diff --git a/nb/qdl-xau-contango.ipynb b/nb/qdl-xau-contango.ipynb index 5c47b0f..a974cf4 100644 --- a/nb/qdl-xau-contango.ipynb +++ b/nb/qdl-xau-contango.ipynb @@ -27,7 +27,20 @@ "\n", "The *London Bullion Market Association* ceased publishing daily data \n", "on their *Gold Forward Offered Rate* (**GOFO**), as of 30 January 2015 -- \n", - "so we develop an observable proxy called *tango*. \n" + "so we develop an observable proxy called *tango*. \n", + "\n", + "During 2015 we detected strong *negative* correlation between price change and tango, \n", + "however, in 2016 that strong correlation became *positive* -- \n", + "thus we conclude the relationship is spurious. \n", + "The observed correlations are mere artifacts \n", + "which do not imply any significant economic relationships.\n", + "\n", + "Tango as an indicator is **not** insightful for price changes \n", + "whenever it is at the extremes of its expected range. \n", + "This can be verified by comparing various commits over time \n", + "of this notebook in the repository.\n", + "\n", + "Short URL: https://git.io/xau-contango" ] }, { @@ -36,12 +49,15 @@ "source": [ "*Dependencies:*\n", "\n", - " - Linux, bash [not critical, generally cross-platform]\n", - " - Python: matplotlib, pandas [recommend Anaconda distribution]\n", - " - Modules: fecon\n", + "- fecon235 repository https://github.com/rsvp/fecon235\n", + "- Python: matplotlib, pandas\n", " \n", "*CHANGE LOG*\n", "\n", + " 2016-12-04 Solve #2 by v5 and PREAMBLE-p6.16.0428 upgrades. \n", + " Switch from fecon to fecon235 for main import module. \n", + " Minor edits given more data and change in futures basis.\n", + " Previous conclusion is negated. Correlation is artificial.\n", " 2015-10-11 Code review.\n", " 2015-09-11 First version." ] @@ -52,77 +68,65 @@ "metadata": { "collapsed": false }, + "outputs": [], + "source": [ + "from fecon235.fecon235 import *" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - " :: TIMESTAMP of last notebook execution:\n", - "Sun Oct 11 10:18:42 PDT 2015\n", - " :: IPython version:\n", - "3.2.1\n", - " :: pandas version:\n", - "0.16.2\n", - " :: Working directory (set as $workd):\n", - "/home/yaya/Dropbox/share/git/nous/fecon235/nb\n", - "\n" + " :: Python 2.7.11\n", + " :: IPython 4.2.0\n", + " :: jupyter_core 4.1.0\n", + " :: notebook 4.1.0\n", + " :: matplotlib 1.5.1\n", + " :: numpy 1.10.4\n", + " :: pandas 0.18.0\n", + " :: pandas_datareader 0.2.1\n", + " :: Repository: fecon235 v5.16.1107 develop\n", + " :: Timestamp: 2016-12-04, 17:25:44 UTC\n", + " :: $pwd: /media/yaya/virt15h/virt/dbx/Dropbox/ipy/fecon235/nb\n" ] } ], "source": [ - "# NOTEBOOK v4 settings and system details: [00-tpl v4.15.0812]\n", - "\n", - "# Assume that the backend is LINUX (e.g. Ubuntu running bash shell):\n", - "print '\\n :: TIMESTAMP of last notebook execution:'\n", - "!date\n", - "print ' :: IPython version:'\n", - "!ipython --version\n", - "\n", - "# Automatically RELOAD modified modules:\n", + "# PREAMBLE-p6.16.0428 :: 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", - "# 0 disables autoreload.\n", + "# Use 0 to disable this feature.\n", "\n", - "# DISPLAY options\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.display import HTML # useful for snippets\n", - "# e.g. HTML('')\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", - "# MATH display, use %%latex, rather than the following:\n", - "# from IPython.display import Math\n", - "# from IPython.display import Latex\n", - "# Generate PLOTS inside notebook:\n", - "%matplotlib inline\n", - "\n", - "import pandas as pd\n", - "print ' :: 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", - "print ' :: Working directory (set as $workd):'\n", - "workd, = !pwd\n", - "print workd + '\\n'" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from fecon import *" + "# Generate PLOTS inside notebook, \"inline\" generates static png:\n", + "%matplotlib inline \n", + "# \"notebook\" argument allows interactive zoom and resize." ] }, { @@ -134,9 +138,9 @@ "outputs": [], "source": [ "# SET UP the particular (f4) futures contracts of interest:\n", - "s_libor = 'f4libor15z'\n", - "s_xau1 = 'f4xau15z'\n", - "s_xau2 = 'f4xau16m'\n", + "s_libor = 'f4libor16z'\n", + "s_xau1 = 'f4xau16z'\n", + "s_xau2 = 'f4xau17m'\n", "\n", "# f4libor* refers to the CME Eurodollar futures.\n", "\n", @@ -172,15 +176,15 @@ { "data": { "text/plain": [ - " Y\n", - "Date \n", - "2015-10-01 0.415\n", - "2015-10-02 0.380\n", - "2015-10-05 0.390\n", - "2015-10-06 0.385\n", - "2015-10-07 0.395\n", - "2015-10-08 0.390\n", - "2015-10-09 0.390" + " Y\n", + "Date \n", + "2016-11-23 0.9950\n", + "2016-11-25 0.9975\n", + "2016-11-28 0.9925\n", + "2016-11-29 0.9825\n", + "2016-11-30 0.9900\n", + "2016-12-01 0.9875\n", + "2016-12-02 0.9875" ] }, "execution_count": 5, @@ -204,13 +208,13 @@ "text/plain": [ " Y\n", "Date \n", - "2015-10-01 1113.7\n", - "2015-10-02 1136.6\n", - "2015-10-05 1137.6\n", - "2015-10-06 1146.4\n", - "2015-10-07 1148.7\n", - "2015-10-08 1144.3\n", - "2015-10-09 1155.9" + "2016-11-23 1189.3\n", + "2016-11-25 1178.4\n", + "2016-11-28 1190.8\n", + "2016-11-29 1187.9\n", + "2016-11-30 1170.8\n", + "2016-12-01 1166.9\n", + "2016-12-02 1175.1" ] }, "execution_count": 6, @@ -234,13 +238,13 @@ "text/plain": [ " Y\n", "Date \n", - "2015-10-01 1115.9\n", - "2015-10-02 1138.6\n", - "2015-10-05 1139.7\n", - "2015-10-06 1148.4\n", - "2015-10-07 1150.7\n", - "2015-10-08 1146.3\n", - "2015-10-09 1158.0" + "2016-11-23 1198.3\n", + "2016-11-25 1187.0\n", + "2016-11-28 1200.0\n", + "2016-11-29 1197.0\n", + "2016-11-30 1179.9\n", + "2016-12-01 1175.3\n", + "2016-12-02 1183.8" ] }, "execution_count": 7, @@ -286,13 +290,13 @@ "text/plain": [ " Y\n", "Date \n", - "2015-10-01 0.395079\n", - "2015-10-02 0.351927\n", - "2015-10-05 0.369198\n", - "2015-10-06 0.348918\n", - "2015-10-07 0.348220\n", - "2015-10-08 0.349559\n", - "2015-10-09 0.363353" + "2016-11-23 1.513495\n", + "2016-11-25 1.459606\n", + "2016-11-28 1.545180\n", + "2016-11-29 1.532115\n", + "2016-11-30 1.554493\n", + "2016-12-01 1.439712\n", + "2016-12-02 1.480725" ] }, "execution_count": 9, @@ -313,9 +317,9 @@ "outputs": [ { "data": { - "image/png": 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jPu8KparK8nl/+CH84hdmuMH83r16WdFmpy4rV1oyL4DPPoNHHy2vHsdpDBVr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\nN25Ubd5c9d//tnVdu6qedZZ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lShAW9WvoEQed1ahx5cpUIWL/d9hfeu2oo4qfal+ozosussypHTvCySfDAw+k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\nP6fS5L79tlUqSmf33S3N71132SzNTPn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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -356,13 +360,13 @@ "text/plain": [ " Y\n", "Date \n", - "2015-10-01 -0.019921\n", - "2015-10-02 -0.028073\n", - "2015-10-05 -0.020802\n", - "2015-10-06 -0.036082\n", - "2015-10-07 -0.046780\n", - "2015-10-08 -0.040441\n", - "2015-10-09 -0.026647" + "2016-11-23 0.518495\n", + "2016-11-25 0.462106\n", + "2016-11-28 0.552680\n", + "2016-11-29 0.549615\n", + "2016-11-30 0.564493\n", + "2016-12-01 0.452212\n", + "2016-12-02 0.493225" ] }, "execution_count": 12, @@ -383,9 +387,9 @@ "outputs": [ { "data": { - "image/png": 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BdfEBnWMMGgQLFyZ/fvr0OhoaoHfvxPfv3Rvq62M0N282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\n69QEwba3lSzpkpG0Evje9nBqLUUUGnrvBUaAk8Bq4CHbk0m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NFpHd81FvIagP/s9iEAedcdAI0ddZW30rVpiLpDZD93MhV519+8L99xdWSzoK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\nLLXwPqq6Iml7Gyz1QugawV2T+SIOOot5Tdbn6Jq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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -409,13 +413,13 @@ "text/plain": [ " Y\n", "count 512.000000\n", - "mean -0.055539\n", - "std 0.175758\n", - "min -0.387929\n", - "25% -0.201538\n", - "50% -0.098203\n", - "75% 0.094842\n", - "max 0.282874" + "mean -0.131907\n", + "std 0.422571\n", + "min -0.679746\n", + "25% -0.427747\n", + "50% -0.321510\n", + "75% 0.197633\n", + "max 0.869532" ] }, "execution_count": 14, @@ -432,7 +436,8 @@ "metadata": {}, "source": [ "Usually *tango* has approximate mean of zero, with somewhat \n", - "wide latitude: 2015-10-09 at 18 bp standard deviation (annualized).\n", + "wide latitude: 2015-10-09 at 18 bp standard deviation (annualized), \n", + "2016-12-02 at 42 bp standard deviation (annualized).\n", "\n", "Since warehousing costs for gold are fairly constant across time, \n", "changes in tango mainly reflect supply and demand. \n", @@ -515,9 +520,9 @@ "outputs": [ { "data": { - "image/png": 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KaG1r4qDRUSmtxRj4ZctyG80hQ6yvpxxa3W9yzZrS9k8kbMxHIUT1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\nJjj/fLjggvzbJxIJxo0bB/CxvcxE3jx4ERkKPOJ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FC4LXX8jpGTPgvPMqmTjRpisrK5k0aRLAN/YyJapa8BfQ2/f5MuCBJOuoI3dO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\nrkJggg==\n", 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QWY/l4INVX3/d3nfooLpiRd1jBgywNhJpzYSrrlI99FDV/ffP/LNh2xnXJucj3fEA4Czg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\nB2Pp1888wldeSX905JlnWhbIj39soyw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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -588,52 +593,53 @@ "text": [ " :: FIRST variable:\n", "count 512.000000\n", - "mean -10.339155\n", - "std 8.217664\n", - "min -29.210332\n", - "25% -15.148375\n", - "50% -8.715890\n", - "75% -5.237605\n", - "max 10.192953\n", + "mean -0.329825\n", + "std 11.062097\n", + "min -17.529926\n", + "25% -9.403597\n", + "50% -3.585356\n", + "75% 7.529238\n", + "max 24.316317\n", "Name: Y, dtype: float64\n", "\n", " :: SECOND variable:\n", "count 512.000000\n", - "mean -0.055539\n", - "std 0.175758\n", - "min -0.387929\n", - "25% -0.201538\n", - "50% -0.098203\n", - "75% 0.094842\n", - "max 0.282874\n", + "mean -0.131907\n", + "std 0.422571\n", + "min -0.679746\n", + "25% -0.427747\n", + "50% -0.321510\n", + "75% 0.197633\n", + "max 0.869532\n", "Name: Y, dtype: float64\n", "\n", " :: CORRELATION\n", - "-0.827301833127\n", - "\n", - "-------------------------Summary of Regression Analysis-------------------------\n", - "\n", - "Formula: Y ~ + \n", - "\n", - "Number of Observations: 494\n", - "Number of Degrees of Freedom: 2\n", + "0.807198796038\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Y R-squared: 0.652\n", + "Model: OLS Adj. R-squared: 0.651\n", + "Method: Least Squares F-statistic: 920.0\n", + "Date: Sun, 04 Dec 2016 Prob (F-statistic): 1.02e-114\n", + "Time: 09:46:52 Log-Likelihood: -1628.5\n", + "No. Observations: 494 AIC: 3261.\n", + "Df Residuals: 492 BIC: 3269.\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "==============================================================================\n", + " coef std err t P>|t| [95.0% Conf. Int.]\n", + "------------------------------------------------------------------------------\n", + "Intercept 2.2679 0.306 7.404 0.000 1.666 2.870\n", + "X 21.0366 0.694 30.332 0.000 19.674 22.399\n", + "==============================================================================\n", + "Omnibus: 4.414 Durbin-Watson: 0.060\n", + "Prob(Omnibus): 0.110 Jarque-Bera (JB): 3.210\n", + "Skew: -0.024 Prob(JB): 0.201\n", + "Kurtosis: 2.608 Cond. No. 2.39\n", + "==============================================================================\n", "\n", - "R-squared: 0.6844\n", - "Adj R-squared: 0.6838\n", - "\n", - "Rmse: 4.5910\n", - "\n", - "F-stat (1, 492): 1067.0753, p-value: 0.0000\n", - "\n", - "Degrees of Freedom: model 1, resid 492\n", - "\n", - "-----------------------Summary of Estimated Coefficients------------------------\n", - " Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5%\n", - "--------------------------------------------------------------------------------\n", - " x -39.8728 1.2206 -32.67 0.0000 -42.2652 -37.4804\n", - " intercept -12.9712 0.2218 -58.47 0.0000 -13.4061 -12.5364\n", - "---------------------------------End of Summary---------------------------------\n", - "\n" + "Warnings:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], @@ -641,16 +647,8 @@ "# Is there near-term correlation between price change and tango?\n", "stat2( xau0pc[Y], tango[Y] )\n", "# 2015-09-11 correlation: -0.85, so YES.\n", - "# 2015-10-09 correlation: -0.83" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So roughly speaking, **increasing tango is correlated to decling prices.** Thus increasing selling pressure in the near-term versus the distant-term (which in effect widens tango) is correlated to future declines in cash prices. \n", - "\n", - "Equivalently, **decreasing tango is correlated to increasing prices.**" + "# 2015-10-09 correlation: -0.83\n", + "# 2016-12-02 correlation: +0.81, but change in sign!" ] }, { @@ -659,17 +657,29 @@ "collapsed": true }, "source": [ - "## Closing comments\n", + "## Closing comments 2015-10-11\n", "\n", - "2015-10-11 Since tango is currently near its mean value, \n", + "So roughly speaking, **increasing tango is correlated to decling prices.** \n", + "Thus increasing selling pressure in the near-term versus the \n", + "distant-term (which in effect widens tango) is correlated to \n", + "future declines in cash prices. \n", + "Equivalently, **decreasing tango is correlated to increasing prices.**\n", + "\n", + "Since tango is currently near its mean value, \n", "it seems equally likely to decrease or increase \n", "(though its short-term trend is upwards), so the \n", "future direction of gold prices seems inconclusive.\n", "\n", - "Tango as an indicator is insightful whenever it is \n", - "at the extremes of its expected range. Trading urgency \n", - "is indicated at such extremes, which is subsequently \n", - "dissipated as the gold market rebalances." + "## Closing comments 2016-12-02\n", + "\n", + "The new data over the last 13 months has reversed the sign \n", + "of the correlation. In other words, the relationship previously \n", + "noted in closing is spurious. \n", + "(Please see the previous commit of this entire notebook \n", + "for specific details.)\n", + "\n", + "Tango as an indicator is [**not**] insightful for price changes \n", + "whenever it is at the extremes of its expected range." ] } ], @@ -689,7 +699,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.10" + "version": "2.7.11" } }, "nbformat": 4,