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    "## Data Set Construction\n",
    "\n",
    "**Functions**\n",
    "\n",
    "`pd.read_csv`, `pd.read_excel`, `np.diff` or `DataFrame.diff`, `DataFrame.resample`\n",
    "\n",
    "### Exercise 1\n",
    "\n",
    "1. Download all available daily data for the S&P 500 and the Hang Seng Index from Yahoo! Finance. \n",
    "2. Import both data sets into Python. The final dataset should have a `DateTimeIndex`, and the date\n",
    "   column should not be part of the `DataFrame`.\n",
    "3. Construct weekly price series from each, using Tuesday prices (less likely to be a holiday).\n",
    "4. Construct monthly price series from each using last day in the month.\n",
    "5. Save the data to the HDF file \"equity-indices.h5\".\n"
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    "### Exercise 2\n",
    "\n",
    "Write a function that will correctly aggregate to weekly or monthly respecting the\n",
    "aggregation rules\n",
    "\n",
    "* High: `max`\n",
    "* Low: `min`\n",
    "* Volume: `sum`\n",
    "\n",
    "The signature should be:\n",
    "\n",
    "```python\n",
    "def yahoo_agg(data, freq):\n",
    "    <code here>\n",
    "    return resampled_data\n",
    "```\n"
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    "### Exercise 3\n",
    "\n",
    "1. Import the Fama-French benchmark portfolios as well as the 25 sorted portfolios at both the\n",
    "   monthly and daily horizon from [Ken French\"s Data Library](http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html).\n",
    "   **Note** It is much easier to clean to data file before importing than to find the precise\n",
    "   command that will load the unmodified data.\n",
    "2. Import daily FX rate data for USD against AUD, Euro, JPY and GBP from the [Federal Reserve Economic Database (FRED)](http://research.stlouisfed.org/fred2/categories/94). Use Excel (xls) rather than csv files.\n",
    "3. Save the data to the HDF files \"fama-french.h5\" and \"fx.h5\""
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    "### Exercise 3 (Alternative method)\n",
    "\n",
    "1. Install and use `pandas-datareader` to repeat the previous exercise.\n",
    "\n",
    "#### Preliminary Step\n",
    "\n",
    "You must first install the module using \n",
    "\n",
    "```\n",
    "pip install pandas-datareader\n",
    "``` \n",
    "\n",
    "from the command line. Then you can run this code. **Note**: Running this code requires access\n",
    "to the internet."
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    "### Exercise 4\n",
    "Download data on 1 year and 10 year US government bond rates from FRED, and \n",
    "construct the term premium as the different in yields on 10 year and 1 year\n",
    "bonds. Combine the two yield series and the term premium into a `DataFrame`\n",
    "and save it as HDF."
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