diff --git a/nb/fred-georeturns.ipynb b/nb/fred-georeturns.ipynb
index 3bba7f4..65ceb9d 100644
--- a/nb/fred-georeturns.ipynb
+++ b/nb/fred-georeturns.ipynb
@@ -1,985 +1,884 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:99159f50a56b9d50b0e82032368b63351365330ee92a1dab740c272b8f0e6af5"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Geometric mean returns on FRED series"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We examine economic and financial time series where Holt-Winters is used to forecast one-year ahead. Daily data for bonds, equity, and gold is then analyzed.\n",
-      "\n",
-      "The focus is on geometric mean returns because they optimally express mean-variance under logarithmic utility."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "*Dependencies:*\n",
-      "\n",
-      "     - Linux, bash\n",
-      "     - Python: matplotlib, pandas\n",
-      "     - Modules: yi_1tool, yi_plot, yi_timeseries, yi_fred\n",
-      "     \n",
-      "*CHANGE LOG*\n",
-      "\n",
-      "    2015-05-26  Code revision using template v14.12.21.\n",
-      "    2014-10-11  Code review. Template 2014-09-28.\n",
-      "    2014-09-01  First version."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#  NOTEBOOK settings and system details:   [00-tpl v14.12.21]\n",
-      "\n",
-      "#  Assume that the backend is LINUX (our particular distro is Ubuntu, running bash shell):\n",
-      "print '\\n ::  TIMESTAMP of last notebook execution:'\n",
-      "!date\n",
-      "print '\\n ::  IPython version:'\n",
-      "!ipython --version\n",
-      "\n",
-      "#  Automatically reload modified modules:\n",
-      "%load_ext autoreload\n",
-      "%autoreload 2\n",
-      "#           0 disables autoreload.\n",
-      "#  Generate plots inside notebook:\n",
-      "%matplotlib inline\n",
-      "\n",
-      "#  DISPLAY options\n",
-      "from IPython.display import Image \n",
-      "#  e.g. Image(filename='holt-winters-equations.png', embed=True) # url= also works\n",
-      "from IPython.display import YouTubeVideo\n",
-      "#  e.g. YouTubeVideo('1j_HxD4iLn8', start='43', width=600, height=400)\n",
-      "from IPython.display import HTML # useful for snippets\n",
-      "#  e.g. HTML('<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=700 height=350></iframe>')\n",
-      "\n",
-      "import pandas as pd\n",
-      "print '\\n ::  pandas version:'\n",
-      "print pd.__version__\n",
-      "#      pandas DataFrames are represented as text by default; enable HTML representation:\n",
-      "#      [Deprecated: pd.core.format.set_printoptions( notebook_repr_html=True ) ]\n",
-      "pd.set_option( 'display.notebook_repr_html', False )\n",
-      "\n",
-      "#  MATH display, use %%latex, rather than the following:\n",
-      "#                from IPython.display import Math\n",
-      "#                from IPython.display import Latex\n",
-      "\n",
-      "print '\\n ::  Working directory (set as $workd):'\n",
-      "workd, = !pwd\n",
-      "print workd + '\\n'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        " ::  TIMESTAMP of last notebook execution:\n",
-        "Thu Jun 25 20:35:28 PDT 2015\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        " ::  IPython version:\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "2.3.0\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        " ::  pandas version:\n",
-        "0.15.0\n",
-        "\n",
-        " ::  Working directory (set as $workd):\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "/home/yaya/Dropbox/share/git/nous/fecon235/nb\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from yi_1tools import *\n",
-      "from yi_plot import *\n",
-      "from yi_timeseries import *\n",
-      "from yi_fred import *"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Download data and construct a dataframe"
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comparative geometric mean returns\n",
+    "\n",
+    "We examine economic and financial time series where Holt-Winters \n",
+    "is used to forecast one-year ahead. Daily data for bonds, equity, \n",
+    "and gold is then analyzed.\n",
+    "\n",
+    "Our focus is on geometric mean returns since they \n",
+    "optimally express mean-variance under logarithmic utility. \n",
+    "We shall cover portfolio optimization in another notebook.\n",
+    "\n",
+    "[ ] TODO: *use sympy to symbolically derive geometric mean \n",
+    "return from the moments of an asset's return distribution.* \n",
+    "Our function georet() gives a *numerical* approximation. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Dependencies:*\n",
+    "\n",
+    "- Repository: https://github.com/rsvp/fecon235\n",
+    "- Python: matplotlib, pandas\n",
+    "     \n",
+    "*CHANGE LOG*\n",
+    "\n",
+    "    2016-01-05  MAJOR REWRITE: use pattern from monthly and daily series\n",
+    "                   for new functions groupget, grouppc, groupgeoret.\n",
+    "                   Forecast print out replaced by preservable groupholtf.\n",
+    "                   Dictionary comprehension clarifies code.\n",
+    "    2016-01-03  Fix issue #2 with v4 and p6 upgrades.\n",
+    "    2015-05-26  Code revision using template v14.12.21.\n",
+    "    2014-10-11  Code review. Template 2014-09-28.\n",
+    "    2014-09-01  First version."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from fecon235.fecon235 import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ::  Python 2.7.10\n",
+      " ::  IPython 4.0.0\n",
+      " ::  jupyter 1.0.0\n",
+      " ::  notebook 4.0.6\n",
+      " ::  matplotlib 1.4.3\n",
+      " ::  numpy 1.10.1\n",
+      " ::  pandas 0.17.1\n",
+      " ::  pandas_datareader 0.2.0\n",
+      " ::  Repository: fecon235 v4.15.1230 develop\n",
+      " ::  Timestamp: 2016-01-06, 17:06:06 UTC\n",
+      " ::  $pwd: /media/yaya/virt15h/virt/dbx/Dropbox/ipy/fecon235/nb\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We shall retrieve the following data of monthly frequency: **(aggregated) inflation, bonds (zero coupon equivalent of 10-y Treasury), equities (S&P 500), and gold (London PM fix) -- all denominated in US dollars -- then lastly, the real trade-weighted USD index (Federal Reserve) and US home prices (per Case-Shiller).** The details for each series is given in their respective notebooks.\n",
-      "\n",
-      "*Home prices will create a 3-month lag, due to their release cycle.*"
+    }
+   ],
+   "source": [
+    "#  PREAMBLE-p6.15.1223 :: Settings and system details\n",
+    "from __future__ import absolute_import, print_function\n",
+    "system.specs()\n",
+    "pwd = system.getpwd()   # present working directory as variable.\n",
+    "print(\" ::  $pwd:\", pwd)\n",
+    "#  If a module is modified, automatically reload it:\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "#       Use 0 to disable this feature.\n",
+    "\n",
+    "#  Notebook DISPLAY options:\n",
+    "#      Represent pandas DataFrames as text; not HTML representation:\n",
+    "import pandas as pd\n",
+    "pd.set_option( 'display.notebook_repr_html', False )\n",
+    "#  Beware, for MATH display, use %%latex, NOT the following:\n",
+    "#                   from IPython.display import Math\n",
+    "#                   from IPython.display import Latex\n",
+    "from IPython.display import HTML # useful for snippets\n",
+    "#  e.g. HTML('<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=700 height=350></iframe>')\n",
+    "from IPython.display import Image \n",
+    "#  e.g. Image(filename='holt-winters-equations.png', embed=True) # url= also works\n",
+    "from IPython.display import YouTubeVideo\n",
+    "#  e.g. YouTubeVideo('1j_HxD4iLn8', start='43', width=600, height=400)\n",
+    "from IPython.core import page\n",
+    "get_ipython().set_hook('show_in_pager', page.as_hook(page.display_page), 0)\n",
+    "#  Or equivalently in config file: \"InteractiveShell.display_page = True\", \n",
+    "#  which will display results in secondary notebook pager frame in a cell.\n",
+    "\n",
+    "#  Generate PLOTS inside notebook, \"inline\" generates static png:\n",
+    "%matplotlib inline   \n",
+    "#          \"notebook\" argument allows interactive zoom and resize."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download data and construct a dataframe\n",
+    "\n",
+    "We retrieve the following data of monthly frequency: **(aggregated) inflation, \n",
+    "bonds (zero coupon equivalent of 10-y Treasury), equities (S&P 500), and \n",
+    "gold (London PM fix)** -- all denominated in US dollars -- **then lastly, the \n",
+    "real trade-weighted USD index (Federal Reserve) and US home prices (per Case-Shiller).** \n",
+    "The details for each series is given in their respective notebooks. \n",
+    "If the available data has daily frequency, we use the pandas method called \n",
+    "\"resampling\" to induce monthly data (enter \"monthly??\" in an \n",
+    "input cell for more details).\n",
+    "\n",
+    "ATTENTION: *The inclusion of home prices, unfortunately, will create a 3-month lag, \n",
+    "due to their release cycle. Since this is a comparative study, \n",
+    "the rest of the data will appear somewhat stale, but this \n",
+    "section is intended for long-term trends.* \n",
+    "Second half of this notebook will examine more responsive daily data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ::  Case-Shiller prepend successfully goes back to 1987.\n",
+      " ::  S&P 500 prepend successfully goes back to 1957.\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#  Specify monthly series of interest as ms list:\n",
-      "ms = [ m4infl, m4zero10, m4spx, m4xau, m4usdrtb, m4homepx ]\n",
-      "names = ['Infl', 'Zero10', 'SPX', 'XAU', 'USD', 'Homes']\n",
+    }
+   ],
+   "source": [
+    "#  Specify monthly series of interest as a dictionary:\n",
+    "msdic = {'Infl' : m4infl, 'Zero10' : m4zero10, 'SPX' : m4spx, \n",
+    "         'XAU' : m4xau, 'USD' : m4usdrtb, 'Homes' : m4homepx }\n",
+    "\n",
+    "#  Download data into a dataframe:\n",
+    "msdf = groupget( msdic )\n",
+    "#     \"groupget??\" at input cell gives function details."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After downloading the level series, we compute the YoY percentage change \n",
+    "for each series. *This will be the a trailing 12-month statistic, \n",
+    "thus it is overlapping.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#  Construct the mega YoY dataframe:\n",
+    "mega = grouppc( msdf, freq=12 )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define start time and get stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#  Define start time as t0\n",
+    "t0 = '1988'\n",
+    "\n",
+    "#  We can easily rerun the rest of this notebook \n",
+    "#  by specifying another start time, then: Cell > Run All Below"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            Homes        Infl         SPX         USD         XAU      Zero10\n",
+      "count  334.000000  334.000000  334.000000  334.000000  334.000000  334.000000\n",
+      "mean     3.905883    2.397870    8.708560   -0.034278    4.657274    2.247932\n",
+      "std      8.002381    1.015202   16.260123    5.224677   15.495695    7.390370\n",
+      "min    -18.906156   -0.183948  -42.349361  -10.574015  -27.752361  -18.521264\n",
+      "25%     -1.069951    1.678485    2.316010   -3.627323   -6.904147   -3.085161\n",
+      "50%      4.490362    2.349958   10.596906   -0.157484    1.745723    2.892972\n",
+      "75%     10.605588    2.847579   19.535143    2.929578   13.956456    7.705685\n",
+      "max     17.077118    5.243597   52.051354   14.502797   60.357143   20.492399\n",
       "\n",
-      "#  Download into a dictionary:\n",
-      "msd = {}\n",
-      "for i in ms:\n",
-      "    msd[i] = getfred(i)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        " ::  S&P 500 prepend successfully goes back to 1957.\n",
-        " ::  Case-Shiller prepend successfully goes back to 1987."
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "After downloading the level series, we compute the YoY percentage change for each series. *This will be the a trailing 12-month statistic, thus it's overlapping.*"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#  Compute the YoY percentage change:\n",
-      "msdc = {}\n",
-      "for i in ms:\n",
-      "    msdc[i] = pcent( msd[i], 12 )"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#  Construct the mega YoY dataframe:\n",
-      "mega = paste( [ msdc[i] for i in ms ] )\n",
-      "#  Give names to the columns for mega:\n",
-      "mega.columns = names"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Define start time and get stats"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#  Start time given by t0\n",
-      "t0 = '1988'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "stats( mega[t0:] )"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "             Infl      Zero10         SPX         XAU         USD       Homes\n",
-        "count  327.000000  327.000000  327.000000  327.000000  327.000000  327.000000\n",
-        "mean     2.424121    2.229173    8.753083    4.949457   -0.286063    3.882495\n",
-        "std      1.005295    7.463804   16.416248   15.524655    4.982369    8.088091\n",
-        "min     -0.180575  -18.521264  -42.349361  -27.752361  -10.577564  -18.906156\n",
-        "25%      1.690927   -3.217100    2.288186   -6.463883   -3.809645   -1.218646\n",
-        "50%      2.366964    2.912582   10.726925    1.898076   -0.357301    4.293769\n",
-        "75%      2.887417    7.752083   19.981864   14.325670    2.682498   10.684985\n",
-        "max      5.242059   20.492399   52.051354   60.357143   14.508997   17.077118\n",
-        "\n",
-        " ::  Index on min:\n",
-        "Infl     2009-07-01\n",
-        "Zero10   1994-10-01\n",
-        "SPX      2009-03-01\n",
-        "XAU      2013-12-01\n",
-        "USD      2008-03-01\n",
-        "Homes    2009-01-01\n",
-        "dtype: datetime64[ns]\n",
-        "\n",
-        " ::  Index on max:\n",
-        "Infl     1990-10-01\n",
-        "Zero10   1996-01-01\n",
-        "SPX      2010-03-01\n",
-        "XAU      2006-05-01\n",
-        "USD      2009-03-01\n",
-        "Homes    2004-07-01\n",
-        "dtype: datetime64[ns]\n",
-        "\n",
-        " ::  Head:\n",
-        "                Infl     Zero10        SPX        XAU       USD      Homes\n",
-        "T                                                                         \n",
-        "1988-01-01  3.984315 -12.661625  -6.207571  17.042392 -9.836868  12.139461\n",
-        "1988-02-01  3.876021  -8.068941  -8.186030   9.982617 -8.633477  11.661442\n",
-        "1988-03-01  3.924380  -9.248410  -8.156954   8.841782 -9.393214  11.345646\n",
-        "1988-04-01  3.992870  -5.477528  -8.960111   2.878355 -8.283502  10.974485\n",
-        "1988-05-01  4.017727  -4.487925 -11.871026  -1.578661 -7.507266  10.543908\n",
-        "1988-06-01  4.129092  -4.738070 -10.689462   0.022137 -7.401580  10.559567\n",
-        "1988-07-01  4.276206  -5.671538 -12.487441  -3.115990 -6.469468  10.756853\n",
-        "\n",
-        " ::  Tail:\n",
-        "                Infl    Zero10        SPX        XAU        USD     Homes\n",
-        "T                                                                        \n",
-        "2014-09-01  1.573155  2.870055  18.259104  -8.035048   1.924209  4.982426\n",
-        "2014-10-01  1.590927  2.873362  13.047620  -6.736243   4.278896  4.557101\n",
-        "2014-11-01  1.397419  3.603244  14.134137  -8.234146   4.984866  4.353347\n",
-        "2014-12-01  1.098285  6.108084  14.380677  -2.298029   6.786695  4.482448\n",
-        "2015-01-01  0.731306  9.213133  10.175566   1.235684   7.608924  4.485425\n",
-        "2015-02-01  0.806330  6.585098  14.399577  -7.207207   8.760392  4.996752\n",
-        "2015-03-01  0.849246  6.019017  11.453936 -11.421092  10.734463  4.990347"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        " ::  Correlation matrix:\n",
-        "            Infl    Zero10       SPX       XAU       USD     Homes\n",
-        "Infl    1.000000 -0.059018 -0.016182 -0.072354 -0.308788  0.005064\n",
-        "Zero10 -0.059018  1.000000 -0.137220 -0.000802  0.214137 -0.295339\n",
-        "SPX    -0.016182 -0.137220  1.000000 -0.212138 -0.049899  0.248055\n",
-        "XAU    -0.072354 -0.000802 -0.212138  1.000000 -0.513692 -0.244964\n",
-        "USD    -0.308788  0.214137 -0.049899 -0.513692  1.000000  0.039124\n",
-        "Homes   0.005064 -0.295339  0.248055 -0.244964  0.039124  1.000000"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "There is not much correlation among our assets, except a mild negative between gold XAU and USD. Next, the boxplot gives us an idea of the range of returns, and their persistence. It's a visual aid to the geometric mean returns which is most significant as investment metric."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#  Overlapping YoY percentage change, recently:\n",
-      "boxplot(mega[t0:], 'Assets YoYm')\n",
-      "#  where the red dot represents the latest point."
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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3LoeSF33/+MehFnoZL/q6zvAcekl4fEVXyboAqSrz+ctTbN5Cdy4jyZb4FVd4Dt21znPo\nzuXAwADE/wPs3LB8LBfncm5gIOsSuDLomgo9T3muNJQhPklNP4qvknUBUlWG92cjeYrNc+guN4ZL\nwUkVzPo7V5gUtPLF4+lJNxqeQ3eFIEGZ3y4+losbreFy6F6hu0Ioe4Xu3Gj5RVHyledKQ9nj8xxz\nsZU5vjzF1pYKXdJmkm6RdHWcnihpsaS7JC2S1NOO47juNX161iVwLv/aknKR9GHgVcDWZnaUpPOB\nh8zsfEmnARPM7PSabTzl4pxzY5RqykXSS4A3Al8Dqgc5Cpgfn88Hjmn1OM4554bXjpTLZ4GPAusT\n87Yzs9Xx+WpguzYcpyV5ynOlweMrtoGBStZFSFWZz1+eYmupH7qkNwEPmtktkvrrrWNmJqlubmVg\nYIDe3l4Aenp66Ovroz8OblF9kdo1PRj/aWNa+8962uMr9vT8+YMMDOSnPH7+8jNdqVSYF8eFqNaX\njbSUQ5d0LvBO4FlgPLAN8ANgP6DfzFZJ2h5YYmZ71GzrOXTnIu+W6UYrtRy6mZ1pZjuZ2S7A8cB1\nZvZO4Cqg2i9hOnBFK8dxzm+6cW5k7e6HXm1jzAEOl3QXcEiczlT1J0xZlT2+2bMrWRchZZWsC5Cq\nMr8/8xRb28ZyMbOlwNL4fA1wWLv27ZxzbmR+678rhLLnmH0sFzdaPpaLK7yyV+jOjZaP5UK+8lxp\nKHt8nmMutjLHl6fYuqZCd8XmY7k4NzJPuTjnXIF4ysU557pA11ToecpzpcHjKzYfy6W48hRb11To\nzuXZ/Pkjr+PcSDyH7lwOeLdMN1qeQ3eF5zfdODeyrqnQ85TnSkPZ4/OxXIqtzO/PPMXWNRW6c86V\nnefQXSGUPcfsY7m40fKxXFzhlb1Cd260/KIo+cpzpaHs8XmOudjKHF+eYuuaCt0Vm4/l4tzIPOXi\nnHMF4ikX55zrAl1Toecpz5UGj6/YfCyX4spTbF1ToTuXZz6Wi2sHz6E7lwPeLdONlufQXeH5TTfO\njaxrKvQ85bnSUPb4fCyXYivz+zNPsbVUoUvaSdISSbdJ+p2kk+P8iZIWS7pL0iJJPe0prnPOuUZa\nyqFLmgRMMrNBSVsBvwaOAd4FPGRm50s6DZhgZqfXbOs5dDdqZc8x+1gubrQ6NpaLpCuA/4mPg8xs\ndaz0K2a2R826XqG7USt7he7caHXkoqikXmBf4CZgOzNbHRetBrZr13Galac8V7MkNf0ovkrWBUhV\nGd6fwylzfHmKbVw7dhLTLd8HTjGzx5MViJmZpLptq4GBAXp7ewHo6emhr6+P/v5+YOhFatf04OBg\nW/eXxfSSJUsaLr/wwgtTff3aNX3ssf08/DAMVdD98e9I04OEt9Vo1w/TEyb0s2ZNfuIv8/uzm+NL\nc7pSqTBv3jyADfVlIy2nXCRtDlwDXGtmF8Z5dwD9ZrZK0vbAEk+5OOh86sRTNa5sUku5KDTFvw4s\nr1bm0VVAdXy86cAVrRzHjcwvqDnnWs2hvxZ4B3CwpFvi4whgDnC4pLuAQ+J0pqo/Ycqq7P20y37+\nfCyX4spTbC3l0M3sBhp/KRzWyr6d6ybz50NMkzrXNB/LpSSKkiv2HHp9RSlnt2qlp1i76zkfy8U5\nl6kcZSWaYmZNPzqpayr0POW50lHJugCp8vNXbHPmVLIuQmry9N7smgq97Px/bro8W7Uq6xJ0B8+h\nu44qew594kTijVOdMWECrFnTueONRaUylGqZPRtmzgzP+/vDoyw6PQ6P59Cd65CHHw5fIJ16dPLL\nw9U3e3bWJRjSNS30SqWy4bbaMipKfGNtMV+/YAGLLrqI+1av5iXbbcfUk0/mwCOPTO14rWr2eM2e\nv6L0jpk0qcKqVf1ZFyMVUgWz/g4er3ELvS1juTiXhusXLODHp5zCp+6+mwphlJYZd98NMKZK3WWj\nUoFLv7KAR2+6iEmrV3P85O3Y9h9P5oT3HVmqlEuedE0L3eXDWFqUZ02bxicXLdpk/tnTpnHOwoVt\nP147lP14w6ntq70V8EbgssS844AfAWtrti1yXdD5c+459NIr41gu4555pu78zZ5+usMlcaNR2//6\nlKlTN6rMIVTup06blmlf7TLrmgo9T31F01CUsVwMhSbNKB7PLl26YbtKYh/rli4d9T6MYowFX8b3\nZ/ILuZKYn9cv5IkTR/222ugBlaa2mzix/TF0TYXu8kGMvgvH1GuuYcbkyRttf+bkyRx+zTWj3ofw\n1l9Wnt1ii7rz140f3+GSjE6zPZSWLMlPDyXPoZdEnnKpw2mml8viuXPZ7OmnWTd+PIefdFIpe7kU\n5XhjkbyoXXXm5Mkc8bnP5fKidlHOXcf+p+hYeIXeXnn+YCcV5UPjx2uPVr+QO6ko584rdIrTT7tZ\nne4L26yy99Mue3zNKsLnryjnznu5FMhYL8xsrQXsp2lM4VT20zS21oLML8w457LRNS30vfeGW2/t\n2OGaNpZv7Xo5yhmTJzNtDDnKorRg/Xj5OF6ZNXsX87hnnuHZLbbo2F3MfqcosHx51iVov0UXXbRR\nZQ7wqbvv5uy5c3Obp3TdqdMDWKWtbmMqB3cxd03KZf36StZFaLui9fNtRRn7aSeVPb6i3CcxWsnG\nVCXO+9Tdd7N47tzMygQlr9D33hvGjQsPGHq+997ZlqtditbP17myyOtdzKXKoW/6f/8OIgzpBDAr\nPiB8py7daM28dKFsNYc+1n6+Zc/5+vHyoRDlHMP/DT0L+GSd+WcD54zlmE28KN5tkYK8ofAbb/x4\n+T5es4pQzqI0prxCpzj9tMfSSkiqMPRbZMwK8MValH7aZY+vWUX4/DXbmFq5ahU7TZrUscZUJr1c\nJB0BXAhsBnzNzM5r047HtPr1wCLgLWzJWXqCqcCBYz1mJys8rLnDVSpN/V8viY6PdtLkd1ZTJkzo\n3LG6QSv/Yq+Z857nf7F34JFHcuCRR+bqpqlUWuiSNgPuBA4D/gz8EjjBzG5PrNNUC30sb4qtWMAb\nOYXLGPpZdByT+RGfYy2j+ybt9BuqKC3KTitKOTv6bVVVgF9YRTheUWLL4k7R/YE/mNkKM/s78B3g\n6HbseCyjmZ0y9aKNKnOAy7ibU6fNHfU+8to6cPk0ltEk2/Hw0SRdUloV+o7AysT0fXFeR3k/7TKp\nZF2AVJX9/JU5vjzFllYOfVTNhoGBAXp7ewHo6emhr69vQy6q+iKNZfrggw/eaP+7s9WGjoqD8W8/\nsGDpb5ld89N4yZIlYz5eGtPVS5tj3X5wcLCjx+v0NAzGywT5KE+3nj+Pr/PHq1QqzJs3D2BDfdlI\nWjn0A4BZZnZEnD4DWJ+8MNqJXi5FG48ZipPH6zQvZz6OV+ZrBEU5d1n0cvkVsJukXuB+wv+GPSGl\nYzVUrbTPnjuXlXc8zU57jOeIHI/H7BqbOTPrEjhooRdWs8frcC+sovfASiWHbmbPAv8B/BhYDlyW\n7OHSSQceeSTnLFxI36mzOGfhwtJW5kM/4cqpv7+SdRFSVfbzV4T4mr02DZWmtkujw0Vq/dDN7Frg\n2rT2P1Z9fVmXwDnn0tU1d4oWRadTlHm+caOIipKH9eO1T+dfEx8PvTCafWMU4Y3vyqHoeeYyK/Xw\nuUlFyOG1ppJ1AVJV9vNXlPjKkGduv0rWBdigayp0V2yxG65zuTN9etYlGOI59JIoe8qlKPF5jrm+\nopSzCDyH7lwHeY7ZZaVrUi5FyVE2a/r0StZFSFkl6wKMiueYG6lkXYDU5Klu6ZoKvewGBrIugXON\n5SnPXGaeQ3eFUPYcbNnjc+2TxXjozrWVj+Xi8mrWrKxLMKRrKvQ85bnSUPb4yjqWy/ULFnDWtGlM\noY+zpk3j+gULsi5SKsr8/pw9u5J1ETbwXi7OZSQ5vPNhQP8imBGHei7rIHIuXZ5DL4lZs/L108+N\n7Kxp0/jkokWbzD972jTOWbgwgxK1Ri301yxyXZCnsVy6JuVSdrNnZ10CN1bJf5GYVNR/kWhmTT9c\ne3RNhV7mHF5QyboAqSrj+Xt2iy02PK8k5q8bP77jZUlbGc/fkErWBdigayp0V2xlHMtl6sknM2Py\n5I3mnTl5MoefdFJGJXLNyFMfe8+hl0TZ+zGXNb7rFyxg8dy5bPb006wbP57D/V8kuhEMl0P3Cr0k\nylrhVZU9Pr+o7UbLL4pS9hyej+VSdHnqy5yGMn/+8hRb11ToZedjuTjnPOXiCqHsKZeyx+fax1Mu\nrvB8LBeXV3m69tE1FXqe8lxpKHt8ZR3LZUgl6wKkqszvzzxd/2i6Qpd0gaTbJS2T9ANJ2yaWnSHp\n95LukDS1PUV1rrzy1JfZFVfTOXRJhwP/Z2brJc0BMLPTJe0JXALsB+wI/ATY3czW12zvOfQ28m5v\nzmUjT2O5tOWiqKQ3A28xs3dIOgNYb2bnxWULgVlm9ouabbxCH6NuHfzIuazl6bPXiYui7wZ+FJ/v\nANyXWHYfoaWeqTLk8IYb3GjJkiWlHvyoDOdvOB5fvhXlszfseOiSFgOT6iw608yujuvMAP5mZpcM\ns6u6UQ0MDNDb2wtAT08PfX199Pf3A0NvgHZNDw4OtnV/eZsue3xz5pQ7vrKfv7LHl+Z0pVJhXhzM\nqFpfNtJSykXSAPBvwKFm9nScdzqAmc2J0wuBmWZ2U822nnJxo+b9tJ0LUkm5SDoC+ChwdLUyj64C\njpf0XEm7ALsBNzd7HOe6gV/Qdu3QSg59LrAVsFjSLZK+AGBmy4HLgeXAtcAH8tAUr/6EKauyx1f2\nftp56suchjK/P/MUW9P/U9TMdhtm2bnAuc3u2znn3Nj5WC6uEMqeQy97fK59fCwXV3hTpmRdAufy\nr2sq9DzludJQhvg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-       "text": [
-        "<matplotlib.figure.Figure at 0xad29588c>"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        " ::  Finished: boxplot-Assets_YoYm.png\n"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Geometric mean returns on non-overlapping periods"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#  Geometric mean returns, non-overlapping, annualized:\n",
-      "for i in range(len(ms)):\n",
-      "    print names[i], georet(msd[ms[i]][t0:], 12)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Infl "
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[2.33, 2.33, 0.5, 12]\n",
-        "Zero10 "
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[2.0, 2.25, 7.19, 12]\n",
-        "SPX "
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[7.82, 8.57, 12.23, 12]\n",
-        "XAU "
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[3.28, 4.14, 13.09, 12]\n",
-        "USD "
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[0.03, 0.12, 4.12, 12]\n",
-        "Homes "
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[3.43, 3.46, 2.55, 12]\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Note: the geometric returns do not include interest and dividend payouts for bonds and equities. Some observations in chronological order:\n",
+      " ::  Index on min:\n",
+      "Homes    2009-01-01\n",
+      "Infl     2009-07-01\n",
+      "SPX      2009-03-01\n",
+      "USD      2008-03-01\n",
+      "XAU      2013-12-01\n",
+      "Zero10   1994-10-01\n",
+      "dtype: datetime64[ns]\n",
       "\n",
-      "- 2014-09-01, georet since 2010\n",
-      "    - Inflation at 1.7% which is below Fed target of 2%.\n",
-      "    - Total return on bonds, approx 2.52 + 2.33 = 4.85%\n",
-      "    - Total return on equity, approx. 11.9 + 2 = 13.9% -- very heated.\n",
-      "    - Gold indecisive about breaking 1260 LTS.\n",
-      "    - USD though at -0.69%, will strengthen given Draghi wanting weak EUR.\n",
-      " \n",
-      " \n",
-      " - 2014-10-11, georet since 2004\n",
-      "    - Inflation over ten years is running 2% annually.\n",
-      "    - Gold dominates over ten years.\n",
-      "    - Gold recently holds at 1180 triple local bottom.\n",
+      " ::  Index on max:\n",
+      "Homes    2004-07-01\n",
+      "Infl     1990-10-01\n",
+      "SPX      2010-03-01\n",
+      "USD      2009-03-01\n",
+      "XAU      2006-05-01\n",
+      "Zero10   1996-01-01\n",
+      "dtype: datetime64[ns]\n",
       "\n",
+      " ::  Head:\n",
+      "                Homes      Infl        SPX       USD        XAU     Zero10\n",
+      "T                                                                         \n",
+      "1988-01-01  12.139461  3.984319  -6.207571 -9.836868  17.042392 -12.661625\n",
+      "1988-02-01  11.661442  3.875810  -8.186030 -8.633477   9.982617  -8.068941\n",
+      "1988-03-01  11.345646  3.923987  -8.156954 -9.393214   8.841782  -9.248410\n",
+      "1988-04-01  10.974485  3.992633  -8.960111 -8.283502   2.878355  -5.477528\n",
+      "1988-05-01  10.543908  4.017451 -11.871026 -7.507266  -1.578661  -4.487925\n",
+      "1988-06-01  10.559567  4.128604 -10.689462 -7.401580   0.022137  -4.738070\n",
+      "1988-07-01  10.756853  4.275829 -12.487441 -6.469468  -3.115990  -5.671538\n",
       "\n",
-      " - 2014-10-12, georet since 1988\n",
-      "    - Inflation in the long-run about 3% annually.\n",
-      "    - Bond price alone increases 2% annually (excludes interest income).\n",
-      "    - Gold at 1.73% does not keep up with inflation.\n",
-      "    - USD at break-even over the long-run.\n",
-      "    - Home prices have georet of 3.6%.\n",
+      " ::  Tail:\n",
+      "               Homes      Infl        SPX        USD        XAU    Zero10\n",
+      "T                                                                        \n",
+      "2015-04-01  4.833607  0.790537  12.280293  10.154619  -7.736721  7.202302\n",
+      "2015-05-01  4.844291  0.815938  12.167416   9.815597  -7.530004  3.148595\n",
+      "2015-06-01  4.841549  0.893118   7.790849  10.680445  -7.225832  2.011656\n",
+      "2015-07-01  4.969711  0.891452   6.329319  12.875617 -12.641221  1.966971\n",
+      "2015-08-01  5.097901  0.907884   6.557453  14.025825 -13.606178  2.058782\n",
+      "2015-09-01  5.391610  0.842386  -2.590674  13.156224  -9.018853  3.285862\n",
+      "2015-10-01  5.565592  0.879616   3.866075  10.834236  -5.184130  2.195704\n",
       "\n",
-      "- 2015-05-27, georet since 1988\n",
-      "    - Inflation in the long-run drops 70 bp to about 2.3% annually.\n",
-      "    - Bond price continues its increase at 2% annually.\n",
-      "    - Gold at 3.4% reacting more to stronger USD (cf. correlation).\n",
-      "    - Home prices also have georet of 3.4% (but low 2.6% volatility)."
+      " ::  Correlation matrix:\n",
+      "           Homes      Infl       SPX       USD       XAU    Zero10\n",
+      "Homes   1.000000  0.005092  0.247100  0.043519 -0.245234 -0.294438\n",
+      "Infl    0.005092  1.000000 -0.008046 -0.357532 -0.047562 -0.066079\n",
+      "SPX     0.247100 -0.008046  1.000000 -0.054522 -0.207546 -0.136715\n",
+      "USD     0.043519 -0.357532 -0.054522  1.000000 -0.523893  0.207311\n",
+      "XAU    -0.245234 -0.047562 -0.207546 -0.523893  1.000000 -0.002806\n",
+      "Zero10 -0.294438 -0.066079 -0.136715  0.207311 -0.002806  1.000000\n"
      ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Forecasts using Holt-Winters method"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We forecast one-year ahead using the monthly data. Note that current infl is rebased to 1, thus 1.02 would signify 2% increase."
+    }
+   ],
+   "source": [
+    "#  Slice the data:\n",
+    "stats( mega[t0:] )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is not much correlation among our assets, \n",
+    "except a mild negative between gold XAU and USD. \n",
+    "(2015-05-26 at -0.51)\n",
+    "\n",
+    "\n",
+    "## Boxplot of overlapping annual changes\n",
+    "\n",
+    "The boxplot gives us an idea of the range of annual returns, \n",
+    "and their persistence due to overlap. Thus trends \n",
+    "can be easily discerned.\n",
+    "\n",
+    "It is also a visual aid for the geometric mean returns \n",
+    "which is most significant as investment metric. \n",
+    "\n",
+    "As usual, the *red line* plots the median, but \n",
+    "the **red dot** represents the latest point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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CzV29hCmqosdX5H7MUPzzV+SxXLJ07prm0Jsxsz2bLLsQuLDVfbtyat6T4D1I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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xacf0ac6c>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ::  Finished: boxplot-Assets_YoYm.png\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#  12-periods ahead forecasts use default alpha and beta values (robust update):\n",
-      "for i in range(len(ms)):\n",
-      "    print names[i] \n",
-      "    print holtfred(msd[ ms[i]], h=12 )\n",
-      "    #                   ^we use all available data to forecast\n",
-      "    print '------------------------------------'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Infl\n",
-        "    Forecast\n",
-        "0   1.000000\n",
-        "1   0.998873\n",
-        "2   0.999353\n",
-        "3   0.999834\n",
-        "4   1.000314\n",
-        "5   1.000794\n",
-        "6   1.001274\n",
-        "7   1.001755\n",
-        "8   1.002235\n",
-        "9   1.002715\n",
-        "10  1.003195\n",
-        "11  1.003675\n",
-        "12  1.004156"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "------------------------------------\n",
-        "Zero10\n",
-        "     Forecast\n",
-        "0   80.929358\n",
-        "1   83.871671\n",
-        "2   84.151155\n",
-        "3   84.430640\n",
-        "4   84.710124\n",
-        "5   84.989608\n",
-        "6   85.269093\n",
-        "7   85.548577\n",
-        "8   85.828062\n",
-        "9   86.107546\n",
-        "10  86.387030\n",
-        "11  86.666515\n",
-        "12  86.945999"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "------------------------------------\n",
-        "SPX\n",
-        "       Forecast\n",
-        "0   2105.200000\n",
-        "1   2146.564284\n",
-        "2   2161.256033\n",
-        "3   2175.947782\n",
-        "4   2190.639532\n",
-        "5   2205.331281\n",
-        "6   2220.023030\n",
-        "7   2234.714780\n",
-        "8   2249.406529\n",
-        "9   2264.098278\n",
-        "10  2278.790028\n",
-        "11  2293.481777\n",
-        "12  2308.173527"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "------------------------------------\n",
-        "XAU\n",
-        "       Forecast\n",
-        "0   1178.500000\n",
-        "1   1178.805087\n",
-        "2   1172.707278\n",
-        "3   1166.609470\n",
-        "4   1160.511661\n",
-        "5   1154.413853\n",
-        "6   1148.316045\n",
-        "7   1142.218236\n",
-        "8   1136.120428\n",
-        "9   1130.022619\n",
-        "10  1123.924811\n",
-        "11  1117.827002\n",
-        "12  1111.729194"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "------------------------------------\n",
-        "USD\n",
-        "      Forecast\n",
-        "0    93.018000\n",
-        "1    95.042219\n",
-        "2    95.923105\n",
-        "3    96.803990\n",
-        "4    97.684876\n",
-        "5    98.565762\n",
-        "6    99.446648\n",
-        "7   100.327534\n",
-        "8   101.208420\n",
-        "9   102.089306\n",
-        "10  102.970192\n",
-        "11  103.851077\n",
-        "12  104.731963"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "------------------------------------\n",
-        "Homes\n",
-        "         Forecast\n",
-        "0   214289.270829\n",
-        "1   212418.051023\n",
-        "2   213348.196411\n",
-        "3   214278.341799\n",
-        "4   215208.487187\n",
-        "5   216138.632575\n",
-        "6   217068.777963\n",
-        "7   217998.923351\n",
-        "8   218929.068739\n",
-        "9   219859.214127\n",
-        "10  220789.359515\n",
-        "11  221719.504904\n",
-        "12  222649.650292"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "------------------------------------\n"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Forecast log for monthly data\n",
-      "\n",
-      "Changing Holt-Winters *alpha* from 0.20 to 0.10 varies the forecast only slightly. The important parameter is *beta* to capture trend effects. Currently we shall rely on default Holt-Winters settings for robustness.\n",
-      "\n",
-      "\n",
-      "- 2014-09-01, Twelve-month Forecasts given data through 2014-07-01:\n",
-      "    - Inflation at 1.44%.\n",
-      "    - 10-y Bonds price -6.7%, thus rate +75 bp given zero10dur. \n",
-      "    - SPX +16.6% to 2280.\n",
-      "    - Gold tanks from 1286 to 1067. \n",
-      "    - USD +1.4% broadly.\n",
-      "\n",
-      "\n",
-      "- 2014-10-11, Twelve-month Forecasts given ten-year data, robust HW:\n",
-      "    - Inflation at 1.9%\n",
-      "    - Zero10 indicates slight downward pressure on interest rates.\n",
-      "    - SPX to 2239, but market seems skeptical.\n",
-      "    - Gold tanks to 1184 (region which we have seen just recently).\n",
-      "    - USD definitely has an upward bias against all FX, even NZD and AUD.\n",
-      "    - Home prices looking to increase from \\$203K to \\$220K \n",
-      "\n",
-      "\n",
-      "- 2015-05-28, Twelve-month Forecasts given data through 2015-03-01, robust HW:\n",
-      "    - Inflation at 0.5% (which seems dramatic).\n",
-      "    - Zero10 price increases by 8.92%, thus 10-year rate decreases by 100 bp.\n",
-      "    - SPX to 2322, but no metric says it's fair valued.\n",
-      "    - Gold to 1130, which would break support.\n",
-      "    - USD very strong, up 12% globally (QE-EU started, and possible Grexit).\n",
-      "    - Home prices looking to increase from \\$214K to \\$223K"
+    }
+   ],
+   "source": [
+    "#  Overlapping YoY percentage change, recently:\n",
+    "boxplot(mega[t0:], 'Assets YoYm')\n",
+    "#  where the red dot represents the latest point."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Red dot outside the mid-range box alerts us to unusual conditions. \n",
+    "Attention should also be paid to the extreme value \"slash\" marks \n",
+    "(where outliers are also revealed).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Geometric mean returns on non-overlapping periods\n",
+    "\n",
+    "David E. Shaw, famous for his proprietary hedge fund, remarked that \n",
+    "one of the most important equations in finance is the penalization \n",
+    "of arithmetic mean by one-half of variance:\n",
+    "\n",
+    "$ g = \\mu - (\\sigma^2 / 2) $\n",
+    "\n",
+    "which turns out to be our geometric mean return. It is an approximation, \n",
+    "by the way, but good enough to maximize, instead of considering \n",
+    "intricate mean-variance trade-off. We find it useful also as a metric \n",
+    "for economic variables.\n",
+    "\n",
+    "The source code shows us that georet() first gives us \n",
+    "the *geometric* mean return, followed by \n",
+    "the **arithmetic mean return and volatility**, \n",
+    "then finally, the yearly frequency used -- in list format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[1;31mSignature: \u001b[0m\u001b[0mgeoret\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdfx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myearly\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m256\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+       "\u001b[1;31mSource:\u001b[0m\n",
+       "def georet( dfx, yearly=256 ):\n",
+       "     '''Compute geometric mean return in a summary list.'''\n",
+       "     #  yearly refers to frequency, e.g. 256 for daily trading days,\n",
+       "     #                                    12 for monthly, \n",
+       "     #                                     4 for quarterly.\n",
+       "     #-alt  dflg = np.log( dfx )\n",
+       "     #-alt  dfpc = dflg.diff( periods=1 )\n",
+       "     dfpc = dfx.pct_change( periods=1 )\n",
+       "     #          ^instead of first difference of logged data,\n",
+       "     #           gives slightly higher arithmetic means.\n",
+       "     mean = dfpc.mean().values.tolist()[0] * yearly\n",
+       "     vari = dfpc.var().values.tolist()[0]  * yearly\n",
+       "     #          ^summary statistics methods, see\n",
+       "     #           McKinney, p.139, Table 5-10.\n",
+       "     geor = mean - (0.5*vari)\n",
+       "     #      ^arithmetic mean return penalized by risk, \n",
+       "     #      optimal choice under log utility.\n",
+       "     lst = [ geor, mean, vari ** 0.5 ]\n",
+       "     #                        ^^^^^^i.e. std sigma, or volatility.\n",
+       "     lst = [ round(i*100, 2) for i in lst ]\n",
+       "     #       ^[ geor, mean, volatility ] in readable % form.\n",
+       "     return lst + [ yearly ]\n",
+       "\u001b[1;31mFile:      \u001b[0m~/Dropbox/ipy/fecon235/lib/yi_1tools.py\n",
+       "\u001b[1;31mType:      \u001b[0mfunction"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#  How are we computing geometric mean returns?\n",
+    "#  Just add \"?\" or \"??\" to variables, procedures, etc.\n",
+    "#  to find out the details, e.g.\n",
+    "\n",
+    "georet??"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Homes': [3.39, 3.42, 2.53, 12],\n",
+       " 'Infl': [2.32, 2.32, 0.5, 12],\n",
+       " 'SPX': [7.58, 8.33, 12.25, 12],\n",
+       " 'USD': [0.19, 0.28, 4.13, 12],\n",
+       " 'XAU': [3.2, 4.05, 13.05, 12],\n",
+       " 'Zero10': [2.07, 2.33, 7.15, 12]}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#  Geometric mean returns, non-overlapping, annualized:\n",
+    "groupgeoret( msdf[t0:], yearly=12 )\n",
+    "    \n",
+    "#  Note that we applied groupgeoret to msdf, not mega.\n",
+    "#  Generally georet requires price levels.\n",
+    "#  groupgeoret is just georet for group dataframes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Note: the geometric returns do not include interest and dividend payouts for bonds and equities. Some observations in chronological order:\n",
+    "\n",
+    "- 2014-09-01, georet since 2010\n",
+    "    - Inflation at 1.7% which is below Fed target of 2%.\n",
+    "    - Total return on bonds, approx 2.52 + 2.33 = 4.85%\n",
+    "    - Total return on equity, approx. 11.9 + 2 = 13.9% -- very heated.\n",
+    "    - Gold indecisive about breaking 1260 LTS.\n",
+    "    - USD though at -0.69%, will strengthen given Draghi wanting weak EUR.\n",
+    " \n",
+    " \n",
+    " - 2014-10-11, georet since 2004\n",
+    "    - Inflation over ten years is running 2% annually.\n",
+    "    - Gold dominates over ten years.\n",
+    "    - Gold recently holds at 1180 triple local bottom.\n",
+    "\n",
+    "\n",
+    " - 2014-10-12, georet since 1988\n",
+    "    - Inflation in the long-run about 3% annually.\n",
+    "    - Bond price alone increases 2% annually (excludes interest income).\n",
+    "    - Gold at 1.73% does not keep up with inflation.\n",
+    "    - USD at break-even over the long-run.\n",
+    "    - Home prices have georet of 3.6%.\n",
+    "\n",
+    "\n",
+    "- 2015-05-27, georet since 1988\n",
+    "    - Inflation in the long-run drops 70 bp to about 2.3% annually.\n",
+    "    - Bond price continues its increase at 2% annually.\n",
+    "    - Gold at 3.4% reacting more to stronger USD (cf. correlation).\n",
+    "    - Home prices also have georet of 3.4% (but low 2.6% volatility).\n",
+    "\n",
+    "\n",
+    "- 2016-01-03, georet since 1988\n",
+    "    - Long-run inflation is 2.3% annually (Current Fed target: 2.0%).\n",
+    "    - Bond price increases at 2% annually (but Fed has just hiked rates!).\n",
+    "    - Equities at robust 7.6% annually (but ZIRP is finished).\n",
+    "    - Gold moving along at 2.9% (reflecting horrible 2015 year).\n",
+    "    - Nominal home prices at steady 3.4% per annum. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Forecasts using Holt-Winters method\n",
+    "\n",
+    "We forecast one-year ahead using the monthly data. \n",
+    "Note that the most current infl level is rebased to 1, \n",
+    "thus 1.02 would signify 2% increase."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "            Homes      Infl          SPX         USD          XAU     Zero10\n",
+       "0   216295.322178  0.999125  2021.400000   97.185000  1165.050000  83.183904\n",
+       "1   216719.918400  0.999473  2067.207192   99.116894  1126.538411  83.140498\n",
+       "2   217393.597328  1.000433  2063.544333   99.945673  1117.644357  83.208857\n",
+       "3   218067.276256  1.001392  2059.881474  100.774452  1108.750303  83.277216\n",
+       "4   218740.955184  1.002352  2056.218615  101.603231  1099.856249  83.345574\n",
+       "5   219414.634112  1.003312  2052.555756  102.432010  1090.962195  83.413933\n",
+       "6   220088.313040  1.004272  2048.892897  103.260789  1082.068141  83.482292\n",
+       "7   220761.991968  1.005232  2045.230038  104.089568  1073.174088  83.550651\n",
+       "8   221435.670896  1.006192  2041.567179  104.918347  1064.280034  83.619010\n",
+       "9   222109.349824  1.007152  2037.904320  105.747125  1055.385980  83.687369\n",
+       "10  222783.028752  1.008112  2034.241461  106.575904  1046.491926  83.755727\n",
+       "11  223456.707680  1.009072  2030.578602  107.404683  1037.597872  83.824086\n",
+       "12  224130.386608  1.010032  2026.915743  108.233462  1028.703819  83.892445"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#  These 12-periods ahead forecasts use default alpha and beta values\n",
+    "#  found to be optimal for a fixed Kalman filter.\n",
+    "\n",
+    "groupholtf( msdf, h=12 )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Forecast log for monthly data\n",
+    "\n",
+    "Changing Holt-Winters *alpha* from 0.20 to 0.10 varies the forecast only slightly. The important parameter is *beta* to capture trend effects. Currently we shall rely on default Holt-Winters settings for robustness.\n",
+    "\n",
+    "\n",
+    "- 2014-09-01, Twelve-month Forecasts given data through 2014-07-01:\n",
+    "    - Inflation at 1.44%.\n",
+    "    - 10-y Bonds price -6.7%, thus rate +75 bp given zero10dur. \n",
+    "    - SPX +16.6% to 2280.\n",
+    "    - Gold tanks from 1286 to 1067. \n",
+    "    - USD +1.4% broadly.\n",
+    "\n",
+    "\n",
+    "- 2014-10-11, Twelve-month Forecasts given ten-year data, robust HW:\n",
+    "    - Inflation at 1.9%\n",
+    "    - Zero10 indicates slight downward pressure on interest rates.\n",
+    "    - SPX to 2239, but market seems skeptical.\n",
+    "    - Gold tanks to 1184 (region which we have seen just recently).\n",
+    "    - USD definitely has an upward bias against all FX, even NZD and AUD.\n",
+    "    - Home prices looking to increase from \\$203K to \\$220K \n",
+    "\n",
+    "\n",
+    "- 2015-05-28, Twelve-month Forecasts given data through 2015-03-01, robust HW:\n",
+    "    - Inflation at 0.5% (which seems dramatic).\n",
+    "    - Zero10 price increases by 8.92%, thus 10-year rate decreases by 100 bp.\n",
+    "    - SPX to 2322, but no metric says it's fair valued.\n",
+    "    - Gold to 1130, which would break support.\n",
+    "    - USD very strong, up 12% globally (QE-EU started, and possible Grexit).\n",
+    "    - Home prices looking to increase from \\$214K to \\$223K\n",
+    "\n",
+    "\n",
+    "- 2016-01-03, Twelve-month Forecasts given data through 2015-10-01, robust HW:\n",
+    "    - Inflation at 1.1% (still below Fed target, see https://git.io/fed)\n",
+    "    - Bonds and equities, forecasting Zero10 and SPX, will be flat.\n",
+    "    - Gold to continue down trend, expected to fall to \\$941 in a year.\n",
+    "    - USD 10% higher in light of divergence between Fed hike(s) and ECB QE.\n",
+    "    - Home prices expected to be flat (but watch mortgage spreads)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# DAILY DATA, including major FX\n",
+    "\n",
+    "We examine bonds (zero coupon equivalent of 10-y Treasury), \n",
+    "equities (SPX), gold (XAU), EURUSD, and USDJPY \n",
+    "at higher frequency (daily) for the most recent developments. \n",
+    "\n",
+    "*Inflation, real trade-weighted USD index, and US home price data \n",
+    "have a slow monthly release schedule. \n",
+    "And for home price data, there is a three month lag.* "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#  Specify daily series of interest as a dictionary\n",
+    "#  where key is name, and value is its data code:\n",
+    "dsdic = { 'Zero10' : d4zero10, 'SPX' : d4spx, 'XAU' : d4xau, \n",
+    "          'EURUSD' : d4eurusd, 'USDJPY' : d4usdjpy }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ::  S&P 500 prepend successfully goes back to 1957.\n"
      ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "DAILY DATA"
+    }
+   ],
+   "source": [
+    "#  Download data into a dataframe:\n",
+    "dsdf = groupget( dsdic )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#  Construct the dega YoY percent dataframe:\n",
+    "dega = grouppc( dsdf, freq=256 )\n",
+    "#                         ^for daily data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#  Set the start date for daily series:\n",
+    "u0 = '2010-01-01'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ei/wPZleBygo4MXb1HdbSZPML784AXh+fr0Yajc9/BDz9vFuR12t4g+6ce5pm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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xab563ccc>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ::  Finished: boxplot-Assets_YoYd.png\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We examine bonds (zero coupon equivalent of 10-y Treasury), equities (SPX), gold (XAU), EURUSD, and USDJPY at higher frequency (daily) for the most recent developments. \n",
+    }
+   ],
+   "source": [
+    "#  Plot overlapping percentage changes:\n",
+    "boxplot( dega[u0:], 'Assets YoYd' )\n",
+    "\n",
+    "#  Note that the \"last\" timestamp will be more\n",
+    "#  recent than for the monthly series."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Although monthly data is more suitable for making long-term forecasts, \n",
+    "daily data is much more sensitive to immediate market perturbations. \n",
+    "\n",
+    "2016-01-03  Good example of the foregoing remark is the \n",
+    "reaction in the overall market due to the first Fed rate hike \n",
+    "in almost a decade on 2015-12-16. ZIRP, zero interest rate program, \n",
+    "has been terminated, along with US quantitative easing, \n",
+    "thus asset prices must adjust to financing constraints. \n",
+    "Note how equities and gold are now below their mid-range boxes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            EURUSD          SPX       USDJPY          XAU       Zero10\n",
+      "count  1565.000000  1565.000000  1565.000000  1565.000000  1565.000000\n",
+      "mean     -3.239333    14.108931     4.967449     4.568358     1.861880\n",
+      "std       9.126193    10.118387    11.876045    18.700101     6.470399\n",
+      "min     -23.984762    -6.776581   -14.310589   -29.210332   -13.248728\n",
+      "25%     -10.613876     7.172626    -6.285016    -9.629768    -1.231181\n",
+      "50%      -2.304728    13.200272     2.984473    -0.627657     2.419923\n",
+      "75%       3.984704    19.505195    15.422171    23.593619     5.626088\n",
+      "max      21.056554    65.300874    30.357599    52.361809    18.075299\n",
       "\n",
-      "[Inflation, real trade-weighted USD, and US home price data have a slow release schedule (monthly).] "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#  Specify daily series of interest:\n",
-      "ds = [ d4zero10, d4spx, d4xau, d4eurusd, d4usdjpy ]\n",
-      "names = [ 'Zero10', 'SPX', 'XAU', 'EURUSD', 'USDJPY' ]\n",
+      " ::  Index on min:\n",
+      "EURUSD   2015-03-11\n",
+      "SPX      2015-08-25\n",
+      "USDJPY   2011-03-17\n",
+      "XAU      2013-12-26\n",
+      "Zero10   2010-01-07\n",
+      "dtype: datetime64[ns]\n",
       "\n",
-      "#  Download into a dictionary:\n",
-      "dsd = {}\n",
-      "for i in ds:\n",
-      "    dsd[i] = getfred(i)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        " ::  S&P 500 prepend successfully goes back to 1957.\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#  Compute the YoY percentage change (overlaping):\n",
-      "dsdc = {}\n",
-      "for i in ds:\n",
-      "    dsdc[i] = pcent( dsd[i], 256 )"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#  Construct the YoY dataframe:\n",
-      "dega = paste( [ dsdc[i] for i in ds ] )\n",
-      "#  Give names to the columns for mega:\n",
-      "dega.columns = names"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#  Start from the recent:\n",
-      "u0 = '2010-01-01'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#  Overlapping YoY percentage change, recently:\n",
-      "boxplot( dega[u0:], 'Assets YoYd' )"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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-       "text": [
-        "<matplotlib.figure.Figure at 0xad2a078c>"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        " ::  Finished: boxplot-Assets_YoYd.png\n"
-       ]
-      }
-     ],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#  Geometric mean returns, non-overlapping, annualized:\n",
-      "for i in range(len(ds)):\n",
-      "    print names[i], georet( dsd[ds[i]][u0:] )"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Zero10 "
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[2.33, 2.61, 7.52, 256]\n",
-        "SPX "
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[11.36, 12.57, 15.53, 256]\n",
-        "XAU "
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[1.36, 2.89, 17.49, 256]\n",
-        "EURUSD "
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[-4.21, -3.77, 9.43, 256]\n",
-        "USDJPY "
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[4.96, 5.4, 9.33, 256]\n"
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Forecast log for daily data\n",
+      " ::  Index on max:\n",
+      "EURUSD   2011-06-06\n",
+      "SPX      2010-03-02\n",
+      "USDJPY   2013-05-28\n",
+      "XAU      2011-09-06\n",
+      "Zero10   2012-02-01\n",
+      "dtype: datetime64[ns]\n",
       "\n",
-      "- 2014-10-11, Really near-term picture is too bright for SPX while XAU looks dark. Sell stocks, and start to accumulate gold.\n",
+      " ::  Head:\n",
+      "              EURUSD        SPX    USDJPY        XAU     Zero10\n",
+      "T                                                              \n",
+      "2010-01-01  4.506344  22.574830  2.117389  27.081507 -11.463243\n",
+      "2010-01-04  6.846980  27.252204  2.548476  32.369431 -11.776234\n",
+      "2010-01-05  7.904398  30.595454  2.878992  35.822249 -11.858700\n",
+      "2010-01-06  9.137748  30.437376  3.212493  36.721113 -12.554147\n",
+      "2010-01-07  8.653408  35.492867  4.513889  37.583688 -13.248728\n",
+      "2010-01-08  9.645639  35.702942  3.471370  39.104938 -13.173018\n",
+      "2010-01-11  9.755361  34.919776  1.783143  38.290855 -12.321456\n",
       "\n",
+      " ::  Tail:\n",
+      "               EURUSD       SPX    USDJPY        XAU    Zero10\n",
+      "T                                                             \n",
+      "2015-12-23 -10.670281 -0.771985  1.340707 -11.422056 -0.618486\n",
+      "2015-12-24  -9.470292  0.101511  0.392157 -11.422056 -0.706616\n",
+      "2015-12-25  -9.470292  0.101511  0.392157 -11.422056 -0.706616\n",
+      "2015-12-28  -8.589263 -0.082596  0.083195  -8.852389 -1.058205\n",
+      "2015-12-29  -8.407451  2.859575  0.668673 -10.825000 -2.451747\n",
+      "2015-12-30  -8.579088  3.033541  1.978691 -12.414790 -2.969830\n",
+      "2015-12-31  -8.130288  0.890468  0.627510 -12.432879 -2.711703\n",
       "\n",
-      "- 2015-05-28, XAU georet changed from 2.6% to 1.6%. Zero10 monthly forecast is basically unchanged. Real rate is what matters for gold. USD stronger by 4.8% against both the EUR and JPY."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#  Show esp. correlations:\n",
-      "stats( dega[u0:] )"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "            Zero10          SPX          XAU       EURUSD       USDJPY\n",
-        "count  1426.000000  1426.000000  1426.000000  1426.000000  1426.000000\n",
-        "mean      1.902672    15.245862     5.972715    -2.194030     4.307208\n",
-        "std       6.762567     9.818755    18.978442     8.842339    12.024459\n",
-        "min     -13.248728    -5.657641   -29.210332   -23.984762   -14.310589\n",
-        "25%      -3.402214     9.723296    -8.516970    -8.651129    -6.838512\n",
-        "50%       2.684079    14.060072     2.285300    -0.706405     1.967362\n",
-        "75%       6.184020    20.287348    24.319358     4.492467    15.174316\n",
-        "max      18.075299    65.300874    52.361809    21.056554    30.357599\n",
-        "\n",
-        " ::  Index on min:\n",
-        "Zero10   2010-01-07\n",
-        "SPX      2011-10-03\n",
-        "XAU      2013-12-26\n",
-        "EURUSD   2015-03-11\n",
-        "USDJPY   2011-03-17\n",
-        "dtype: datetime64[ns]\n",
-        "\n",
-        " ::  Index on max:\n",
-        "Zero10   2012-02-01\n",
-        "SPX      2010-03-02\n",
-        "XAU      2011-09-06\n",
-        "EURUSD   2011-06-06\n",
-        "USDJPY   2013-05-28\n",
-        "dtype: datetime64[ns]\n",
-        "\n",
-        " ::  Head:\n",
-        "               Zero10        SPX        XAU    EURUSD    USDJPY\n",
-        "T                                                              \n",
-        "2010-01-01 -11.463243  22.574830  27.081507  4.506344  2.117389\n",
-        "2010-01-04 -11.776234  27.252204  32.369431  6.846980  2.548476\n",
-        "2010-01-05 -11.858700  30.595454  35.822249  7.904398  2.878992\n",
-        "2010-01-06 -12.554147  30.437376  36.721113  9.137748  3.212493\n",
-        "2010-01-07 -13.248728  35.492867  37.583688  8.653408  4.513889\n",
-        "2010-01-08 -13.173018  35.702942  39.104938  9.645639  3.471370\n",
-        "2010-01-11 -12.321456  34.919776  38.290855  9.755361  1.783143\n",
-        "\n",
-        " ::  Tail:\n",
-        "              Zero10       SPX        XAU     EURUSD     USDJPY\n",
-        "T                                                              \n",
-        "2015-06-11  1.966382  7.760938  -7.186454 -17.181396  21.006265\n",
-        "2015-06-12  2.237337  6.870700  -8.522815 -17.195301  21.003535\n",
-        "2015-06-15  2.418714  6.192973  -9.988571 -17.076402  20.794987\n",
-        "2015-06-16  2.782262  6.811338 -10.334983 -17.325094  21.089303\n",
-        "2015-06-17  2.419197  7.715977 -10.656049 -17.250515  21.658670\n",
-        "2015-06-18  1.966775  8.252489  -8.726030 -16.380701  20.756757\n",
-        "2015-06-19  2.419923  7.805459  -8.259958 -16.715650  20.696439"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        " ::  Correlation matrix:\n",
-        "          Zero10       SPX       XAU    EURUSD    USDJPY\n",
-        "Zero10  1.000000 -0.750870  0.418415 -0.575325 -0.417933\n",
-        "SPX    -0.750870  1.000000 -0.152196  0.404478  0.159153\n",
-        "XAU     0.418415 -0.152196  1.000000  0.074339 -0.866292\n",
-        "EURUSD -0.575325  0.404478  0.074339  1.000000 -0.093702\n",
-        "USDJPY -0.417933  0.159153 -0.866292 -0.093702  1.000000"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "- 2015-05-29, Suprisingly, very little correlation between EURUSD and USDJPY: -6%. Gold appears more correlated with USDJPY at -87% than EURUSD at +6%"
+      " ::  Correlation matrix:\n",
+      "          EURUSD       SPX    USDJPY       XAU    Zero10\n",
+      "EURUSD  1.000000  0.471964 -0.163779  0.158736 -0.524794\n",
+      "SPX     0.471964  1.000000  0.090843 -0.052154 -0.687276\n",
+      "USDJPY -0.163779  0.090843  1.000000 -0.859083 -0.400329\n",
+      "XAU     0.158736 -0.052154 -0.859083  1.000000  0.409829\n",
+      "Zero10 -0.524794 -0.687276 -0.400329  0.409829  1.000000\n"
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "stats(dega[u0:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- 2015-05-29, Suprisingly, very little correlation between EURUSD and USDJPY: -6%. Gold appears more correlated with USDJPY at -87% than EURUSD at +6%\n",
+    "\n",
+    "- 2016-01-05, Given the latest Fed hike, the correlation to watch is between equities and bonds (-0.69 SPX and Zero10)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "            EURUSD      SPX  USDJPY      XAU     Zero10\n",
+       "T                                                      \n",
+       "2015-12-23  1.0875  2064.29  120.94  1068.25  81.685673\n",
+       "2015-12-24  1.0955  2060.99  120.32  1068.25  81.830579\n",
+       "2015-12-25  1.0955  2060.99  120.32  1068.25  81.830579\n",
+       "2015-12-28  1.0983  2056.50  120.30  1068.25  81.903134\n",
+       "2015-12-29  1.0916  2078.36  120.44  1070.10  81.324593\n",
+       "2015-12-30  1.0912  2063.36  120.60  1060.00  81.396674\n",
+       "2015-12-31  1.0859  2043.94  120.27  1060.00  81.685673"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#  What are the latest daily prices?\n",
+    "tail( dsdf )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'EURUSD': [-4.54, -4.08, 9.64, 256],\n",
+       " 'SPX': [9.92, 11.16, 15.76, 256],\n",
+       " 'USDJPY': [4.19, 4.63, 9.3, 256],\n",
+       " 'XAU': [-0.41, 1.08, 17.27, 256],\n",
+       " 'Zero10': [2.28, 2.56, 7.48, 256]}"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#  Geometric mean returns, non-overlapping, annualized:\n",
+    "groupgeoret( dsdf[u0:], yearly=256 )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Closing remarks on daily data\n",
+    "\n",
+    "- 2014-10-11, Really near-term picture is too bright for SPX while XAU looks dark. Sell stocks, and start to accumulate gold.\n",
+    "\n",
+    "\n",
+    "- 2015-05-28, XAU georet changed from 2.6% to 1.6%. Zero10 monthly forecast is basically unchanged. Real rate is what matters for gold. USD stronger by 4.8% against both the EUR and JPY.\n",
+    "\n",
+    "\n",
+    "- 2016-01-03, XAU georet changed from 1.6% to -0.41%, commodities including oil going through a bear market. Bonds have not sold off despite 2015-12-16 Fed rate hike, probably due to world appetite for USD which is stronger by about 4.3% against EUR and JPY. SPX looks vulnerable given the past maxims about rate hikes, but the Fed is actually still very accomodative.\n",
+    "\n",
+    "[ ] TODO: notebook on r\\* the so-called natural interest rate."
+   ]
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}