diff --git a/nb/fred-oil-brent-wti.ipynb b/nb/fred-oil-brent-wti.ipynb index 1da4090..ecbcb3c 100644 --- a/nb/fred-oil-brent-wti.ipynb +++ b/nb/fred-oil-brent-wti.ipynb @@ -1,785 +1,817 @@ { - "metadata": { - "name": "", - "signature": "sha256:1116ddb949417e1e93677aea889dec9a8d79cfaf4ed7b667bad291f8a7e481c7" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Oil: Brent vs. West Texas Intermediate (WTI), and their weighted price" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We examine the history of oil prices, and their spreads. Real prices give additional insight, along with some of the statistical characteristics used in financial economics.\n", - "\n", - "[Unless otherwise noted, the price per barrel is in U.S. dollars. Near the conclusion, we use USD index units.]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Dependencies:*\n", - "\n", - " - Linux, bash [not crucial, cross-platform prefered]\n", - " - Python: matplotlib, pandas [recommend Anaconda distribution]\n", - " - Modules: yi_1tools, yi_plot, yi_timeseries, yi_fred\n", - " \n", - "*CHANGE LOG*\n", - "\n", - " 2015-05-26 Code review and revision.\n", - " 2014-10-09 First version for oil, using 2014-09-28 Template." + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Crude Oil :: Brent vs. West Texas Intermediate (WTI)\n", + "\n", + "We examine the history of crude oil prices, and their spreads.\n", + "A Boltzmann portfolio is computed for *optimal* financial positions.\n", + "\n", + "Deflated prices give additional insight, along with some of the\n", + "statistical tools useful in financial economics.\n", + "\n", + "***Although WTI is more desirable than Brent from a petrochemical\n", + "perspective, that preference is reversed when the metrics\n", + "are financial.***\n", + "\n", + "Unless otherwise noted, the price per barrel is in U.S. dollars.\n", + "The source of the data is the U.S. Department of Energy via FRED.\n", + "We will use Federal Reserve USD index units to compute oil prices in non-dollar terms." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Petrochemical background\n", + "\n", + "Before exploring our financial metrics, let's get familiar\n", + "with the physical aspects.\n", + "\n", + "> **Crude oil is arguably the most crucial commodity in the world today.**\n", + "An important issue is the different benchmarks for crude oil prices. \n", + "\n", + "> The American Petroleum Institute ***API gravity*** is a measure that is\n", + "used to compare a petroleum liquid's density to water.\n", + "This scale generally falls between 10 and 70, with \"light\" crude oil\n", + "typically having an API gravity on the higher side of the range, while\n", + "\"heavy\" oil has a reading that falls on the lower end of the range.\n", + "\n", + "> The sulfur content of petroleum must also be considered.\n", + "***Sulfur content of 0.50% is a key benchmark.***\n", + "When oil has a total sulfur level greater than the benchmark, it is considered \"sour.\"\n", + "Sulfur content less than the benchmark indicates that an oil is \"sweet.\"\n", + "Sour crude oil is more prevalent than its sweet counterpart and comes from\n", + "oil sands in Canada, the Gulf of Mexico, some South American nations,\n", + "as well as most of the Middle East.\n", + "Sweet crude is typically produced in the central United States,\n", + "the North Sea region of Europe, and much of Africa and the Asia Pacific region.\n", + "*End users generally prefer sweet crude as it requires less processing\n", + "to remove impurities than its sour counterpart.*\n", + "\n", + "> [Edited source: Daniela Pylypczak-Wasylyszyn](http://commodityhq.com/education/crude-oil-guide-brent-vs-wti-whats-the-difference)\n", + "\n", + "Light and sweet forms of crude oil are generally valued higher while\n", + "heavy and sour types often trade at a discount.\n", + "These two key factors distinguish the two major benchmarks for\n", + "world oil prices: West Texas Intermediate (WTI) and Brent crude oil.\n", + "\n", + "***WTI is generally lighter and sweeter than Brent, but the supply of each\n", + "can differ considerably over time, thus their price difference will vary.***\n", + "\n", + "WTI is refined mostly in the Midwest and Gulf Coast regions of the United States,\n", + "while Brent oil is typically refined in Northwest Europe.\n", + "\n", + "#### Approximate characteristics:\n", + "- **Brent**: API gravity 38.06, sulfur 0.37%\n", + "- **WTI**: API gravity 39.6, sulfur 0.24%\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Dependencies:*\n", + "\n", + "- Repository: https://github.com/rsvp/fecon235\n", + "\n", + "*CHANGE LOG*\n", + "\n", + " 2017-08-08 Fix #2 and introduce Boltzmann portfolio of oils.\n", + " 2015-05-26 Code review and revision.\n", + " 2014-10-09 First version for oil, using 2014-09-28 Template." + ] + }, + { + "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 2.7.13\n", + " :: IPython 5.1.0\n", + " :: jupyter_core 4.2.1\n", + " :: notebook 4.1.0\n", + " :: matplotlib 1.5.1\n", + " :: numpy 1.11.0\n", + " :: scipy 0.17.0\n", + " :: sympy 1.0\n", + " :: pandas 0.19.2\n", + " :: pandas_datareader 0.2.1\n", + " :: Repository: fecon235 v5.17.0722 develop\n", + " :: Timestamp: 2017-08-09T09:31:30Z\n", + " :: $pwd: /media/yaya/virt15h/virt/dbx/Dropbox/ipy/fecon235/nb\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# NOTEBOOK settings and system details: [00-tpl v14.12.21]\n", + } + ], + "source": [ + "# PREAMBLE-p6.15.1223d :: Settings and system details\n", + "from __future__ import absolute_import, print_function, division\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", + "from IPython.display import HTML # useful for snippets\n", + "# e.g. HTML('')\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": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Define dictionary for dataframe:\n", + "oils4d = { 'Brent' : d4brent, 'WTI' : d4wti }" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Retrieve data:\n", + "oils = groupget( oils4d )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## BoW spread := Brent - WTI\n", + "\n", + "***Brent over WTI spread***" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Define price variables individually for convenience:\n", + "brent = oils['Brent']\n", + "wti = oils['WTI']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Define BoW: Brent over WTI spread:\n", + "bow = todf( brent - wti, 'BoW' )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The difference between Brent and WTI is not superficial in the 21st century. Brent over WTI, bow, can represent over 20% of the underlying oil price!" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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6iqTdRQFWXdWtt922+vpeey3YPu88uPPO4jwPPJA/92qltIxhr3ef5ccfd+s/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\nCC/lWsKFv23YN/zdd84Fcs0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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot( bow )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define \"Oil\" as weighted average price\n", + "\n", + "When BoW is non-zero, the price of \"Oil\" is ambiguous,\n", + "thus we define a weighted average price of oil\n", + "between Brent and WTI.\n", + "\n", + "Note that the composition of Brent oil benchmark no longer\n", + "really represents Brent as an location." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Set wtbrent, the primary weight for Oil:\n", + "wtbrent = 0.50\n", + "# 0.50 represents the mean\n", + "# ==================================\n", + "wtwti = 1 - wtbrent\n", + "\n", + "# Weighted average:\n", + "oil = todf( (wtbrent * brent) + (wtwti * wti), 'Oil' )" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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q+hnwJ2AGzpB/rapjgH6qOi+7z1xgvVoILYb5S6PH9EZPHJp///vy9g/WnkvV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\nyImaqO+J7t1z3SS33JKbH4w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ll7d+TEvlLluWm9+jh+rvfudv/+lP4WX+5jfFtW+zTe1q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\nYbQR/j8z0wHAcGc+GQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot( oil )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**The Great Recession plunge from \\$140 to \\$40 is extraordinary.**\n", + "*But so is the post-2014 crash which wiped out legendary traders.*\n", + "The volatility is around 33% annualized, as we shall see later.\n", + "\n", + "[Using equal weights, we have created two synthetic series called `d4oil` and `m4oil`\n", + "for future convenience.]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Brent WTI Oil\n", + "count 7879.000000 7879.000000 7879.000000\n", + "mean 45.011701 44.248576 44.630138\n", + "std 33.469778 30.000004 31.660557\n", + "min 9.100000 10.820000 9.960000\n", + "25% 18.500000 19.850000 19.177500\n", + "50% 28.560000 30.080000 29.275000\n", + "75% 64.985000 64.870000 64.987500\n", + "max 143.950000 145.310000 144.630000\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", + " :: Index on min:\n", + "Brent 1998-12-10\n", + "WTI 1998-12-10\n", + "Oil 1998-12-10\n", + "dtype: datetime64[ns]\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", + " :: Index on max:\n", + "Brent 2008-07-03\n", + "WTI 2008-07-03\n", + "Oil 2008-07-03\n", + "dtype: datetime64[ns]\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('')\n", + " :: Head:\n", + " Brent WTI Oil\n", + "T \n", + "1987-05-20 18.63 19.75 19.190\n", + "1987-05-21 18.45 19.95 19.200\n", + "1987-05-22 18.55 19.68 19.115\n", + " :: Tail:\n", + " Brent WTI Oil\n", + "T \n", + "2017-07-27 50.67 49.05 49.86\n", + "2017-07-28 52.00 49.72 50.86\n", + "2017-07-31 51.99 50.21 51.10\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", + " :: Correlation matrix:\n", + " Brent WTI Oil\n", + "Brent 1.000000 0.990614 0.997901\n", + "WTI 0.990614 1.000000 0.997386\n", + "Oil 0.997901 0.997386 1.000000\n" + ] + } + ], + "source": [ + "# Temporarily combine data for Brent, WTI, and weighted Oil:\n", + "stats( paste([oils, oil]) )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The very tight correlation between Brent and WTI (> 99%)\n", + "can mask the potential turbulence of the BoW spread. \n", + "\n", + "## BoW spread as a function of Oil price" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " :: FIRST variable:\n", + "count 7879.000000\n", + "mean 0.763125\n", + "std 5.557796\n", + "min -22.180000\n", + "25% -1.810000\n", + "50% -1.160000\n", + "75% 0.685000\n", + "max 29.590000\n", + "Name: BoW, dtype: float64\n", "\n", - "# MATH display, use %%latex, rather than the following:\n", - "# from IPython.display import Math\n", - "# from IPython.display import Latex\n", + " :: SECOND variable:\n", + "count 7879.000000\n", + "mean 44.630138\n", + "std 31.660557\n", + "min 9.960000\n", + "25% 19.177500\n", + "50% 29.275000\n", + "75% 64.987500\n", + "max 144.630000\n", + "Name: Oil, dtype: float64\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", - "Tue May 26 10:26:40 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/ipy/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": 1, - "metadata": {}, - "source": [ - "Brent vs. WTI" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Retreive daily data:\n", - "brent = getfred( d4brent )\n", - "wti = getfred( d4wti )" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# BoW: Brent over WTI spread:\n", - "bow = todf( brent - wti )" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 4 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "plotfred( bow )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### The difference between Brent and WTI is not superficial. BoW can represent over 20% of the underlying oil price!" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Let's look at BoW trend since 1999:\n", - "plotfred( trend( bow ) )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " :: regresstime slope = 0.00140233726321\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Since the slope uses daily data, we annualize it:\n", - "0.0014023 * 256" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 7, - "text": [ - "0.3589888" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Over the long haul, we can **anticipate BoW to increase $0.36 annually per barrel**, though there will be very large deviations around the expected \\$6 spread. Note that the composition of Brent oil benchmark no longer really represents Brent as an location." - ] - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Oil: weighted average price" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Set wtbrent, the primary weight for OIL:\n", - "wtbrent = 0.50\n", - "# 0.50 represents the mean\n", - "# ==================================\n", - "wtwti = 1 - wtbrent\n", - "oil = todf( (wtbrent * brent) + (wtwti * wti) )" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 8 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "plotfred( oil )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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FDPXqDAVSjPQ0DKMl0Ls3fPll7vWbY8ivu86NV3/oIZgxw7k7/Lb8UTP+ggxH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\nZ/jss8Q8MwlNWbEicSSPSPp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- "text": [ - "" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**The Great Recession plunge from \\$140 to \\$40 is extraordinary.** " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[At equal weights, we have created two convenience series called d4oil and m4oil.]" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "stats( oil )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " Y\n", - "count 7304.000000\n", - "mean 44.469187\n", - "std 32.819087\n", - "min 9.960000\n", - "25% 18.948750\n", - "50% 27.170000\n", - "75% 68.925000\n", - "max 144.630000\n", - "\n", - " :: Index on min:\n", - "Y 1998-12-10\n", - "dtype: datetime64[ns]\n", - "\n", - " :: Index on max:\n", - "Y 2008-07-03\n", - "dtype: datetime64[ns]\n", - "\n", - " :: Head:\n", - " Y\n", - "T \n", - "1987-05-20 19.190\n", - "1987-05-21 19.200\n", - "1987-05-22 19.115\n", - "1987-05-25 19.140\n", - "1987-05-26 18.990\n", - "1987-05-27 18.990\n", - "1987-05-28 18.940" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "\n", - " :: Tail:\n", - " Y\n", - "T \n", - "2015-05-08 61.615\n", - "2015-05-11 61.025\n", - "2015-05-12 62.905\n", - "2015-05-13 63.415\n", - "2015-05-14 62.735\n", - "2015-05-15 62.210\n", - "2015-05-18 62.295\n", - "\n", - " :: Correlation matrix:\n", - " Y\n", - "Y 1" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 10 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Oil on 1998-12-10 at $9.96 is our record LOW" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "tmin = '1998-12-10'" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 11 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "georet( oil[tmin:] )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 12, - "text": [ - "[10.97, 16.3, 32.66, 256]" - ] - } - ], - "prompt_number": 12 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "May 2015: The geometric return since tmin is around 11% with volatility of 33% -- both very high relative to traditional assets." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**The technical support for the 1998 uptrend on the chart is currently $45.40 (on 2015-01-29).** " - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Oil trend since 1998-12-10" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "oiltrend = trend( oil[tmin:] )\n", - "plotfred( oiltrend )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " :: regresstime slope = 0.0221070094873\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 13 - }, - { - "cell_type": "heading", - "level": 5, - "metadata": {}, - "source": [ - "Next the detrended series using percentage deviation since tmin:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "plotfred( detrendpc(oil[tmin:]) )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " :: regresstime slope = 0.0221070094873\n" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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2tI07P++8wgY96n7p1i17Wffu3odeL2bODDMUlvKVFDWCc+eGMdCWxYvTa8vF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\nteMXtGrHr+EkogU1+8VTSsv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- "text": [ - "" - ] - } - ], - "prompt_number": 14 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Oil prices can easily go +/- 40% off their statistical trend (about 1.2 std). \n", + " :: CORRELATION\n", + "0.625773327194\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y R-squared: 0.392\n", + "Model: OLS Adj. R-squared: 0.392\n", + "Method: Least Squares F-statistic: 5070.\n", + "Date: Wed, 09 Aug 2017 Prob (F-statistic): 0.00\n", + "Time: 02:31:49 Log-Likelihood: -22736.\n", + "No. Observations: 7879 AIC: 4.548e+04\n", + "Df Residuals: 7877 BIC: 4.549e+04\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [95.0% Conf. Int.]\n", + "------------------------------------------------------------------------------\n", + "Intercept -4.1395 0.084 -49.036 0.000 -4.305 -3.974\n", + "X 0.1099 0.002 71.203 0.000 0.107 0.113\n", + "==============================================================================\n", + "Omnibus: 1718.842 Durbin-Watson: 0.053\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 10202.633\n", + "Skew: 0.913 Prob(JB): 0.00\n", + "Kurtosis: 8.267 Cond. No. 94.6\n", + "==============================================================================\n", "\n", - "From the +100% deviation on 2008-07-03, we see that our record HIGH of $144.63 was an one-time explosive blow-off. " + "Warnings:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# So discount 40% off the trend, \n", - "# to illustrate statistical downside:\n", - "tailvalue( oiltrend ) * 0.60" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 15, - "text": [ - "65.95000820390936" - ] - } - ], - "prompt_number": 15 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Real oil prices" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# We change the sampling frequency to match inflation data:\n", - "oilmth = monthly( oil )\n", - "defl = getfred( m4defl )\n", - "oildefl = todf( oilmth * defl )" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 16 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "plotfred( oildefl )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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CDTfU1HWJBfWhQ2376NGw776p9Rc7228PbdpYJzRkrj/blvpXwBagpYhsA1oC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\nz/81Fy+2/PKOHTM7bsIE0zd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- "text": [ - "" - ] - } - ], - "prompt_number": 17 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here the supportive 1998 uptrend is broken by the recent crash in prices. The level support from the 1990's is around \\$20 in real terms." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Oil price ex-USD" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we are interested in how oil prices appear outside the United States. A basket of trade-weighted currencies against USD helps our foreign exchange viewpoint." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# rtb is the real trade-weighted USD index,\n", - "# computed monthly by the Federal Reserve.\n", - "rtb = getfred( m4usdrtb )\n", - "oilrtb = todf( oil / rtb )" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 18 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# cf. \"oil\" in dollars\n", - "plotfred( oilrtb )\n", - "# ^in index units" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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+aMF7yBBbGi5+dGOyIB7G9yC+YdM9ccdxmoyJE2H48NzOaUzDZlUV9OhhPvgn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\n/CLCjjvCW29V8thjxaXPt5t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- "text": [ - "" - ] - } - ], - "prompt_number": 19 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "No big suprises here. Oil from world-ex-USD perspective looks very similar to the dollar-only perspective." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "BoW spread as a function of Oil" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Do the regression:\n", - "stat2( bow['Y'], oil['Y'] )" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " :: FIRST variable:\n", - "count 7304.00000\n", - "mean 0.71444\n", - "std 5.75087\n", - "min -22.18000\n", - "25% -1.88000\n", - "50% -1.25000\n", - "75% -0.02750\n", - "max 29.59000\n", - "Name: Y, dtype: float64" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "\n", - " :: SECOND variable:\n", - "count 7304.000000\n", - "mean 44.469187\n", - "std 32.819087\n", - "min 9.960000\n", - "25% 18.948750\n", - "50% 27.170000\n", - "75% 68.925000\n", - "max 144.630000\n", - "Name: Y, dtype: float64" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "\n", - " :: CORRELATION\n", - "0.627241758244\n", - "\n", - "-------------------------Summary of Regression Analysis-------------------------\n", - "\n", - "Formula: Y ~ + \n", - "\n", - "Number of Observations: 7304\n", - "Number of Degrees of Freedom: 2\n", - "\n", - "R-squared: 0.3934\n", - "Adj R-squared: 0.3933\n", - "\n", - "Rmse: 4.4792\n", - "\n", - "F-stat (1, 7302): 4736.2260, p-value: 0.0000\n", - "\n", - "Degrees of Freedom: model 1, resid 7302\n", - "\n", - "-----------------------Summary of Estimated Coefficients------------------------\n", - " Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5%\n", - "--------------------------------------------------------------------------------\n", - " x 0.1099 0.0016 68.82 0.0000 0.1068 0.1130\n", - " intercept -4.1732 0.0883 -47.28 0.0000 -4.3462 -4.0002\n", - "---------------------------------End of Summary---------------------------------\n" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 31 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Does the price of oil influence the BoW spread? Not much, the correlation is about 63%. And linear regression does not show anything reliable. Maybe: *take 10% of the oil price and subtract $4 to roughly guess at BoW.*" + } + ], + "source": [ + "# Do the regression:\n", + "stat2( bow['BoW'], oil['Oil'] )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Does the price of Oil influence the BoW spread?\n", + "Not much, the correlation is about 63%, and the linear regression is not robust.\n", + "\n", + "*To roughly estimate BoW*: take 11% of Oil price and subtract $4.\n", + "So higher Oil prices imply greater Brent premium over WTI." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Record LOW: Oil on 1998-12-10 at 9.96 USD\n", + "\n", + "We use that date to define the variable `tmin`." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "tmin = '1998-12-10'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**What is the geometric mean rate since tmin?**" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[1.9017090903568512,\n", + " 8.6098951245776831,\n", + " 33.342833957643094,\n", + " 7.4891318245988723,\n", + " 256,\n", + " 4862]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gemrat( oil[tmin:], yearly=256 )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "May 2015: The geometric mean rate since tmin is around 11%\n", + "with volatility of 33% -- both very high relative to traditional assets.\n", + "\n", + "Aug 2017: The geometric mean rate since tmin is down to around 2%\n", + "with volatility still of 33%. The kurtosis of 7.5 is very high\n", + "since it would be 3 for a Gaussian distribution:\n", + "hence oil returns are leptokurtotic, i.e. have \"fat tails.\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Oil trend since tmin on 1998-12-10\n", + "\n", + "Given the high price volatility,\n", + "what seems to be underlying trend?" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " :: regresstime slope = 0.0131256573363\n" ] }, { - "cell_type": "markdown", + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "# Concluding remarks\n", - "\n", - "The bottom of the barrel is in the region of \\$45.40 to \\$66 with respect to our weighted oil series. The lower range derives from the technical uptrend dating back to 1998, which is also the year of our record low. The upper range is an estimate derived from statistical trend deviation.\n", - "\n", - "A definitive break of the \\$45.40 mark would look downward at the $20 support in real dollars.\n", - "\n", - "Before taking a position in oil, carefully evaluate the premium of Brent over WTI.\n", - "\n", - "### Caveats\n", - "\n", - "- A complete analysis would include the impact of shale oil.\n", - "\n", - "- If ISIS becomes dominate over oil fields, expect some minor supply at half the market price." - ] + "output_type": "display_data" + } + ], + "source": [ + "plot( trend(todf(oil[tmin:])) )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Oil prices can easily go +/- 40% off their statistical trend (about 1.2 std).\n", + "So the trend is very deceptive.\n", + "\n", + "Aug 2017: the trend indicates \\$92 oil, but the\n", + "current market average is in fact around \\$50." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Boltzmann portfolio of oils\n", + "\n", + "Given the BoW spread, correlations, volatilities, and overall uptrend --\n", + "what would be an *optimal* portfolio structure out-of-sample?\n", + "\n", + "We shall compute a **Boltzmann portfolio**\n", + "(see https://git.io/boltz1 for details),\n", + "consisting of Brent and WTI, which uses cross-entropy\n", + "as its foundation for best geometric growth at minimized risk.\n", + "\n", + "The short-side shall be practically unrestricted (floor=level=-25)\n", + "since we assume that crude oil derivatives will be used\n", + "to implement the strategy.\n", + "\n", + "The history since *tmin* will be the base,\n", + "but the user can modify the date interactively\n", + "(see https://git.io/boltz2 for details on sequential decisions)." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[0.7845, [[0.9814, 0.8337, 'Brent'], [0.0186, -1.82, 'WTI']]]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "prtf = boltzportfolio( oils[tmin:], temp=55, floor=-25, level=-25, n=4 )\n", + "prtf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Brent has a higher geometric mean rate (0.83% vs. -1.82 for WTI),\n", + "hence ***0.98 of the portfolio's notional principal is dedicated to Brent***,\n", + "and 0.02 towards WTI.\n", + "The expected geometric mean rate of this particular\n", + "Boltzmann portfolio is a mere 0.78%,\n", + "hardly better than a Treasury note.\n", + "\n", + "In a Boltzmann portfolio, the second and fourth centralized moments\n", + "are taken into account to properly access risk,\n", + "and to adjust the geometric mean rates.\n", + "\n", + "This component analysis is more accurate out-of-sample than a\n", + "treatment of a single time-series such as the weighted average." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deflated oil prices\n", + "\n", + "We use our monthly deflator consisting of CPI and PCE,\n", + "both core and headline versions for each,\n", + "to compute ***real*** oil prices." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# First change the sampling frequency to match inflation data:\n", + "oilmth = todf(monthly( oil ))\n", + "defl = todf(get( m4defl ))\n", + "oildefl = todf( oilmth * defl )" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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R5o0b4aST4PHH4Zhj4MknYwf0xYsL1yCaq/uifXt7sCW71rnAe+gez3bG11/D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\nqiKatm3h5JPhySdt1L5f/tJsmaCsrli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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Deflated Oil price:\n", + "plot( oildefl )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The supportive 1998 uptrend is broken by the post-2014 crash in prices.\n", + "The level support from the 1990's is around \\$20 in current US dollars." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Oil price ex-USD\n", + "\n", + "Here we are interested in how oil prices appear when priced in\n", + "foreign currencies (outside the United States).\n", + "A basket of trade-weighted currencies against USD helps\n", + "our foreign exchange viewpoint." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# rtb is the real trade-weighted USD index,\n", + "# computed monthly by the Federal Reserve.\n", + "rtb = get( m4usdrtb )\n", + "oilrtb = todf( oilmth / rtb )" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Oil priced in rtb index units:\n", + "plot( oilrtb )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "No big suprises here. Oil from world-ex-USD perspective looks very similar to the dollar-only perspective." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Concluding remarks\n", + "\n", + "Although WTI has more desirable petrochemical properties than Brent oil,\n", + "our analysis of their prices reveal that Brent is preferable over WTI\n", + "in the context of a financial portfolio -- denominated in practically\n", + "any currency.\n", + "\n", + "Before taking a position in oil, carefully evaluate the premium of Brent over WTI,\n", + "i.e. the BoW spread.\n", + "\n", + "The post-2014 crash in oil prices portends a downward look at\n", + "the \\$20 support level in current U.S. dollars especially in view\n", + "of the caveats listed below.\n", + "This would be a major concern for oil companies worldwide\n", + "(for example, in August 2017 BP announced their break-even point\n", + "was \\$47 with respect to the price of oil). \n", + "\n", + "\n", + "### Caveats\n", + "\n", + "- A complete analysis would include the impact of shale oil and *alternate energy sources*.\n", + "\n", + "- As electric cars become more popular, the demand for petroleum will obviously diminish.\n", + "\n", + "- If ISIS becomes dominate over oil fields, expect some minor supply at half the market price.\n", + "\n", + "- Be attentive to offshore storage of crude oil, literally on non-active tanker ships with no destinations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "*Dedicated to recently retired Andrew Hall: the name of the game has changed.*" + ] } - ] -} \ 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": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}