKJUICAAAAAIUSIQEAAACAQomQAAAAAEChREgAAAAAoFAiJAAAAABQKBESAAAAACiUCAmwDVq4cOHWXgUAADaD3+OAzyoREgAAAAAolAgJAAAAABRKhAQAAAAACiVCAgAAAACFEiEBAAAAgEKJkAAAAABAoURIAAAAAKBQIiQAAAAAUCgREgAAAAAolAgJAAAAABRKhAQAAAAACiVCAgAAAACFEiEBAAAAgEKJkAAAAABAoURIAAAAAKBQIiQAAAAAUCgREgAAAAAolAgJAAAAABRKhAQAAAAACiVCAgAAAACFEiEBAAAAgEKJkAAAAABAoURIAAAAAKBQIiQAAAAAUCgREgAAAAAolAgJAAAAABRKhAQAAAAACiVCAgAAAACFEiEBAAAAgEKJkAAAAABAoURIAAAAAKBQIiQAAAAAUCgREgAAAAAolAgJAAAAABRKhAQAAAAACtVka68AAAAAfJIMH1ZZ8CMsLmzJlw7rXtiubTGeAAAgAElEQVSyAbaEkZAAAAAAQKFKVqxYUbW1VwKATbNw4cK0a9dua68GAACboHqEpdGKwGeRkZAAAAAAQKFESAAAAACgUCIkAAAAAFAoERIAAAAAKJQICQAAAAAUSoQEAAAAAAolQgIAAAAAhRIhAQAAAIBCiZAAAAAAQKFESAAAAACgUCIkAAAAAFAoERIAAAAAKJQICQAAAAAUSoQEAAAAAAolQgIAAAAAhRIhAQAAAIBCiZAAAAAAQKFESAAAAACgUCIkAAAAAFAoERIAAAAAKJQICQAAAAAUSoQEAAAAAAolQgIAAAAAhRIhAQAAAIBCiZAAAAAAQKFESAAAAACgUCIkAAAAAFCoJlt7BQA+jYYPq/wYHmVxIUu9dFj3QpYLAADAZ5eRkAAAAABAoYyEBChAkaMJq0dZGrEIAADAtsJISAAAAACgUCIkAAAAAFAoERIAAAAAKJQICQAAAAAUSoQEAAAAAApVWIS87bbb0rFjx5SXl6dnz56ZNWvWBuedMGFC+vfvn7Zt22b33XdP796988gjj9SZ78EHH0xFRUVatWqVioqKTJw4sajVBwAAAAAaSCER8v7778+QIUMyePDgzJgxI127ds2AAQOyaNGieud//PHH06NHj9x3332ZMWNGvv71r+fUU0+tFS6feuqpfPe7382AAQNSWVmZAQMG5Dvf+U7mzp1bxCYAAAAAAA2kZMWKFVUNvdDevXtnv/32y4033lgz7YADDki/fv1y2WWXbdQyevXqlW7dumX48OFJkjPOOCNvvPFGfvvb39bM069fv3z+85/P7bff3rAbAPAJNnxYZZLk0mHdt/KaAACwKfweB3yWNfhIyDVr1mTevHnp1atXrem9evXK7NmzN3o5b775ZkpLS2u+nzNnTp1l9u7de5OWCQAAAAB8/Jo09AKXLVuW9957L2VlZbWml5WV5fXXX9+oZfziF7/I3/72t5x00kk105YsWbLJy1y4cOEmrDnAtsV7HADAtsnvccCnUbt27T709gaPkFvqwQcfzI9+9KOMGTMmrVu33qJlfdTGA2ybFifxHgcAsO3xexzw2dXgh2Pvsssuady4cZYuXVpr+tKlS9OqVasPve+DDz6YQYMG5ZZbbslRRx1V67by8vLNWiYAAAAAsHU1eIRs2rRp9t9//0ydOrXW9KlTp6aiomKD93vggQcycODA3HzzzenXr1+d27t06bLJywQAAAAAtr5CDsc+++yzM3DgwHTq1CkVFRUZM2ZMFi9enDPOOCNJMnDgwCTJ6NGjkyS/+c1vMnDgwFx55ZU56KCDsmTJkiTrgmaLFi2SJIMGDUrfvn1zww035Oijj85DDz2UysrKTJ48uYhNAAAAAAAaSCER8oQTTsjy5cszcuTILFmyJPvss0/uu+++mnM8vvLKK7XmHzNmTNauXZtLLrkkl1xySc30gw8+OA8//HCS1MTMq666Kj/+8Y/zpS99KWPGjEnnzp2L2AQAAAAAoIGUrFixomprrwQAG2/4sMokyaXDum/lNQEAYFP4PQ74LGvwc0ICAAAAAKxPhAQAAAAACiVCAgAAAACFEiEBAAAAgEKJkAAAAABAoURIAAAAAKBQIiQAAAAAUCgREgAAAAAolAgJAAAAABRKhAQAAAAACiVCAgAAAACFEiEBAAAAgEKJkAAAAABAoURIAAAAAKBQIiQAAAAAUCgREgAAAAAolAgJAAAAABRKhAQAAAAACiVCAgAAAACFEiEBAAAAgEKJkAAAAABAoURIAAAAAKBQIiQAAAAAUCgREgAAAAAolAgJAAAAABRKhAQAAAAACiVCAgAAAACFEiEBAAAAgEKJkAAAAABAoURIAAAAAKBQIiQAAAAAUCgREgAAAAAolAgJAAAAABRKhAQAAAAACiVCAgAAAACFEiEBAAAAgEKJkAAAAABAoURIAAAAAKBQIiQAAAAAUCgREgAAAAAolAgJAAAAABRKhAQAAAAACiVCAgAAAACFEiEBAAAAgEKJkAAAAABAoURIAAAAAKBQIiQAAAAAUCgREgAAAAAolAgJAAAAABRKhAQAAAAACiVCAgAAAACFEiEBAAAAgEKJkAAAAABAoURIAAAAAKBQIiQAAAAAUCgREgAAAAAolAgJAAAAABRKhAQAAAAACiVCAgAAAACFEiEBAAAAgEKJkAAAAABAoURIAAAAAKBQIiQAAAAAUCgREgAAAAAolAgJAAAAABRKhAQAAAAACiVCAgAAAACFEiEBAAAAgEKJkAAAAABAoURIAAAAAKBQIiQAAAAAUCgREgAAAAAolAgJAAAAABRKhAQAAAAACiVCAgAAAACFEiEBAAAAgEKJkAAAAABAoURIAAAAAKBQIiQAAAAAUCgREgAAAAAolAgJAAAAABRKhAQAAAAACiVCAgAAAACFEiEBAAAAgEKJkAAAAABAoURIAAAAAKBQIiQAAAAAUCgREgAAAAAoVGER8rbbbkvHjh1TXl6enj17ZtasWRucd/HixTnzzDPTpUuXtGzZMmeddVadee6+++6UlpbW+Vq9enVRmwAAAAAANIBCIuT999+fIUOGZPDgwZkxY0a6du2aAQMGZNGiRfXO/84776Rly5Y577zz0rlz5w0ut1mzZvnzn/9c6+v//J//U8QmAAAAAAANpJAI+fOf/zwnn3xyTj/99Oy1114ZOXJkysvLM2bMmHrnb9OmTa699tqccsopadGixQaXW1JSkvLy8lpfAAAAAMAnW4NHyDVr1mTevHnp1atXrem9evXK7Nmzt2jZb7/9dtq3b5999903J510Up555pktWh4AAAAAULwmDb3AZcuW5b333ktZWVmt6WVlZXn99dc3e7nt2rXLTTfdlPbt2+fNN9/MLbfckj59+mTmzJlp27ZtvfdZuHDhZj8ewCed9zgAgG2T3+OAT6N27dp96O0NHiGL0rVr13Tt2rXm+4qKinTv3j2jR4/OtddeW+99PmrjAbZNi5N4jwMA2Pb4PQ747Grww7F32WWXNG7cOEuXLq01fenSpWnVqlWDPU7jxo2z//7758UXX2ywZQIAAAAADa/BI2TTpk2z//77Z+rUqbWmT506NRUVFQ32OFVVVXnuuedcnAYAAAAAPuEKORz77LPPzsCBA9OpU6dUVFRkzJgxWbx4cc4444wkycCBA5Mko0ePrrnP/PnzkySrVq1KSUlJ5s+fn6ZNm2bvvfdOklx99dXp0qVL2rZtm1WrVmX06NF57rnncv311xexCQAAAABAAykkQp5wwglZvnx5Ro4cmSVLlmSfffbJfffdl9atWydJXnnllTr36dGjR63vJ0+enD322CMLFixIkqxcuTLnnntuXn/99TRv3jwdO3bMI488kk6dOhWxCQAAAABAAylZsWJF1dZeCQA23vBhlUmSS4d138prAgDApvB7HPBZ1uDnhAQAAAAAWJ8ICQAAAAAUSoQEAAAAAAolQgIAAAAAhRIhAQAAAIBCiZAAAAAAQKFESAAAAACgUCIkAAAAAFAoERIAAAAAKJQICQAAAAAUSoQEAAAAAAolQgIAAAAAhRIhAQAAAIBCiZAAAAAAQKFESAAAAACgUCIkAAAAAFAoERIAAAAAKJQICQAAAAAUSoQEAAAAAAolQgIAAAAAhRIhAQAAAIBCiZAAAAAAQKFESAAAAACgUCIkAAAAAFAoERIAAAAAKJQICQAAAAAUSoQEAAAAAAolQgIAAAAAhRIhAQAAAIBCiZAAAAAAQKFESAAAAACgUCIkAAAAAFAoERIAAAAAKJQICQAAAAAUSoQEAAAAAAolQgIAAAAAhRIhAQAAAIBCiZAAAAAAQKFESAAAAACgUCIkAAAAAFAoERIAAAAAKJQICQAAAAAUSoQ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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1, figsize=(20, 15))\n",
    "\n",
    "# Create an axes instance\n",
    "ax = fig.add_subplot(111)\n",
    "bp = ax.boxplot(forward_three_month_rolling_returns, patch_artist=True, showfliers=False)\n",
    "\n",
    "## change outline color, fill color and linewidth of the boxes\n",
    "for box in bp['boxes']:\n",
    "    # change outline color\n",
    "    box.set( color='#7570b3', linewidth=2)\n",
    "    # change fill color\n",
    "    box.set( facecolor = '#1b9e77' )\n",
    "\n",
    "## change color and linewidth of the whiskers\n",
    "for whisker in bp['whiskers']:\n",
    "    whisker.set(color='#7570b3', linewidth=2)\n",
    "\n",
    "## change color and linewidth of the caps\n",
    "for cap in bp['caps']:\n",
    "    cap.set(color='#7570b3', linewidth=2)\n",
    "\n",
    "## change color and linewidth of the medians\n",
    "for median in bp['medians']:\n",
    "    median.set(color='#b2df8a', linewidth=2)\n",
    "\n",
    "## change the style of fliers and their fill\n",
    "for flier in bp['fliers']:\n",
    "    flier.set(marker='o', color='#e7298a', alpha=0.5)\n",
    "ind = ['Normal Yield Curve', 'Inverted Yield Curve']\n",
    "#plt.boxplot(forward_one_year_rolling_returns)\n",
    "ax.set_xticklabels(ind)\n",
    "ax.get_xaxis().tick_bottom()\n",
    "ax.get_yaxis().tick_left()\n",
    "ax.set_title('Three Month Forward Returns for Each Time Period')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1, figsize=(20, 15))\n",
    "\n",
    "# Create an axes instance\n",
    "ax = fig.add_subplot(111)\n",
    "bp = ax.boxplot(forward_one_year_rolling_returns, patch_artist=True, showfliers=False)\n",
    "\n",
    "## change outline color, fill color and linewidth of the boxes\n",
    "for box in bp['boxes']:\n",
    "    # change outline color\n",
    "    box.set( color='#7570b3', linewidth=2)\n",
    "    # change fill color\n",
    "    box.set( facecolor = '#1b9e77' )\n",
    "\n",
    "## change color and linewidth of the whiskers\n",
    "for whisker in bp['whiskers']:\n",
    "    whisker.set(color='#7570b3', linewidth=2)\n",
    "\n",
    "## change color and linewidth of the caps\n",
    "for cap in bp['caps']:\n",
    "    cap.set(color='#7570b3', linewidth=2)\n",
    "\n",
    "## change color and linewidth of the medians\n",
    "for median in bp['medians']:\n",
    "    median.set(color='#b2df8a', linewidth=2)\n",
    "\n",
    "## change the style of fliers and their fill\n",
    "for flier in bp['fliers']:\n",
    "    flier.set(marker='o', color='#e7298a', alpha=0.5)\n",
    "ind = ['Normal Yield Curve', 'Inverted Yield Curve']\n",
    "#plt.boxplot(forward_one_year_rolling_returns)\n",
    "ax.set_xticklabels(ind)\n",
    "ax.get_xaxis().tick_bottom()\n",
    "ax.get_yaxis().tick_left()\n",
    "ax.set_title('One Year Forward Returns for Each Time Period')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "Again, nothing new or earth shattering uncovered in these charts, but shows the larger dispersion of possible future returns after an inverted yield curve when compared to normal yield curves and lower returns on average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "econ_indicators.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "extensions": {
   "jupyter_dashboards": {
    "activeView": "report_default",
    "version": 1,
    "views": {
     "grid_default": {
      "name": "grid",
      "type": "grid"
     },
     "report_default": {
      "name": "report",
      "type": "report"
     }
    }
   }
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}

## requirements.txt
pandas==0.24.0
pandas-datareader==0.7.0
matplotlib==3.0.2
datetime
	pandas==0.24.0
	pandas-datareader==0.7.0
	matplotlib==3.0.2
	datetime