04-time-series-answers.ipynb

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   "source": [
    "(c) 2016 - present. Enplus Advisors, Inc."
   ]
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   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "setup"
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   "source": [
    "This module uses:\n",
    "* SP500 returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "setup"
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   "source": [
    "import datetime as dt\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "pd.set_option('display.precision', 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "setup"
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   },
   "outputs": [],
   "source": [
    "sp5_df = pd.read_csv(\n",
    "    'sp500.csv', usecols=['date', 'adj_close'], \n",
    "    parse_dates=['date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "source": [
    "**Exercise:**\n",
    "\n",
    "Create a `pandas` Timestamp for January 1st, 1993 16:00 (don't worry about timezone)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
       "Timestamp('1993-01-01 16:00:00')"
      ]
     },
     "execution_count": 3,
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   ],
   "source": [
    "pd.Timestamp('1993-01-01 16:00') # __"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "source": [
    "**Exercise:**\n",
    "\n",
    "Generate a an Index of:\n",
    "* 5 calendar days starting on January 1, 2010.\n",
    "* All US business days (weekdays) starting on January 1, 2010\n",
    "  and ending on January 15, 2010.\n",
    "  \n",
    "__Hint:__ You can view the help for a function by running `help(function_name)`, e.g. `help(pd.Timestamp)`. Try looking at the help for `pd.date_range`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2010-01-01', '2010-01-02', '2010-01-03', '2010-01-04',\n",
       "               '2010-01-05'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start='2010-01-01', periods=5, freq='D') # __"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2010-01-01', '2010-01-04', '2010-01-05', '2010-01-06',\n",
       "               '2010-01-07', '2010-01-08', '2010-01-11', '2010-01-12',\n",
       "               '2010-01-13', '2010-01-14', '2010-01-15'],\n",
       "              dtype='datetime64[ns]', freq='B')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start='2010-01-01', end='2010-01-15', freq='B') # __"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "source": [
    "**Exercise:**\n",
    "\n",
    "Create a Series named `sp5` from the `adj_close` column `sp5_df`, using `date` as the\n",
    "index. Make sure you call `sort_index()` to make sure the index is sorted.\n",
    "\n",
    "__Hint:__ The first two parameters of `pd.Series` are `data` and `index`. When both `data` and `index` are `Series`, the `index` of `data` is aligned against the values in `Series`. You can always force positional alignment by converting a `Series` to an `PandasArray` (`pd.Series.array`)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp5 = pd.Series( # __\n",
    "    sp5_df.adj_close.array, index=sp5_df.date, \n",
    "    name='adj_close').sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "source": [
    "**Exercise:**\n",
    "\n",
    "Write 2 different ways to select January 3, 1995 from the `sp5` series. \n",
    "\n",
    "_There are more than 2 ways to do this, but you only need 2!_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "d1a = sp5['19950103'] # __\n",
    "d1b = sp5['1995-01-03'] # __\n",
    "d1c = sp5[dt.datetime(1995, 1, 3)] # __"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exercise"
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   },
   "source": [
    "**Exercise:**\n",
    "\n",
    "Select from `sp5` all observations for:\n",
    "* March 1995\n",
    "* Year of 1995"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mar_95 = sp5['1995-03'] # __"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_95 = sp5['1995'] # __"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "source": [
    "**Exercise**\n",
    "\n",
    "For `sp5`:\n",
    "\n",
    "Calculate the day-over-day percent change in the values and to assign the result to the variable `sp5_rtn`.\n",
    "\n",
    "Hint: Use `shift`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp5_rtn = sp5 / sp5.shift(1) - 1 # __"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alternative solution\n",
    "# sp5.pct_change()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "source": [
    "**Exercise**\n",
    "\n",
    "Resample the data from daily to monthly to calculate average day-over-day percent change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "rtn_mnth = sp5_rtn.resample('M').mean() # __"
   ]
  }
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