time_series_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "import quandl as ql\n",
    "import statsmodels.formula.api as smf\n",
    "pd.set_option('display.float_format', lambda x: '%.9f' % x)\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n",
    "import os\n",
    "ql.ApiConfig.api_key = os.getenv('quandl_key')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Return Forecasting:  Read Historical Daily Yen Futures Data from Quandl\n",
    " In this activity, you will pull historical Dollar-Yen exchange rate futures data from Quandl and test whether there is any predictable behavior using time-series models.\n",
    "\n",
    " * First, you'll need to download the futures data using the Quandl API, and return it as a Pandas DataFrame.\n",
    " * You'll need a Quandl API key (free): https://www.quandl.com/\n",
    " * (Use code \"CHRIS/CME_JY1\")\n",
    " * Trim the resulting data to include only from the the year 1990 through today."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "foo = os.getenv('quandl_key')"
   ]
  },
  {
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Change</th>\n",
       "      <th>Settle</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Previous Day Open Interest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976-08-02</th>\n",
       "      <td>3398.000000000</td>\n",
       "      <td>3401.000000000</td>\n",
       "      <td>3398.000000000</td>\n",
       "      <td>3401.000000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>3401.000000000</td>\n",
       "      <td>2.000000000</td>\n",
       "      <td>1.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-08-03</th>\n",
       "      <td>3401.000000000</td>\n",
       "      <td>3401.000000000</td>\n",
       "      <td>3401.000000000</td>\n",
       "      <td>3401.000000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>3401.000000000</td>\n",
       "      <td>0.000000000</td>\n",
       "      <td>1.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-08-04</th>\n",
       "      <td>3401.000000000</td>\n",
       "      <td>3401.000000000</td>\n",
       "      <td>3401.000000000</td>\n",
       "      <td>3401.000000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>3401.000000000</td>\n",
       "      <td>0.000000000</td>\n",
       "      <td>1.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-08-05</th>\n",
       "      <td>3401.000000000</td>\n",
       "      <td>3401.000000000</td>\n",
       "      <td>3401.000000000</td>\n",
       "      <td>3401.000000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>3401.000000000</td>\n",
       "      <td>0.000000000</td>\n",
       "      <td>1.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-08-06</th>\n",
       "      <td>3401.000000000</td>\n",
       "      <td>3401.000000000</td>\n",
       "      <td>3401.000000000</td>\n",
       "      <td>3401.000000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>3401.000000000</td>\n",
       "      <td>0.000000000</td>\n",
       "      <td>1.000000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                     Open           High            Low           Last  \\\n",
       "Date                                                                     \n",
       "1976-08-02 3398.000000000 3401.000000000 3398.000000000 3401.000000000   \n",
       "1976-08-03 3401.000000000 3401.000000000 3401.000000000 3401.000000000   \n",
       "1976-08-04 3401.000000000 3401.000000000 3401.000000000 3401.000000000   \n",
       "1976-08-05 3401.000000000 3401.000000000 3401.000000000 3401.000000000   \n",
       "1976-08-06 3401.000000000 3401.000000000 3401.000000000 3401.000000000   \n",
       "\n",
       "            Change         Settle      Volume  Previous Day Open Interest  \n",
       "Date                                                                       \n",
       "1976-08-02     nan 3401.000000000 2.000000000                 1.000000000  \n",
       "1976-08-03     nan 3401.000000000 0.000000000                 1.000000000  \n",
       "1976-08-04     nan 3401.000000000 0.000000000                 1.000000000  \n",
       "1976-08-05     nan 3401.000000000 0.000000000                 1.000000000  \n",
       "1976-08-06     nan 3401.000000000 0.000000000                 1.000000000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Futures contract on the Yen-dollar exchange rate:\n",
    "# This is the continuous chain of the futures contracts that are 1 month to expiration\n",
    "fut = ql.get(\"CHRIS/CME_JY1\")\n",
    "fut.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Change</th>\n",
       "      <th>Settle</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Previous Day Open Interest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>6954.000000000</td>\n",
       "      <td>6954.000000000</td>\n",
       "      <td>6835.000000000</td>\n",
       "      <td>6847.000000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>6847.000000000</td>\n",
       "      <td>48336.000000000</td>\n",
       "      <td>51473.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>6877.000000000</td>\n",
       "      <td>6910.000000000</td>\n",
       "      <td>6865.000000000</td>\n",
       "      <td>6887.000000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>6887.000000000</td>\n",
       "      <td>38206.000000000</td>\n",
       "      <td>53860.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>6937.000000000</td>\n",
       "      <td>7030.000000000</td>\n",
       "      <td>6924.000000000</td>\n",
       "      <td>7008.000000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>7008.000000000</td>\n",
       "      <td>49649.000000000</td>\n",
       "      <td>55699.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>6952.000000000</td>\n",
       "      <td>6985.000000000</td>\n",
       "      <td>6942.000000000</td>\n",
       "      <td>6950.000000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>6950.000000000</td>\n",
       "      <td>29944.000000000</td>\n",
       "      <td>53111.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-08</th>\n",
       "      <td>6936.000000000</td>\n",
       "      <td>6972.000000000</td>\n",
       "      <td>6936.000000000</td>\n",
       "      <td>6959.000000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>6959.000000000</td>\n",
       "      <td>19763.000000000</td>\n",
       "      <td>52072.000000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Open           High            Low           Last  \\\n",
       "Date                                                                     \n",
       "1990-01-02 6954.000000000 6954.000000000 6835.000000000 6847.000000000   \n",
       "1990-01-03 6877.000000000 6910.000000000 6865.000000000 6887.000000000   \n",
       "1990-01-04 6937.000000000 7030.000000000 6924.000000000 7008.000000000   \n",
       "1990-01-05 6952.000000000 6985.000000000 6942.000000000 6950.000000000   \n",
       "1990-01-08 6936.000000000 6972.000000000 6936.000000000 6959.000000000   \n",
       "\n",
       "            Change         Settle          Volume  Previous Day Open Interest  \n",
       "Date                                                                           \n",
       "1990-01-02     nan 6847.000000000 48336.000000000             51473.000000000  \n",
       "1990-01-03     nan 6887.000000000 38206.000000000             53860.000000000  \n",
       "1990-01-04     nan 7008.000000000 49649.000000000             55699.000000000  \n",
       "1990-01-05     nan 6950.000000000 29944.000000000             53111.000000000  \n",
       "1990-01-08     nan 6959.000000000 19763.000000000             52072.000000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Trim the dataset to begin on January 1st, 1990\n",
    "fut = fut[\"1989-12-31\":]\n",
    "fut.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Return Forecasting: Initial Time-Series Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Start by plotting the \"Settle\" price. Do you see any patterns, long-term and/or short?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fefa55a3d68>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot just the \"Settle\" column from the dataframe:\n",
    "fut.Settle.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Return Forecasting: MA/EWMA Smoothing of Futures Prices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 1. Plot the Settle Price, along with a moving average of 15 and 60 days. Use indexing to just plot the data from 2015 onward.\n",
    " 2. What do you see now, in terms of short versus long-term trends?\n",
    " 3. Try an exponential moving average, with a half life of 15 days.\n",
    " 4. Which seems to respond faster, an exponential moving average with a half life of 15 days, or a moving average of 15 days?\n",
    "\n",
    " (15 days = 3 weeks)\n",
    " (60 days = 3 months)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fefa48c4240>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "###### Moving averages and exponentially weighted moving averages \n",
    "# Create a dataframe using just the \"Settle\" price\n",
    "df=fut[[\"Settle\"]].copy()\n",
    "\n",
    "# Add in columns for moving average windows of 15 and 60 days, and a column for an EWMA window of 15 days\n",
    "df['Rolling15'] = df[\"Settle\"].rolling(window=15).mean()\n",
    "df['Rolling60'] = df[\"Settle\"].rolling(window=60).mean()\n",
    "df['RollingEWM'] = df[\"Settle\"].ewm(halflife=15).mean()\n",
    "\n",
    "# Plot the settle price and moving average/ewma columns all in one plot from 2015:\n",
    "df[\"2015\":].plot(figsize=(14,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Return Forecasting: Decomposition Using a Hodrick-Prescott Filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Using a Hodrick-Prescott Filter, decompose the Settle price into a trend and noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "# Apply the Hodrick-Prescott Filter by decomposing the \"Settle\" price into two separate series:\n",
    "ts_noise, ts_trend =  sm.tsa.filters.hpfilter(df['Settle']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "1990-01-02   6908.503966971\n",
       "1990-01-03   6908.799755631\n",
       "1990-01-04   6909.057104311\n",
       "1990-01-05   6909.223948186\n",
       "1990-01-08   6909.310061738\n",
       "Name: Settle, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_trend.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "futures_decomp = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Settle</th>\n",
       "      <th>Noise</th>\n",
       "      <th>Trend</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>1990-01-02</th>\n",
       "      <td>6847.000000000</td>\n",
       "      <td>-61.503966971</td>\n",
       "      <td>6908.503966971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>6887.000000000</td>\n",
       "      <td>-21.799755631</td>\n",
       "      <td>6908.799755631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>7008.000000000</td>\n",
       "      <td>98.942895689</td>\n",
       "      <td>6909.057104311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>6950.000000000</td>\n",
       "      <td>40.776051814</td>\n",
       "      <td>6909.223948186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-08</th>\n",
       "      <td>6959.000000000</td>\n",
       "      <td>49.689938262</td>\n",
       "      <td>6909.310061738</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Settle         Noise          Trend\n",
       "Date                                                  \n",
       "1990-01-02 6847.000000000 -61.503966971 6908.503966971\n",
       "1990-01-03 6887.000000000 -21.799755631 6908.799755631\n",
       "1990-01-04 7008.000000000  98.942895689 6909.057104311\n",
       "1990-01-05 6950.000000000  40.776051814 6909.223948186\n",
       "1990-01-08 6959.000000000  49.689938262 6909.310061738"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe of just the settle price, and add in the \"noise\" and \"trend\" series from above:\n",
    "futures_decomp =  df[['Settle']].copy()\n",
    "futures_decomp[\"Noise\"] =  ts_noise\n",
    "futures_decomp[\"Trend\"] =  ts_trend\n",
    "futures_decomp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the \"Settle\" and \"Trend\" price \n",
    "fig = plt.figure(figsize=(12,8))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "futures_decomp[[\"Settle\",\"Trend\"]].plot(ax=ax)\n",
    "#plt.show()\n",
    "\n",
    "# Then plot the \"noise\" separately\n",
    "# I like it better on the same chart : )\n",
    "futures_decomp[\"Noise\"].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "futures_decomp[\"Noise\"].plot(figsize=(12,8))\n",
    "plt.show()\n",
    "futures_decomp[\"Noise\"][\"2015\":].plot(figsize=(12,8), title=\"2015 and beyond...\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Return Forecasting: Seasonal Effects with Sklearn Linear Regression\n",
    " Using the full dataset, use a SKLearn linear regression to predict Yen futures (\"settle\") returns with *lagged* Yen futures returns and categorical calendar seasonal effects (e.g., day-of-week or week-of-year seasonal effects)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Change</th>\n",
       "      <th>Settle</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Previous Day Open Interest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>6954.000000000</td>\n",
       "      <td>6954.000000000</td>\n",
       "      <td>6835.000000000</td>\n",
       "      <td>6847.000000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>6847.000000000</td>\n",
       "      <td>48336.000000000</td>\n",
       "      <td>51473.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>6877.000000000</td>\n",
       "      <td>6910.000000000</td>\n",
       "      <td>6865.000000000</td>\n",
       "      <td>6887.000000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>6887.000000000</td>\n",
       "      <td>38206.000000000</td>\n",
       "      <td>53860.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>6937.000000000</td>\n",
       "      <td>7030.000000000</td>\n",
       "      <td>6924.000000000</td>\n",
       "      <td>7008.000000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>7008.000000000</td>\n",
       "      <td>49649.000000000</td>\n",
       "      <td>55699.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>6952.000000000</td>\n",
       "      <td>6985.000000000</td>\n",
       "      <td>6942.000000000</td>\n",
       "      <td>6950.000000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>6950.000000000</td>\n",
       "      <td>29944.000000000</td>\n",
       "      <td>53111.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-08</th>\n",
       "      <td>6936.000000000</td>\n",
       "      <td>6972.000000000</td>\n",
       "      <td>6936.000000000</td>\n",
       "      <td>6959.000000000</td>\n",
       "      <td>nan</td>\n",
       "      <td>6959.000000000</td>\n",
       "      <td>19763.000000000</td>\n",
       "      <td>52072.000000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Open           High            Low           Last  \\\n",
       "Date                                                                     \n",
       "1990-01-02 6954.000000000 6954.000000000 6835.000000000 6847.000000000   \n",
       "1990-01-03 6877.000000000 6910.000000000 6865.000000000 6887.000000000   \n",
       "1990-01-04 6937.000000000 7030.000000000 6924.000000000 7008.000000000   \n",
       "1990-01-05 6952.000000000 6985.000000000 6942.000000000 6950.000000000   \n",
       "1990-01-08 6936.000000000 6972.000000000 6936.000000000 6959.000000000   \n",
       "\n",
       "            Change         Settle          Volume  Previous Day Open Interest  \n",
       "Date                                                                           \n",
       "1990-01-02     nan 6847.000000000 48336.000000000             51473.000000000  \n",
       "1990-01-03     nan 6887.000000000 38206.000000000             53860.000000000  \n",
       "1990-01-04     nan 7008.000000000 49649.000000000             55699.000000000  \n",
       "1990-01-05     nan 6950.000000000 29944.000000000             53111.000000000  \n",
       "1990-01-08     nan 6959.000000000 19763.000000000             52072.000000000  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Use the dataframe containing \"Settle\" Prices:\n",
    "fut[\"Settle\"]\n",
    "fut.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lagged Return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-02-18</th>\n",
       "      <td>0.409123453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-19</th>\n",
       "      <td>-0.427829276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-20</th>\n",
       "      <td>-0.020460358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-21</th>\n",
       "      <td>0.020464545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-24</th>\n",
       "      <td>-0.204603581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Lagged Return\n",
       "Date                     \n",
       "2014-02-18    0.409123453\n",
       "2014-02-19   -0.427829276\n",
       "2014-02-20   -0.020460358\n",
       "2014-02-21    0.020464545\n",
       "2014-02-24   -0.204603581"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# HELP! Why do these numbers keep changing???\n",
    "### OOOOooooooh, maybe is the \"shift\" deleting rows everytime...?\n",
    "\n",
    "# Construct a \"Futures Return\" column, using pandas pct_change() on the \"Settle\" price \n",
    "# If will be easier to also multiply this by 100, both to read and when we use it with SKLearn:\n",
    "fut[\"Futures Return\"] = fut[\"Settle\"].copy().pct_change()*100\n",
    "\n",
    "# Feature Engineering\n",
    "# Create a second \"Lagged Return\" variable, that is the futures return variable shifted above, but lagged 1 day:\n",
    "# We\"ll use this lagged return as the feature vector X:\n",
    "fut[\"Lagged Return\"] = fut[\"Futures Return\"].shift()\n",
    "fut = fut.replace(-np.inf, np.nan).dropna()\n",
    "\n",
    "# Construct a new \"X\" dataframe using just the \"Lagged Return\" variable:\n",
    "X = fut[\"Lagged Return\"].to_frame()\n",
    "#fut.head()\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Lagged Return</th>\n",
       "      <th>Week of Year_2</th>\n",
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       "      <th>Week of Year_4</th>\n",
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       "      <th>Week of Year_48</th>\n",
       "      <th>Week of Year_49</th>\n",
       "      <th>Week of Year_50</th>\n",
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       "      <th>Week of Year_52</th>\n",
       "      <th>Week of Year_53</th>\n",
       "    </tr>\n",
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       "      <th>Date</th>\n",
       "      <th></th>\n",
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       "      <th>2014-02-18</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-20</th>\n",
       "      <td>-0.020460358</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2014-02-21</th>\n",
       "      <td>0.020464545</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-24</th>\n",
       "      <td>-0.204603581</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Lagged Return  Week of Year_2  Week of Year_3  Week of Year_4  \\\n",
       "Date                                                                        \n",
       "2014-02-18    0.409123453               0               0               0   \n",
       "2014-02-19   -0.427829276               0               0               0   \n",
       "2014-02-20   -0.020460358               0               0               0   \n",
       "2014-02-21    0.020464545               0               0               0   \n",
       "2014-02-24   -0.204603581               0               0               0   \n",
       "\n",
       "            Week of Year_5  Week of Year_6  Week of Year_7  Week of Year_8  \\\n",
       "Date                                                                         \n",
       "2014-02-18               0               0               0               1   \n",
       "2014-02-19               0               0               0               1   \n",
       "2014-02-20               0               0               0               1   \n",
       "2014-02-21               0               0               0               1   \n",
       "2014-02-24               0               0               0               0   \n",
       "\n",
       "            Week of Year_9  Week of Year_10  ...  Week of Year_44  \\\n",
       "Date                                         ...                    \n",
       "2014-02-18               0                0  ...                0   \n",
       "2014-02-19               0                0  ...                0   \n",
       "2014-02-20               0                0  ...                0   \n",
       "2014-02-21               0                0  ...                0   \n",
       "2014-02-24               1                0  ...                0   \n",
       "\n",
       "            Week of Year_45  Week of Year_46  Week of Year_47  \\\n",
       "Date                                                            \n",
       "2014-02-18                0                0                0   \n",
       "2014-02-19                0                0                0   \n",
       "2014-02-20                0                0                0   \n",
       "2014-02-21                0                0                0   \n",
       "2014-02-24                0                0                0   \n",
       "\n",
       "            Week of Year_48  Week of Year_49  Week of Year_50  \\\n",
       "Date                                                            \n",
       "2014-02-18                0                0                0   \n",
       "2014-02-19                0                0                0   \n",
       "2014-02-20                0                0                0   \n",
       "2014-02-21                0                0                0   \n",
       "2014-02-24                0                0                0   \n",
       "\n",
       "            Week of Year_51  Week of Year_52  Week of Year_53  \n",
       "Date                                                           \n",
       "2014-02-18                0                0                0  \n",
       "2014-02-19                0                0                0  \n",
       "2014-02-20                0                0                0  \n",
       "2014-02-21                0                0                0  \n",
       "2014-02-24                0                0                0  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add into the X dataframe either a \"dayofweek\" or \"weekofyear\" column, (e.g., using X.index.weekofyear) \n",
    "X[\"Week of Year\"] = X.index.weekofyear\n",
    "\n",
    "# Create a new categorical X dataframe, by using pd.get_dummies() on the seasonal column created just above:\n",
    "#X_binary_encoded = pd.get_dummies(X,prefix=\"Week\")\n",
    "X_binary_encoded = pd.get_dummies(X,columns=['Week of Year'], drop_first=True)\n",
    "X_binary_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a \"y\" series, using just the \"Futures Return\" column created above.\n",
    "y = fut[\"Futures Return\"].copy()\n",
    "\n",
    "# Construct a \"LinearRegression\" object:\n",
    "model = LinearRegression()\n",
    "\n",
    "# Fit a model using the X and y objects:\n",
    "results = model.fit(X_binary_encoded,y)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.09345819, -0.05400652, -0.07320878, ...,  0.07617032,\n",
       "        0.0967315 ,  0.06911629])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Construct predictions based on the X object:\n",
    "predictions = model.predict(X_binary_encoded)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Summarize the quality of your model using R2 and root-mean-squared-error (RMSE) statistics. How good is it?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error (MSE): 0.28327437159459073\n",
      "R-squared (R2 ): 0.04919945433764117\n",
      "Root Mean Squared Error (RMSE): 0.532235259631106\n",
      "Standard Deviation of Futures Return: 0.546025445415136\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "# Calculate r2_score (r-squared)\n",
    "r2 = r2_score(y,predictions)\n",
    "\n",
    "# Calculate mean squared error \n",
    "# (A \"good\" MSE score will be close to zero while a \"good\" R2 Score will be close to 1.)\n",
    "mse = mean_squared_error(y,predictions)\n",
    "\n",
    "# Using mean squared eror above, calculate root-mean-squared error (RMSE)\n",
    "rmse = np.sqrt(mse)\n",
    "\n",
    "print(f\"Mean Squared Error (MSE): {mse}\")\n",
    "print(f\"R-squared (R2 ): {r2}\")\n",
    "print(f\"Root Mean Squared Error (RMSE): {rmse}\")\n",
    "print(f\"Standard Deviation of Futures Return: {y.std()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " #### Plotting and Independent (X) Variable Interpretation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Plot the model predictions of returns against actual returns, for just data from 2018 onward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>2014-02-18</th>\n",
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       "      <td>-0.093458188</td>\n",
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       "    <tr>\n",
       "      <th>2014-02-19</th>\n",
       "      <td>-0.020460358</td>\n",
       "      <td>-0.054006524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-20</th>\n",
       "      <td>0.020464545</td>\n",
       "      <td>-0.073208782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-21</th>\n",
       "      <td>-0.204603581</td>\n",
       "      <td>-0.075137870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-24</th>\n",
       "      <td>0.020502307</td>\n",
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       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "            Futures Return   prediction\n",
       "Date                                   \n",
       "2014-02-18    -0.427829276 -0.093458188\n",
       "2014-02-19    -0.020460358 -0.054006524\n",
       "2014-02-20     0.020464545 -0.073208782\n",
       "2014-02-21    -0.204603581 -0.075137870\n",
       "2014-02-24     0.020502307 -0.066382647"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a \"y\" dataframe, using just the \"Futures Return\" column created above. \n",
    "y_df = y.to_frame()\n",
    "\n",
    "# Add the model predictions from above as an additional column to that dataframe:\n",
    "y_df[\"prediction\"] = predictions\n",
    "y_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fefa43e6b70>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the two from 2018 onward\n",
    "y_df[\"2018\":].plot(figsize=(12,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Separately, plot the calendar effects estimated from the model using matplotlib. Do you see any seasonal patterns?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Week of Year_2</th>\n",
       "      <td>0.081178409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_3</th>\n",
       "      <td>-0.211048541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_4</th>\n",
       "      <td>-0.207719167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_5</th>\n",
       "      <td>0.015616153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_6</th>\n",
       "      <td>-0.049868534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_7</th>\n",
       "      <td>-0.048521800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_8</th>\n",
       "      <td>-0.234090714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_9</th>\n",
       "      <td>-0.235944588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_10</th>\n",
       "      <td>-0.266025004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_11</th>\n",
       "      <td>0.047185901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_12</th>\n",
       "      <td>0.004135718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_13</th>\n",
       "      <td>-0.218515455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_14</th>\n",
       "      <td>-0.125469372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_15</th>\n",
       "      <td>-0.096498183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_16</th>\n",
       "      <td>-0.254843643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_17</th>\n",
       "      <td>-0.107779217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_18</th>\n",
       "      <td>-0.242773074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_19</th>\n",
       "      <td>-0.176589855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_20</th>\n",
       "      <td>-0.175658410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_21</th>\n",
       "      <td>-0.170341263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_22</th>\n",
       "      <td>-0.049725619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_23</th>\n",
       "      <td>-0.218497284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_24</th>\n",
       "      <td>-0.054501878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_25</th>\n",
       "      <td>0.029040814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_26</th>\n",
       "      <td>-0.264841837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_27</th>\n",
       "      <td>-0.151809608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_28</th>\n",
       "      <td>-0.289949715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_29</th>\n",
       "      <td>-0.149356561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_30</th>\n",
       "      <td>-0.034243604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_31</th>\n",
       "      <td>-0.123447535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_32</th>\n",
       "      <td>-0.025869108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_33</th>\n",
       "      <td>-0.170037278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_34</th>\n",
       "      <td>-0.203109802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_35</th>\n",
       "      <td>-0.270595015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_36</th>\n",
       "      <td>-0.007182002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_37</th>\n",
       "      <td>-0.471035622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_38</th>\n",
       "      <td>-0.107576822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_39</th>\n",
       "      <td>-0.267305194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_40</th>\n",
       "      <td>-0.199749155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_41</th>\n",
       "      <td>-0.082743980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_42</th>\n",
       "      <td>-0.155490446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_43</th>\n",
       "      <td>-0.319407364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_44</th>\n",
       "      <td>-0.294404965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_45</th>\n",
       "      <td>-0.479703696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_46</th>\n",
       "      <td>-0.268220630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_47</th>\n",
       "      <td>-0.315634992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_48</th>\n",
       "      <td>-0.270282449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_49</th>\n",
       "      <td>-0.336996760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_50</th>\n",
       "      <td>-0.085857073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_51</th>\n",
       "      <td>-0.041463052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_52</th>\n",
       "      <td>-0.082305744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week of Year_53</th>\n",
       "      <td>-0.140491858</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Coefficient\n",
       "Week of Year_2   0.081178409\n",
       "Week of Year_3  -0.211048541\n",
       "Week of Year_4  -0.207719167\n",
       "Week of Year_5   0.015616153\n",
       "Week of Year_6  -0.049868534\n",
       "Week of Year_7  -0.048521800\n",
       "Week of Year_8  -0.234090714\n",
       "Week of Year_9  -0.235944588\n",
       "Week of Year_10 -0.266025004\n",
       "Week of Year_11  0.047185901\n",
       "Week of Year_12  0.004135718\n",
       "Week of Year_13 -0.218515455\n",
       "Week of Year_14 -0.125469372\n",
       "Week of Year_15 -0.096498183\n",
       "Week of Year_16 -0.254843643\n",
       "Week of Year_17 -0.107779217\n",
       "Week of Year_18 -0.242773074\n",
       "Week of Year_19 -0.176589855\n",
       "Week of Year_20 -0.175658410\n",
       "Week of Year_21 -0.170341263\n",
       "Week of Year_22 -0.049725619\n",
       "Week of Year_23 -0.218497284\n",
       "Week of Year_24 -0.054501878\n",
       "Week of Year_25  0.029040814\n",
       "Week of Year_26 -0.264841837\n",
       "Week of Year_27 -0.151809608\n",
       "Week of Year_28 -0.289949715\n",
       "Week of Year_29 -0.149356561\n",
       "Week of Year_30 -0.034243604\n",
       "Week of Year_31 -0.123447535\n",
       "Week of Year_32 -0.025869108\n",
       "Week of Year_33 -0.170037278\n",
       "Week of Year_34 -0.203109802\n",
       "Week of Year_35 -0.270595015\n",
       "Week of Year_36 -0.007182002\n",
       "Week of Year_37 -0.471035622\n",
       "Week of Year_38 -0.107576822\n",
       "Week of Year_39 -0.267305194\n",
       "Week of Year_40 -0.199749155\n",
       "Week of Year_41 -0.082743980\n",
       "Week of Year_42 -0.155490446\n",
       "Week of Year_43 -0.319407364\n",
       "Week of Year_44 -0.294404965\n",
       "Week of Year_45 -0.479703696\n",
       "Week of Year_46 -0.268220630\n",
       "Week of Year_47 -0.315634992\n",
       "Week of Year_48 -0.270282449\n",
       "Week of Year_49 -0.336996760\n",
       "Week of Year_50 -0.085857073\n",
       "Week of Year_51 -0.041463052\n",
       "Week of Year_52 -0.082305744\n",
       "Week of Year_53 -0.140491858"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Construct a dataframe of the model calendar-effect coefficients and review the output:\n",
    "results = pd.DataFrame(model.coef_, columns=[\"Coefficient\"], index=X_binary_encoded.columns)\n",
    "\n",
    "# Filter results so that it only includes either the day-of-week or week-of-year effect \n",
    "results = (results[results.index.str.contains(\"Week of Year\")])\n",
    "\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.04713727,  0.08117841, -0.21104854, -0.20771917,  0.01561615,\n",
       "       -0.04986853, -0.0485218 , -0.23409071, -0.23594459, -0.266025  ,\n",
       "        0.0471859 ,  0.00413572, -0.21851545, -0.12546937, -0.09649818,\n",
       "       -0.25484364, -0.10777922, -0.24277307, -0.17658985, -0.17565841,\n",
       "       -0.17034126, -0.04972562, -0.21849728, -0.05450188,  0.02904081,\n",
       "       -0.26484184, -0.15180961, -0.28994972, -0.14935656, -0.0342436 ,\n",
       "       -0.12344754, -0.02586911, -0.17003728, -0.2031098 , -0.27059501,\n",
       "       -0.007182  , -0.47103562, -0.10757682, -0.26730519, -0.19974915,\n",
       "       -0.08274398, -0.15549045, -0.31940736, -0.29440496, -0.4797037 ,\n",
       "       -0.26822063, -0.31563499, -0.27028245, -0.33699676, -0.08585707,\n",
       "       -0.04146305, -0.08230574, -0.14049186])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fefa45fe6d8>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results of the individual seasonal effects using pandas df.plot()\n",
    "results.plot(figsize=(12,5), kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Return Forecasting: Autocorrelation and the ARMA Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Using futures Settle *Returns*, estimate an ARMA model.\n",
    " 1. Set the AR and MA (\"p\" and \"q\") parameters to p=2 and q=1.\n",
    " 2. Plot autocorrelation and partial correlation graphs with an alpha of 0.10 and lags=30. If, when looking at the correlation graphs, additional lags seem important for predicting returns, increase the number of lags in the ARMA model from (1).\n",
    " 3. Does increasing the number of \"p\" or \"q\" increase the ability of the model? Compare your results between the two models by comparing the Bayseian Information Criterion, or BIC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statsmodels.tsa.arima_model.ARMA at 0x7fefa48c4668>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "# Create a series using \"Settle\" price percentage returns, and drop any nan's:\n",
    "df = fut[[\"Settle\"]].pct_change().dropna()\n",
    "\n",
    "# Estimate and ARMA model using statsmodels\n",
    "# Hint: ARMA(df, order=(p, q)).fit()\n",
    "from statsmodels.tsa.arima_model import ARMA \n",
    "autoregressive_count = 2\n",
    "moving_average_window = 1\n",
    "model = ARMA(df.values, order=(autoregressive_count,moving_average_window))\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>y</td>        <th>  No. Observations:  </th>    <td>1410</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARMA(2, 1)</td>    <th>  Log Likelihood     </th>  <td>5347.586</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>    <td>0.005</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 09 Oct 2019</td> <th>  AIC                </th> <td>-10685.172</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>22:14:58</td>     <th>  BIC                </th> <td>-10658.915</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                <td>0</td>        <th>  HQIC               </th> <td>-10675.360</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                       <td> </td>        <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>   <td>-1.771e-05</td> <td>    0.000</td> <td>   -0.116</td> <td> 0.908</td> <td>   -0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.y</th> <td>    0.2980</td> <td>    0.339</td> <td>    0.880</td> <td> 0.379</td> <td>   -0.366</td> <td>    0.962</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2.y</th> <td>    0.0464</td> <td>    0.027</td> <td>    1.746</td> <td> 0.081</td> <td>   -0.006</td> <td>    0.099</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.y</th> <td>   -0.3117</td> <td>    0.338</td> <td>   -0.921</td> <td> 0.357</td> <td>   -0.975</td> <td>    0.352</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th> <td>           2.4334</td> <td>          +0.0000j</td> <td>           2.4334</td> <td>           0.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.2</th> <td>          -8.8531</td> <td>          +0.0000j</td> <td>           8.8531</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th> <td>           3.2082</td> <td>          +0.0000j</td> <td>           3.2082</td> <td>           0.0000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              ARMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                 1410\n",
       "Model:                     ARMA(2, 1)   Log Likelihood                5347.586\n",
       "Method:                       css-mle   S.D. of innovations              0.005\n",
       "Date:                Wed, 09 Oct 2019   AIC                         -10685.172\n",
       "Time:                        22:14:58   BIC                         -10658.915\n",
       "Sample:                             0   HQIC                        -10675.360\n",
       "                                                                              \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const      -1.771e-05      0.000     -0.116      0.908      -0.000       0.000\n",
       "ar.L1.y        0.2980      0.339      0.880      0.379      -0.366       0.962\n",
       "ar.L2.y        0.0464      0.027      1.746      0.081      -0.006       0.099\n",
       "ma.L1.y       -0.3117      0.338     -0.921      0.357      -0.975       0.352\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1            2.4334           +0.0000j            2.4334            0.0000\n",
       "AR.2           -8.8531           +0.0000j            8.8531            0.5000\n",
       "MA.1            3.2082           +0.0000j            3.2082            0.0000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Output model summary results:\n",
    "result= model.fit()\n",
    "result.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot autocorrelation and partial correlation graphs with an alpha of 0.10 and lags=30.\n",
    "\n",
    "# Autocorrelation (For detecting # of MA (\"P\") Lags)\n",
    "# YOUR CODE HERE \n",
    "\n",
    "# Partial Autocorrelation (For detecting # of MA (\"P\") Lags)\n",
    "# YOUR CODE HERE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/cyb/anaconda3/lib/python3.6/site-packages/statsmodels/base/model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>Settle</td>      <th>  No. Observations:  </th>    <td>1345</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARMA(17, 6)</td>   <th>  Log Likelihood     </th>  <td>5104.300</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>    <td>0.005</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Sun, 07 Jul 2019</td> <th>  AIC                </th> <td>-10158.601</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>22:05:34</td>     <th>  BIC                </th> <td>-10028.497</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                <td>0</td>        <th>  HQIC               </th> <td>-10109.868</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                       <td> </td>        <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>         <td>-2.498e-05</td> <td>    0.000</td> <td>   -0.159</td> <td> 0.874</td> <td>   -0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.Settle</th>  <td>   -0.0092</td> <td>    1.166</td> <td>   -0.008</td> <td> 0.994</td> <td>   -2.294</td> <td>    2.275</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2.Settle</th>  <td>    0.1173</td> <td>    0.761</td> <td>    0.154</td> <td> 0.877</td> <td>   -1.373</td> <td>    1.608</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L3.Settle</th>  <td>   -0.1401</td> <td>    0.348</td> <td>   -0.403</td> <td> 0.687</td> <td>   -0.821</td> <td>    0.541</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L4.Settle</th>  <td>   -0.4151</td> <td>    0.430</td> <td>   -0.965</td> <td> 0.335</td> <td>   -1.258</td> <td>    0.428</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L5.Settle</th>  <td>   -0.4680</td> <td>    0.706</td> <td>   -0.663</td> <td> 0.508</td> <td>   -1.852</td> <td>    0.916</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L6.Settle</th>  <td>    0.5746</td> <td>    1.001</td> <td>    0.574</td> <td> 0.566</td> <td>   -1.387</td> <td>    2.536</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L7.Settle</th>  <td>    0.0406</td> <td>    0.063</td> <td>    0.646</td> <td> 0.518</td> <td>   -0.083</td> <td>    0.164</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L8.Settle</th>  <td>   -0.0099</td> <td>    0.085</td> <td>   -0.117</td> <td> 0.907</td> <td>   -0.176</td> <td>    0.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L9.Settle</th>  <td>   -0.0858</td> <td>    0.053</td> <td>   -1.612</td> <td> 0.107</td> <td>   -0.190</td> <td>    0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L10.Settle</th> <td>   -0.0198</td> <td>    0.084</td> <td>   -0.235</td> <td> 0.815</td> <td>   -0.185</td> <td>    0.146</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L11.Settle</th> <td>    0.1056</td> <td>    0.081</td> <td>    1.312</td> <td> 0.190</td> <td>   -0.052</td> <td>    0.263</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L12.Settle</th> <td>    0.0032</td> <td>    0.091</td> <td>    0.035</td> <td> 0.972</td> <td>   -0.174</td> <td>    0.181</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L13.Settle</th> <td>   -0.0596</td> <td>    0.056</td> <td>   -1.074</td> <td> 0.283</td> <td>   -0.168</td> <td>    0.049</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L14.Settle</th> <td>   -0.0379</td> <td>    0.067</td> <td>   -0.562</td> <td> 0.574</td> <td>   -0.170</td> <td>    0.094</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L15.Settle</th> <td>    0.0529</td> <td>    0.085</td> <td>    0.621</td> <td> 0.534</td> <td>   -0.114</td> <td>    0.220</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L16.Settle</th> <td>    0.0244</td> <td>    0.041</td> <td>    0.600</td> <td> 0.549</td> <td>   -0.055</td> <td>    0.104</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L17.Settle</th> <td>   -0.0063</td> <td>    0.050</td> <td>   -0.126</td> <td> 0.900</td> <td>   -0.104</td> <td>    0.092</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.Settle</th>  <td>   -0.0015</td> <td>    1.166</td> <td>   -0.001</td> <td> 0.999</td> <td>   -2.287</td> <td>    2.284</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2.Settle</th>  <td>   -0.0690</td> <td>    0.750</td> <td>   -0.092</td> <td> 0.927</td> <td>   -1.538</td> <td>    1.400</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L3.Settle</th>  <td>    0.1817</td> <td>    0.389</td> <td>    0.467</td> <td> 0.641</td> <td>   -0.581</td> <td>    0.945</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L4.Settle</th>  <td>    0.3985</td> <td>    0.499</td> <td>    0.798</td> <td> 0.425</td> <td>   -0.580</td> <td>    1.377</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L5.Settle</th>  <td>    0.4197</td> <td>    0.736</td> <td>    0.570</td> <td> 0.569</td> <td>   -1.024</td> <td>    1.863</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L6.Settle</th>  <td>   -0.5153</td> <td>    0.963</td> <td>   -0.535</td> <td> 0.593</td> <td>   -2.403</td> <td>    1.373</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>    <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th>  <td>           0.6838</td> <td>          -0.7725j</td> <td>           1.0317</td> <td>          -0.1347</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.2</th>  <td>           0.6838</td> <td>          +0.7725j</td> <td>           1.0317</td> <td>           0.1347</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.3</th>  <td>           1.2914</td> <td>          -0.0000j</td> <td>           1.2914</td> <td>          -0.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.4</th>  <td>           1.2093</td> <td>          -0.5319j</td> <td>           1.3211</td> <td>          -0.0659</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.5</th>  <td>           1.2093</td> <td>          +0.5319j</td> <td>           1.3211</td> <td>           0.0659</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.6</th>  <td>           0.6457</td> <td>          -1.0403j</td> <td>           1.2244</td> <td>          -0.1616</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.7</th>  <td>           0.6457</td> <td>          +1.0403j</td> <td>           1.2244</td> <td>           0.1616</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.8</th>  <td>          -0.2092</td> <td>          -1.2216j</td> <td>           1.2394</td> <td>          -0.2770</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.9</th>  <td>          -0.2092</td> <td>          +1.2216j</td> <td>           1.2394</td> <td>           0.2770</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.10</th> <td>          -0.5388</td> <td>          -0.8910j</td> <td>           1.0412</td> <td>          -0.3366</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.11</th> <td>          -0.5388</td> <td>          +0.8910j</td> <td>           1.0412</td> <td>           0.3366</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.12</th> <td>          -1.0470</td> <td>          -0.0000j</td> <td>           1.0470</td> <td>          -0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.13</th> <td>          -0.9907</td> <td>          -0.9793j</td> <td>           1.3930</td> <td>          -0.3759</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.14</th> <td>          -0.9907</td> <td>          +0.9793j</td> <td>           1.3930</td> <td>           0.3759</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.15</th> <td>          -1.4923</td> <td>          -0.0000j</td> <td>           1.4923</td> <td>          -0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.16</th> <td>          -1.6789</td> <td>          -0.0000j</td> <td>           1.6789</td> <td>          -0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.17</th> <td>           5.1913</td> <td>          -0.0000j</td> <td>           5.1913</td> <td>          -0.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th>  <td>          -1.0450</td> <td>          -0.0000j</td> <td>           1.0450</td> <td>          -0.5000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.2</th>  <td>          -0.5318</td> <td>          -0.9157j</td> <td>           1.0590</td> <td>          -0.3337</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.3</th>  <td>          -0.5318</td> <td>          +0.9157j</td> <td>           1.0590</td> <td>           0.3337</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.4</th>  <td>           0.6647</td> <td>          -0.7728j</td> <td>           1.0193</td> <td>          -0.1369</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.5</th>  <td>           0.6647</td> <td>          +0.7728j</td> <td>           1.0193</td> <td>           0.1369</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.6</th>  <td>           1.5937</td> <td>          -0.0000j</td> <td>           1.5937</td> <td>          -0.0000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              ARMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                 Settle   No. Observations:                 1345\n",
       "Model:                    ARMA(17, 6)   Log Likelihood                5104.300\n",
       "Method:                       css-mle   S.D. of innovations              0.005\n",
       "Date:                Sun, 07 Jul 2019   AIC                         -10158.601\n",
       "Time:                        22:05:34   BIC                         -10028.497\n",
       "Sample:                             0   HQIC                        -10109.868\n",
       "                                                                              \n",
       "=================================================================================\n",
       "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------\n",
       "const         -2.498e-05      0.000     -0.159      0.874      -0.000       0.000\n",
       "ar.L1.Settle     -0.0092      1.166     -0.008      0.994      -2.294       2.275\n",
       "ar.L2.Settle      0.1173      0.761      0.154      0.877      -1.373       1.608\n",
       "ar.L3.Settle     -0.1401      0.348     -0.403      0.687      -0.821       0.541\n",
       "ar.L4.Settle     -0.4151      0.430     -0.965      0.335      -1.258       0.428\n",
       "ar.L5.Settle     -0.4680      0.706     -0.663      0.508      -1.852       0.916\n",
       "ar.L6.Settle      0.5746      1.001      0.574      0.566      -1.387       2.536\n",
       "ar.L7.Settle      0.0406      0.063      0.646      0.518      -0.083       0.164\n",
       "ar.L8.Settle     -0.0099      0.085     -0.117      0.907      -0.176       0.156\n",
       "ar.L9.Settle     -0.0858      0.053     -1.612      0.107      -0.190       0.019\n",
       "ar.L10.Settle    -0.0198      0.084     -0.235      0.815      -0.185       0.146\n",
       "ar.L11.Settle     0.1056      0.081      1.312      0.190      -0.052       0.263\n",
       "ar.L12.Settle     0.0032      0.091      0.035      0.972      -0.174       0.181\n",
       "ar.L13.Settle    -0.0596      0.056     -1.074      0.283      -0.168       0.049\n",
       "ar.L14.Settle    -0.0379      0.067     -0.562      0.574      -0.170       0.094\n",
       "ar.L15.Settle     0.0529      0.085      0.621      0.534      -0.114       0.220\n",
       "ar.L16.Settle     0.0244      0.041      0.600      0.549      -0.055       0.104\n",
       "ar.L17.Settle    -0.0063      0.050     -0.126      0.900      -0.104       0.092\n",
       "ma.L1.Settle     -0.0015      1.166     -0.001      0.999      -2.287       2.284\n",
       "ma.L2.Settle     -0.0690      0.750     -0.092      0.927      -1.538       1.400\n",
       "ma.L3.Settle      0.1817      0.389      0.467      0.641      -0.581       0.945\n",
       "ma.L4.Settle      0.3985      0.499      0.798      0.425      -0.580       1.377\n",
       "ma.L5.Settle      0.4197      0.736      0.570      0.569      -1.024       1.863\n",
       "ma.L6.Settle     -0.5153      0.963     -0.535      0.593      -2.403       1.373\n",
       "                                    Roots                                     \n",
       "==============================================================================\n",
       "                   Real          Imaginary           Modulus         Frequency\n",
       "------------------------------------------------------------------------------\n",
       "AR.1             0.6838           -0.7725j            1.0317           -0.1347\n",
       "AR.2             0.6838           +0.7725j            1.0317            0.1347\n",
       "AR.3             1.2914           -0.0000j            1.2914           -0.0000\n",
       "AR.4             1.2093           -0.5319j            1.3211           -0.0659\n",
       "AR.5             1.2093           +0.5319j            1.3211            0.0659\n",
       "AR.6             0.6457           -1.0403j            1.2244           -0.1616\n",
       "AR.7             0.6457           +1.0403j            1.2244            0.1616\n",
       "AR.8            -0.2092           -1.2216j            1.2394           -0.2770\n",
       "AR.9            -0.2092           +1.2216j            1.2394            0.2770\n",
       "AR.10           -0.5388           -0.8910j            1.0412           -0.3366\n",
       "AR.11           -0.5388           +0.8910j            1.0412            0.3366\n",
       "AR.12           -1.0470           -0.0000j            1.0470           -0.5000\n",
       "AR.13           -0.9907           -0.9793j            1.3930           -0.3759\n",
       "AR.14           -0.9907           +0.9793j            1.3930            0.3759\n",
       "AR.15           -1.4923           -0.0000j            1.4923           -0.5000\n",
       "AR.16           -1.6789           -0.0000j            1.6789           -0.5000\n",
       "AR.17            5.1913           -0.0000j            5.1913           -0.0000\n",
       "MA.1            -1.0450           -0.0000j            1.0450           -0.5000\n",
       "MA.2            -0.5318           -0.9157j            1.0590           -0.3337\n",
       "MA.3            -0.5318           +0.9157j            1.0590            0.3337\n",
       "MA.4             0.6647           -0.7728j            1.0193           -0.1369\n",
       "MA.5             0.6647           +0.7728j            1.0193            0.1369\n",
       "MA.6             1.5937           -0.0000j            1.5937           -0.0000\n",
       "------------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoregressive_count = 17\n",
    "moving_average_window = 6\n",
    "\n",
    "# Esimate your model based on your interpretation of the autocorrelation/partial autocorrelation plots:\n",
    "model = # YOUR CODE HERE \n",
    "\n",
    "# Review model summary results:\n",
    "# YOUR CODE HERE "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Return Forecasting: Deploying the ARIMA Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 1. Using the *raw* Yen **Settle Price**, estimate an ARIMA model.\n",
    "     1. Set P=5, D=1, and Q=1 in the model (e.g., ARIMA(df, order=(5,1,1))\n",
    "     2. P= # of Auto-Regressive Lags, D= # of Differences (this is usually =1), Q= # of Moving Average Lags\n",
    "\n",
    " 2. Plot the residuals from the model. If residuals are the part of returns that our model *doesn\"t* explain, does it seem like our model has missed any seasonality?\n",
    " 3. Construct a 15 day (3 week) forecast for the Settle Price. What does the model forecast will happen to the Japanese Yen in the near term?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Settle</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-02-18</th>\n",
       "      <td>9775.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-19</th>\n",
       "      <td>9773.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-20</th>\n",
       "      <td>9775.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-21</th>\n",
       "      <td>9755.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-24</th>\n",
       "      <td>9757.000000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                   Settle\n",
       "Date                     \n",
       "2014-02-18 9775.000000000\n",
       "2014-02-19 9773.000000000\n",
       "2014-02-20 9775.000000000\n",
       "2014-02-21 9755.000000000\n",
       "2014-02-24 9757.000000000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For ARIMA, construct a dataframe using just the futures \"Settle\" prices:\n",
    "df = # YOUR CODE HERE \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             ARIMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:               D.Settle   No. Observations:                 1345\n",
      "Model:                 ARIMA(5, 1, 1)   Log Likelihood               -7153.142\n",
      "Method:                       css-mle   S.D. of innovations             49.371\n",
      "Date:                Sun, 07 Jul 2019   AIC                          14322.283\n",
      "Time:                        22:06:39   BIC                          14363.916\n",
      "Sample:                             1   HQIC                         14337.878\n",
      "                                                                              \n",
      "==================================================================================\n",
      "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "const             -0.3845      1.385     -0.278      0.781      -3.098       2.329\n",
      "ar.L1.D.Settle    -0.6682      0.288     -2.323      0.020      -1.232      -0.104\n",
      "ar.L2.D.Settle     0.0407      0.033      1.237      0.216      -0.024       0.105\n",
      "ar.L3.D.Settle     0.0678      0.035      1.943      0.052      -0.001       0.136\n",
      "ar.L4.D.Settle     0.0064      0.034      0.185      0.853      -0.061       0.074\n",
      "ar.L5.D.Settle    -0.0621      0.030     -2.079      0.038      -0.121      -0.004\n",
      "ma.L1.D.Settle     0.6615      0.288      2.297      0.022       0.097       1.226\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                  Real          Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "AR.1           -1.2721           -0.0000j            1.2721           -0.5000\n",
      "AR.2           -0.9839           -1.4121j            1.7211           -0.3469\n",
      "AR.3           -0.9839           +1.4121j            1.7211            0.3469\n",
      "AR.4            1.6711           -1.2166j            2.0670           -0.1002\n",
      "AR.5            1.6711           +1.2166j            2.0670            0.1002\n",
      "MA.1           -1.5117           +0.0000j            1.5117            0.5000\n",
      "-----------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   0\n",
      "count 1345.000000000\n",
      "mean     0.001503358\n",
      "std     49.389742669\n",
      "min   -233.607904105\n",
      "25%    -27.690635374\n",
      "50%     -0.403362484\n",
      "75%     25.313943148\n",
      "max    325.375471649\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "# Estimate and ARIMA Model:\n",
    "# Hint: ARIMA(df, order=(p, d, q))\n",
    "model = # YOUR CODE HERE \n",
    "model_fit = # YOUR CODE HERE \n",
    "# Output model summary results:\n",
    "print(model_fit.summary())\n",
    "# plot residual errors:\n",
    "residuals = # YOUR CODE HERE \n",
    "residuals.plot(title=\"Noise (Model Residuals)\")\n",
    "plt.show()\n",
    "print(residuals.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8d00114320>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Construct a 15 day (3 week) forecast for the Settle Price. \n",
    "# What does the model forecast will happen to the Japanese Yen in the near term?\n",
    "# Specifically, construct a pandas DataFrame containing ARIMA model forecasts for a 15-day window:\n",
    "# YOUR CODE HERE "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Volatility Forecasting\n",
    "\n",
    " Rather than predicting returns, let's forecast near-term **volatility** of Japanese Yen futures returns. Being able to accurately predict volatility will be extremely useful if we want to trade in derivatives or quantify our maximum loss."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Volatility Forecasting: GARCH and EGARCH Models\n",
    "Using the \"arch\" package, estimate either GARCH(1,1) or an EGARCH(p,q) volatility forecast model on futures settle returns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arch as arch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Settle</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-06-28</th>\n",
       "      <td>-0.032140561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>-0.648376380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-02</th>\n",
       "      <td>0.587886306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-03</th>\n",
       "      <td>-0.010723861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-05</th>\n",
       "      <td>-0.702488202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Settle\n",
       "Date                   \n",
       "2019-06-28 -0.032140561\n",
       "2019-07-01 -0.648376380\n",
       "2019-07-02  0.587886306\n",
       "2019-07-03 -0.010723861\n",
       "2019-07-05 -0.702488202"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a series using \"Settle\" price percentage returns, drop any nan\"s, and check the results:\n",
    "# (Make sure to multiply the pct_change() results by *100)\n",
    "# In this case, you may have to replace inf, -inf values with np.nan\"s\n",
    "returns=(fut[[\"Settle\"]].pct_change()*100)\n",
    "returns=returns.replace(-np.inf, np.nan).dropna()\n",
    "returns.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                           AR - GARCH Model Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                 Settle   R-squared:                       0.001\n",
       "Mean Model:                        AR   Adj. R-squared:                 -0.000\n",
       "Vol Model:                      GARCH   Log-Likelihood:               -1025.22\n",
       "Distribution:                  Normal   AIC:                           2062.44\n",
       "Method:            Maximum Likelihood   BIC:                           2093.66\n",
       "                                        No. Observations:                 1343\n",
       "Date:                Sun, Jul 07 2019   Df Residuals:                     1337\n",
       "Time:                        22:06:40   Df Model:                            6\n",
       "                                  Mean Model                                  \n",
       "==============================================================================\n",
       "                  coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "------------------------------------------------------------------------------\n",
       "Const      -4.5850e-03  1.273e-02     -0.360      0.719 [-2.954e-02,2.037e-02]\n",
       "Settle[1]  -4.1009e-03  2.942e-02     -0.139      0.889 [-6.177e-02,5.357e-02]\n",
       "Settle[2]       0.0478  2.914e-02      1.641      0.101   [-9.303e-03,  0.105]\n",
       "                               Volatility Model                              \n",
       "=============================================================================\n",
       "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "-----------------------------------------------------------------------------\n",
       "omega      2.2531e-03  1.627e-03      1.385      0.166 [-9.349e-04,5.441e-03]\n",
       "alpha[1]       0.0421  1.532e-02      2.748  5.998e-03  [1.207e-02,7.211e-02]\n",
       "beta[1]        0.9515  1.721e-02     55.289      0.000      [  0.918,  0.985]\n",
       "=============================================================================\n",
       "\n",
       "Covariance estimator: robust\n",
       "ARCHModelResult, id: 0x7f8d71e47da0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Estimate a GARCH(1,1) model using the \"arch\" package:\n",
    "# (We need to drop all NaN\"s first in order to get this to run):\n",
    "am = # YOUR CODE HERE \n",
    "res = am.fit(# YOUR CODE HERE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           AR - GARCH Model Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                 Settle   R-squared:                       0.001\n",
      "Mean Model:                        AR   Adj. R-squared:                 -0.000\n",
      "Vol Model:                      GARCH   Log-Likelihood:               -1025.22\n",
      "Distribution:                  Normal   AIC:                           2062.44\n",
      "Method:            Maximum Likelihood   BIC:                           2093.66\n",
      "                                        No. Observations:                 1343\n",
      "Date:                Sun, Jul 07 2019   Df Residuals:                     1337\n",
      "Time:                        22:06:40   Df Model:                            6\n",
      "                                  Mean Model                                  \n",
      "==============================================================================\n",
      "                  coef    std err          t      P>|t|       95.0% Conf. Int.\n",
      "------------------------------------------------------------------------------\n",
      "Const      -4.5850e-03  1.273e-02     -0.360      0.719 [-2.954e-02,2.037e-02]\n",
      "Settle[1]  -4.1009e-03  2.942e-02     -0.139      0.889 [-6.177e-02,5.357e-02]\n",
      "Settle[2]       0.0478  2.914e-02      1.641      0.101   [-9.303e-03,  0.105]\n",
      "                               Volatility Model                              \n",
      "=============================================================================\n",
      "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
      "-----------------------------------------------------------------------------\n",
      "omega      2.2531e-03  1.627e-03      1.385      0.166 [-9.349e-04,5.441e-03]\n",
      "alpha[1]       0.0421  1.532e-02      2.748  5.998e-03  [1.207e-02,7.211e-02]\n",
      "beta[1]        0.9515  1.721e-02     55.289      0.000      [  0.918,  0.985]\n",
      "=============================================================================\n",
      "\n",
      "Covariance estimator: robust\n"
     ]
    }
   ],
   "source": [
    "# Output the summary results of the GARCH model:\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Volatility Forecasting: Identifying Patterns of Clustered Volatility\n",
    "\n",
    "GARCH/EGARCH, etc. models are good for capturing the \"volatility clustering\" effect that is common in financial data. After estimating your model, can you identify periods when volatility clusters together?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the model\"s conditional volatility for the Yen:\n",
    "# Hint: use .plot(annualize=\"D\")\n",
    "fig = # YOUR CODE HERE "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Volatility Forecasting: 3-day Yen Volatility Forecasts using GARCH/EGARCH\n",
    "  * Let's do some forecasting of near-term risk. Specifically, use the .forecast() method to calculate 3-day forecasts. Then plot the resulting 3-day annualized variance. Is Yen volatility expected to rise or fall in the near term?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<arch.univariate.base.ARCHModelForecast at 0x7f8d504ab3c8>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note--You can either specify today\"s date manually, or use pd.datetime.today().strftime(\"%Y-%m-%d\") \n",
    "# (example format: start=\"2019-06-28\")\n",
    "# If you have problems with datetime, \n",
    "# specify data date manually using the most recent trading date in the dataframe\n",
    "todays_date = pd.datetime.today().strftime(\"%Y-%m-%d\")                  \n",
    "# todays_date = '2019-07-05'\n",
    "# Forecast volatility, from today\"s date until the following three days:\n",
    "forecast_horizon = # YOUR CODE HERE \n",
    "forecasts = # YOUR CODE HERE \n",
    "forecasts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ce0eb5b38>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the annualized volatility forecast using pd.plot():\n",
    "# (Make sure to use dropna() on the forecasts before plotting):\n",
    "# (There are 252 trading days in a year)\n",
    "# Note: each column in forecasts.variance represents one day ahead: \n",
    "plot_title = f\"Forecast Volatility over the next {forecast_horizon} days\"\n",
    "model_forecasts = # YOUR CODE HERE \n",
    "model_forecasts.dropna().T.plot(title=plot_title) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = # YOUR CODE HERE "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Out-of-Sample Predictions\n",
    " So far, you\"ve used the full dataset to construct a model. Let's try a more rigorous technique."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Out-of-Sample Predictions: Slicing the Data into Training and Test Set (\"One Shot\" Approach)\n",
    "\n",
    "Using the SKLearn regression model and just lagged returns as the independent variable, start by estimating a model using **training data** from January, 2015 through December 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>Futures Return</th>\n",
       "      <th>Lagged Return</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>2014-03-04</th>\n",
       "      <td>-0.801298306</td>\n",
       "      <td>0.346055980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-06</th>\n",
       "      <td>-0.766871166</td>\n",
       "      <td>-0.801298306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-07</th>\n",
       "      <td>-0.247295209</td>\n",
       "      <td>-0.766871166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-10</th>\n",
       "      <td>0.103295114</td>\n",
       "      <td>-0.247295209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-11</th>\n",
       "      <td>0.278609019</td>\n",
       "      <td>0.103295114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Futures Return  Lagged Return\n",
       "Date                                     \n",
       "2014-03-04    -0.801298306    0.346055980\n",
       "2014-03-06    -0.766871166   -0.801298306\n",
       "2014-03-07    -0.247295209   -0.766871166\n",
       "2014-03-10     0.103295114   -0.247295209\n",
       "2014-03-11     0.278609019    0.103295114"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If you haven\"t already, create and add \"Futures Return\" and \"Lagged Futures Return\" variables to your dataframe:\n",
    "# Replace inf, -inf with np.nan, then drop any row with at least one np.nan in the dataframe\n",
    "fut[\"Futures Return\"] = # YOUR CODE HERE \n",
    "fut[\"Lagged Return\"] = # YOUR CODE HERE \n",
    "fut = fut.replace(-np.inf, np.nan).dropna()\n",
    "fut[[\"Futures Return\", \"Lagged Return\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set covers from  2015-01-02 00:00:00  to  2017-12-29 00:00:00\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Change</th>\n",
       "      <th>Settle</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Previous Day Open Interest</th>\n",
       "      <th>Futures Return</th>\n",
       "      <th>Lagged Return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>8352.000000000</td>\n",
       "      <td>8353.000000000</td>\n",
       "      <td>8286.000000000</td>\n",
       "      <td>8310.000000000</td>\n",
       "      <td>34.000000000</td>\n",
       "      <td>8315.000000000</td>\n",
       "      <td>101438.000000000</td>\n",
       "      <td>222022.000000000</td>\n",
       "      <td>-0.407234399</td>\n",
       "      <td>-0.262812089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>8305.000000000</td>\n",
       "      <td>8382.000000000</td>\n",
       "      <td>8293.000000000</td>\n",
       "      <td>8371.000000000</td>\n",
       "      <td>56.000000000</td>\n",
       "      <td>8371.000000000</td>\n",
       "      <td>165487.000000000</td>\n",
       "      <td>226085.000000000</td>\n",
       "      <td>0.673481660</td>\n",
       "      <td>-0.407234399</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Open           High            Low           Last  \\\n",
       "Date                                                                     \n",
       "2015-01-02 8352.000000000 8353.000000000 8286.000000000 8310.000000000   \n",
       "2015-01-05 8305.000000000 8382.000000000 8293.000000000 8371.000000000   \n",
       "\n",
       "                 Change         Settle           Volume  \\\n",
       "Date                                                      \n",
       "2015-01-02 34.000000000 8315.000000000 101438.000000000   \n",
       "2015-01-05 56.000000000 8371.000000000 165487.000000000   \n",
       "\n",
       "            Previous Day Open Interest  Futures Return  Lagged Return  \n",
       "Date                                                                   \n",
       "2015-01-02            222022.000000000    -0.407234399   -0.262812089  \n",
       "2015-01-05            226085.000000000     0.673481660   -0.407234399  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using datetime index slicing, create a \"training\" data dataframe using data from just 2015-01-01 to 2017-12-31\n",
    "train = # YOUR CODE HERE \n",
    "print(\"Training set covers from \", train.index.min(), \" to \", train.index.max())\n",
    "train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set covers from  2018-01-02 00:00:00  to  2019-07-05 00:00:00\n"
     ]
    },
    {
     "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Change</th>\n",
       "      <th>Settle</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Previous Day Open Interest</th>\n",
       "      <th>Futures Return</th>\n",
       "      <th>Lagged Return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>8909.500000000</td>\n",
       "      <td>8957.500000000</td>\n",
       "      <td>8898.500000000</td>\n",
       "      <td>8938.000000000</td>\n",
       "      <td>26.500000000</td>\n",
       "      <td>8940.500000000</td>\n",
       "      <td>96714.000000000</td>\n",
       "      <td>227884.000000000</td>\n",
       "      <td>0.297285169</td>\n",
       "      <td>0.224870699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>8943.000000000</td>\n",
       "      <td>8947.500000000</td>\n",
       "      <td>8913.000000000</td>\n",
       "      <td>8921.000000000</td>\n",
       "      <td>21.500000000</td>\n",
       "      <td>8919.000000000</td>\n",
       "      <td>93498.000000000</td>\n",
       "      <td>226582.000000000</td>\n",
       "      <td>-0.240478720</td>\n",
       "      <td>0.297285169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Open           High            Low           Last  \\\n",
       "Date                                                                     \n",
       "2018-01-02 8909.500000000 8957.500000000 8898.500000000 8938.000000000   \n",
       "2018-01-03 8943.000000000 8947.500000000 8913.000000000 8921.000000000   \n",
       "\n",
       "                 Change         Settle          Volume  \\\n",
       "Date                                                     \n",
       "2018-01-02 26.500000000 8940.500000000 96714.000000000   \n",
       "2018-01-03 21.500000000 8919.000000000 93498.000000000   \n",
       "\n",
       "            Previous Day Open Interest  Futures Return  Lagged Return  \n",
       "Date                                                                   \n",
       "2018-01-02            227884.000000000     0.297285169    0.224870699  \n",
       "2018-01-03            226582.000000000    -0.240478720    0.297285169  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using datetime index slicing, create a \"test\" data dataframe using data from 2018-01-01 through the end of the data\n",
    "test = # YOUR CODE HERE \n",
    "print(\"Test set covers from \", test.index.min(), \" to \", test.index.max())\n",
    "test.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  * Then use the model developed on a test set using data from 2018 onward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create four dataframes:\n",
    "# X_train (training set using just the independent variables), X_test (test set of of just the independent variables)\n",
    "# Y_train (training set using just the \"y\" variable, i.e., \"Futures Return\"), Y_test (test set of just the \"y\" variable):\n",
    "X_train = # YOUR CODE HERE \n",
    "y_train = # YOUR CODE HERE \n",
    "X_test = # YOUR CODE HERE \n",
    "y_test = # YOUR CODE HERE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit a SKLearn linear regression using  just the training set (X_train, Y_train):\n",
    "# YOUR CODE HERE \n",
    "\n",
    "# Score how that model works using only the test data\n",
    "# YOUR CODE HERE \n",
    "\n",
    "# And make a prediction of \"y\" values for just the test dataset\n",
    "predictions = # YOUR CODE HERE \n",
    "\n",
    "# Assemble actual y data (Y_test) with predicted y data (from just above) into two columns in a dataframe:\n",
    "Results = y_test.to_frame()\n",
    "Results[\"Predicted Return\"] = predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>Futures Return</th>\n",
       "      <th>Predicted Return</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>2018-01-02</th>\n",
       "      <td>0.297285169</td>\n",
       "      <td>0.004644804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>-0.240478720</td>\n",
       "      <td>0.002612746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>-0.213028366</td>\n",
       "      <td>0.017703208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>-0.353932584</td>\n",
       "      <td>0.016932910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>0.062017252</td>\n",
       "      <td>0.020886894</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Futures Return  Predicted Return\n",
       "Date                                        \n",
       "2018-01-02     0.297285169       0.004644804\n",
       "2018-01-03    -0.240478720       0.002612746\n",
       "2018-01-04    -0.213028366       0.017703208\n",
       "2018-01-05    -0.353932584       0.016932910\n",
       "2018-01-08     0.062017252       0.020886894"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "  * Compare the RMSE from the model using the test sample to the RMSE when using the full-sample. Are the results surprising?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out-of-Sample Root Mean Squared Error (RMSE): 0.40994570155874877\n"
     ]
    }
   ],
   "source": [
    "# Calculate the mean_squared_error (MSE) on actual versus predicted test \"y\" \n",
    "# (Hint: use the dataframe above)\n",
    "mse = # YOUR CODE HERE \n",
    "\n",
    "# Using that mean-squared-error, calculate the root-mean-squared error (RMSE):\n",
    "one_shot_rmse = # YOUR CODE HERE \n",
    "print(f\"Out-of-Sample Root Mean Squared Error (RMSE): {one_shot_rmse}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In-sample Root Mean Squared Error (RMSE): 0.6257993297323167\n"
     ]
    }
   ],
   "source": [
    "# Construct a dataframe using just the \"y\" training data:\n",
    "in_sample_results = y_train.to_frame()\n",
    "\n",
    "# Add a column of \"in-sample\" predictions to that dataframe:  \n",
    "in_sample_results[\"In-sample Predictions\"] = # YOUR CODE HERE \n",
    "\n",
    "# Calculate in-sample mean_squared_error (for comparison to out-of-sample)\n",
    "in_sample_mse = # YOUR CODE HERE \n",
    "\n",
    "# Calculate in-sample root mean_squared_error (for comparison to out-of-sample)\n",
    "in_sample_rmse = # YOUR CODE HERE \n",
    "print(f\"In-sample Root Mean Squared Error (RMSE): {in_sample_rmse}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Out-of-Sample Predictions: The Rolling Out-of-Sample Approach\n",
    "\n",
    "Let's take the model above (i.e., SKLearn predicting futures returns with lagged futures returns), but apply it using a rolling, out of sample approach. This method, which is specific to time-series predictions, will be a more realistic and more rigorous approximation of how the model would perform in real life.\n",
    "\n",
    "Specifically, start at the beginning of the dataset and \"roll forward\" one week at a time, using a 26-week (6 month) rolling training window."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Main Idea: Each week, re-estimate the model, then trade on it the following week. \n",
    "# Continue this \"out of sample\" approach throughout the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construct an empty placeholder dataframe, with a column named \"Out-of-Sample Predictions\"\n",
    "# (Each iteration of the loop, we\"ll append that week\"s predictions to this DataFrame)\n",
    "all_predictions = pd.DataFrame(columns=[\"Out-of-Sample Predictions\"])\n",
    "# Construct an empty placeholder dataframe, with a column named \"Actual Returns\"\n",
    "# (Each iteration of the loop, we\"ll append that week\"s predictions to this DataFrame)\n",
    "all_actuals = pd.DataFrame(columns=[\"Actual Returns\"])\n",
    "\n",
    "# construct a list of all pandas weekly periods that are in the dataset:\n",
    "weeks = fut.index.to_period(\"w\").unique()\n",
    "\n",
    "# Specify a training window (26 weeks (6 months) of data)\n",
    "training_window = 26  \n",
    "\n",
    "# Write a for loop that iterates from 0 to the number of weeks (minus the training window) in the dataset:\n",
    "# We have to subtract the length of the training window because\n",
    "# Going beyond this goes beyond the length of our dataset    \n",
    "for i in range(0, (len(weeks) - training_window - 1)):\n",
    "# Create an datetime object to mark the beginning of the training window (which changes, for each \"i\"):     \n",
    "    beginning_training_week = # YOUR CODE HERE \n",
    "# Create a string variable from that time object with format=\"%Y-%m-%d\"\n",
    "    start_training_period = # YOUR CODE HERE \n",
    "# Create an datetime object to mark the ending of the training window (which would be \"i\"+training_window_length):  \n",
    "    ending_training_week = # YOUR CODE HERE \n",
    "# Create a string variable from that time object with format=\"%Y-%m-%d\"\n",
    "    end_training_period = # YOUR CODE HERE \n",
    "# Create a datetime object that marks the week of test-window data:\n",
    "    test_week = # YOUR CODE HERE \n",
    "# Create a string variable from the beginning of that time object with format=\"%Y-%m-%d\"\n",
    "    start_test_week = # YOUR CODE HERE \n",
    "# Create a string variable from the end of that time object with format=\"%Y-%m-%d\"    \n",
    "    end_test_week = # YOUR CODE HERE \n",
    "# Create a dataframe that includes only data during the training window\n",
    "# (Use datetime slicing and the two start and end string dates constructed above):\n",
    "    train =  # YOUR CODE HERE \n",
    "# Do the same to construct a dataframe including data in the test sample window:    \n",
    "    test =  # YOUR CODE HERE \n",
    "# Create four dataframes:\n",
    "# X_train (training set using just the independent variables), X_test (test set of of just the independent variables)\n",
    "# Y_train (training set using just the \"y\" variable, i.e., \"Futures Return\"), Y_test (test set of just the \"y\" variable):\n",
    "    X_train = train[\"Lagged Return\"].to_frame()\n",
    "    y_train = train[\"Futures Return\"]\n",
    "    X_test = test[\"Lagged Return\"].to_frame()\n",
    "    y_test = test[\"Futures Return\"]\n",
    "# Fit a linear regression to the two training dataframes\n",
    "    # YOUR CODE HERE \n",
    "# Use the model to construct predictions on the X test dataframe,\n",
    "# and save them as a temporary pandas DataFrame \n",
    "    predictions =  # YOUR CODE HERE \n",
    "    predictions = pd.DataFrame(predictions, index=X_test.index, columns=[\"Out-of-Sample Predictions\"])\n",
    "# Create a temporary DataFrame of the actual \"y\"s in the test dataframe, with column name=\"Actual Returns\": \n",
    "    actuals = pd.DataFrame(y_test, index=y_test.index)\n",
    "    actuals.columns = [\"Actual Returns\"]  \n",
    "# Append these two dataframes (predictions and actuals) to the two master DataFrames built outside the loop\n",
    "    all_predictions = all_predictions.append(predictions)\n",
    "    all_actuals = all_actuals.append(actuals)   \n",
    "    # Once the loop is done, concatenate (combine) the two dataframes to a final \"Results\" dataframe:    \n",
    "    Results =  # YOUR CODE HERE \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8d4039c5c0>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results:\n",
    "# YOUR CODE HERE "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  * Evaluate the model RMSE.\n",
    "      * Which has the better RMSE--this more robust method, or the single (\"one-shot\") training and test split just above?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5738123732985758"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mean_squared_error on the Results DataFrame:\n",
    "# (This compares the average (squared) difference between actual and model-predicted returns)\n",
    "rolling_mse = # YOUR CODE HERE \n",
    "# Then use that to calculate the Root-Mean-Squared Error (RMSE):\n",
    "rolling_rmse = # YOUR CODE HERE \n",
    "rolling_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.40994570155874877"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare the RMSE from the training-testing single split versus the rolling out-of-sample approach we just did:\n",
    "# YOUR CODE HERE "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Challenge: Can you calculate the out-of-sample R2? Based on this, should we bet money on this model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out-of-Sample R-squared: -0.022563606609533604\n"
     ]
    }
   ],
   "source": [
    "# YOUR CODE HERE "
   ]
  }
 ],
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  "file_extension": ".py",
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   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
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   "file_extension": ".py",
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