04-time-series-answers.ipynb

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    "(c) 2016 - present. Enplus Advisors, Inc."
   ]
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    "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('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": [],
   "source": [
    "ts = pd.Timestamp('1993-01-01 00:16:00') # __"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "source": [
    "**Exercise:**\n",
    "\n",
    "Generate a an Index of:\n",
    "* 500 calendar days starting on January 1, 2010.\n",
    "* 1 year of US business days starting on January 1, 2010"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cd500 = pd.date_range(start='2010-01-01', periods=500, freq='D') # __"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "bd2010 = pd.date_range(start='2010-01-01', end='2010-12-31', freq='B') # __"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exercise"
    ]
   },
   "source": [
    "**Exercise:**\n",
    "\n",
    "* Create a Series from the `adj_close` column `sp5_df`, using `date` as a `DatetimeIndex`\n",
    "* Sort the index\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."
   ]
  },
  {
   "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"
    ]
   },
   "source": [
    "**Exercise:**\n",
    "\n",
    "Select from the `sp5` DataFrame 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_df`:\n",
    "* Calculate 1-day returns (1-day % difference in `adj_close`)\n",
    "* Calculate monthly and yearly average 1-day returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp5_rtn = sp5.pct_change() # __"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Monthly, Yearly returns\n",
    "rtn_mnth = sp5_rtn.resample('M').mean() # __\n",
    "rtn_year = sp5_rtn.resample('Y').mean() # __"
   ]
  }
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