2018-12-03 (tag: v6.18.1203)

Add nb/boots_ndl_d4spx_1957-2018.csv.gz and nb/boots-eq-spx.ipynb
for Bootstrapping, which demonstrates fecon236 sim and bootstrap
modules, applied to leptokurtotic SPX equity returns.

Add nb/fred-credit-spreads.ipynb which also serves as a
tutorial on MAD, Median Absolute Deviation,
in robustly rescaling non-Gaussian time-series.
We consider mortgage and corporate credit spreads to
construct a robust Unified Credit Profile to calibrate
credit default risk in the context of monetary policy.

2018-06-23 (tag: v6.18.0623)

**Major version change from v5 to v6**
signaling our integration with **fecon236**
which was spun-off from our source code.

**Henceforth, fecon235 becomes a repository solely of Jupyter notebooks.**
The old Python source code at fecon235 will remain for archival purposes,
while new code development shifts over to fecon236.
Please see https://git.io/econ for details.

Refresh README.md, esp. "What for" section,
include "Migration notice" at the top
regarding separation of tools from notebooks.

Revise docs/fecon235-00-README.ipynb to introduce fecon236.
Function names have been retained, but under fecon236
the call routing is expected to be more explicit than casual,
i.e. modules names are more significant.

2018-03-12 (tag: v5.18.0312)

Update and fix fred-oil-brent-wti.ipynb per issue #2.
New shortcut: https://git.io/oil for crude oil markets.
New introductions regarding petrochemicals
and the Boltzmann portfolio of oils.

nb/fred-debt-pop.ipynb: Update preamble and data.
Use gemrat() instead of georet().
Append Appendix 1. New shortcut: https://git.io/debtpop

fecon235.py: Add foreholt() function which
supercedes yi_fred.holtfred(), but
move holtfred() to fecon235 module for backward compatibility.
Then at lib/yi_fred.py: comment out holtfred() with notification.
We only need to maintain foreholt().
Pre-2016 notebooks with updated import and preambles should work.

Fix fred-inflation.ipynb per issue #2, and optimize forecast.
Holt-Winters model now uses robust optimized parameters.
Unified inflation has its own section.
Geometric mean rate introduced.
Drop section on correlation to gold.
Summary combines three orthogonal forecasts.

fecon235.py: Add foreinfl() to forecast Unified Inflation.
The best documentation for this function is nb/fred-inflation.ipynb
which shows how it was derived by interacting with data and plots.
This single function distills the forecasting process
derived in the notebook. Further discussed in new Appendix 2.

2017-07-22 (tag: v5.17.0722)

lib/ys_gauss_mix.py: Add gm2gemrat() and gm2gem().
Unify GM(2) model and gemrat() with only one pass through data.
Fallback clause for gemrate() adopts
second-order approximation used in georet().
Note: nan will occur when expected losses exceed 100% -- log error!!
Such mean estimates actually occurred during 2008Q4, Great Recession.

nb/fred-georeturns.ipynb: Replace groupgeoret() by groupgemrat().

Add docs/fecon235-08-sympy.ipynb: SymPy tutorial.
Demo sympy with LaTeX features.
Reference its use with Gaussian mixture models.

Add lib/yi_matrix.py: Linear algebra module.
Numerically understand numpy inverse methods.
Add cov2cor(): convert covariance to correlation coefficients.

Add lib/ys_mlearn.py: new softmax().
New module for machine learning tools.
Function softmax() is used in cross-entropy and MLE situations,
useful for constructing Boltzmann portfolios.
New softmax_sort(): sort on probabilities with options
for filter and renormalization.

Add lib/ys_prtf_boltzmann.py: Boltzmann portfolio,
alternative to Markowitz portfolio.
Usage demonstrated in notebook nb/prtf-boltzmann-1.ipynb

Add nb/prtf-boltzmann-2.ipynb:
Investigate temperature parametization, portfolio weight dynamics.
Add Appendix 1 regarding August 2011 global crisis
and learning from stress conditions.

2017-06-03 (tag: v5.17.0603)

lib/yi_0sys.py: Add timestamp() per RFC-3339 standard.

Relationship between the real economy and the equities market,
fred-gdp-spx.ipynb: Fix issue #2, optimize HW parameters,
update data and narrative.
Usage of the code for the Holt-Winters time-series model is illustrated
in the Jupyter notebook rendered at https://git.io/gdpspx

lib/yi_1tools.py: Add toar(), converter to pure array.
Add df2a() to convert single column dataframe to np array
of shape (n,) rather than (n,1) which can be annoying.
Add pastear() to merge arrays as columns.
Add diflog(), and accept numpy array as data;
this takes the difference between lagged log(data).

lib/yi_1tools.py: Add roundit() to round floats from iterable.
Echo or return a list from iterable where floats are rounded n places.
This is mainly to make some lengthy output readable.

lib/yi_1tools.py: Add kurtfun() to compute Pearson kurtosis, and
append said function to stat().

lib/yi_simulation.py: add random functions:
uniform randou(), and indicator maybe().
Add Gaussian randog(), simug(), and simug_mix().
Mathematical usage given in nb/gauss-mix-kurtosis.ipynb
which covers Gaussian mixture GM(n) models.

gauss-mix-kurtosis.ipynb: Analytic solution for GM(2)
is complete. Use sympy to compute model parameters numerically.

Fix gm2_strategy() when inadmissible solution occurs.
A seemingly free parameter like b may not lead to a solution
in the real domain, but a slight upward adjustment may help
to avoid the imaginary domain, thanks to sympy.

lib/yi_plot.py: Add plotqq() for Q-Q probability plot.
Quantile-quantile plots are used to compare data
to a theoretical distribution (default is Gaussian).

lib/ys_gauss_mix.py: Refine geometric mean approximation.
Add gemreturn_Jean(), gemrate(), and for data: georat().
These involve kurtosis, but not the GM(2) sigma decomposition.

Add tests/test_gauss_mix.py: PASSED, for module ys_gauss_mix.
Add tests for gemrate() and gemrat().
The former implicitly is a test of approximating Jean (1983)
infinite series for geometric mean returns.

For yi_1tools.py: Add names() for column and index names.

Data from different sources need standardized NAMES
to interoperate, esp. the time index which we call 'T'.
We imposed this convention when importing FRED data, and
thus for compatibility we shall use names()
as part of getqdl() and getstocks().

Generalize forecast() in main fecon235 module.
This supercedes: "Unifies holtfred and holtqdl for quick forecasting,"
yet preserves and expands former interface.
Data retrieval logic is streamlined, and
"data" may be a DataFrame, fredcode, quandlcode, or stock slang.
Former argument defaults are duly respected.

For lib/ys_opt_holt.py: Add optimize_holtforecast() which
will produce forecasts using optimal Holt-Winters parameters.

For ys_opt_holt.py: Include losspc among alphabetaloss list
to indicate loss percentage relative to absolute tailvalue,
useful in discerning the precision of the forecasts.

For yi_fred.py: Add USDCNY daily series, Chinese yuan, "d4usdcny".
The source is Federal Reserve Bank H.10 which references the onshore rate,
not the freely traded offshore USDCNH rate.
The daily series goes back to 1981.

For yi_quandl.py: Add Bitcoin count and USD price:
"d7xbtcount" and "d7xbtusd" are new quandlcodes.
Use d7 quandlcode prefix to signify 7 days/week data.

Add qdl-xbt-bitcoin.ipynb, new notebook for Bitcoin:
We first examine time-series data for price, mining, and capitalization of Bitcoin,
then optimize a robust model for the extremely volatile USD price series.
Taking the viewpoint of a Chinese user we perform a comparative valuation in
Chinese yuan, and also cross-check with the perennial store of value: gold.
The astonishing volatility and geometric return makes Bitcoin a
speculative financial asset which may hinder it as a payment system.
Shortcut: https://git.io/xbt

LICENSE.md: Add further terms & conditions, especially note:
Our material has been prepared for informational and educational purposes
only, without regard to any particular user's investment objectives, financial
situation or means.  We are not soliciting any action based upon it. Our
material is not to be construed as a recommendation; or an offer to buy or
sell; or the solicitation of an offer to buy or sell any security, financial
product, or instrument.
You should neither construe any of our material as business, financial,
investment, hedging, trading, legal, regulatory, tax, or accounting advice nor
make our service the primary basis for any investment decisions made by or on
behalf of you, your accountants, or your managed or fiduciary accounts.

fred-gdp-wage.ipynb: Fix #2 by v5, p6.16.0428 upgrades --

notebook code is now Python 2.7 and 3 compatible.
Minor changes in the econometrics due to new
additional data since December 2014 (two more years of data).

fred-infl-unem-fed.ipynb: Fix #2 by v5, p6.16.0428 upgrades --
switch from fecon to fecon235 for main import module.
Minor edits given additional year of data.

Update README.md with recent shortcut URLs.
Worker wage correlated with GDP output: https://git.io/gdpwage
Studies of the Phillips curve: https://git.io/phillips
which redirects to fred-infl-unem-fed.ipynb
thus the same as: https://git.io/fed

lib/yi_1tools.py: Add retrace() and retracedf() --
useful in understanding how technical chart points are derived.

qdl-xau-contango.ipynb: Solve #2 by v5 & p6.16.0428 upgrades --
switch from fecon to fecon235 for main import module,
minor edits given more data and change in futures basis.
During 2015 we detected strong negative correlation between price change and
tango, however, in 2016 that strong correlation became positive -- thus we
conclude the relationship is spurious. The observed correlations are mere
artifacts which do not imply any significant economic relationships.

2016-12-14 CRITICAL fix of initial b[0] for holt_winters_growth() --
modified: yi_timeseries.py,
also a fix for recently rewritten ema() since it assumes beta=0.
The symptoms were bizarre exponential moving average estimates
due to the growth coefficient unintentionally set always to a
non-zero constant equal to y[1]-y[0], rather than zero.
Add tests/test_timeseries.py to verify fix #5 --
see discussion: #5

Add lib/ys_opt_holt.py optimize Holt-Winters alpha and beta --
conditional on specific data, helpful application of our optimization package.

Add Fed Funds and its "30-day" exponential moving average
as d4ff and d4ff30 -- thus modifying lib/yi_fred.py.
The exponential moving average of d4ff is intended as
a synthetic series to simulate the spot rate of the
Fed Funds futures traded at the CME which uses a
30-day average for settlement of its contracts.

Huge revision: qdl-libor-fed-funds.ipynb Fed rate hikes --
major clarification using transposition and tenor assumptions.
Include 2016-12-14 Fed rate hike, and 2017 policy forecast;
must see: https://git.io/fedfunds

New index_delta_secs() to infer frequency

in lib/yi_fred.py module for the purpose of appropriately
chosing downsampling or upsampling for .resample

New resample_main() fixes #6 deprecations.
Rewrite daily(), monthly(), quarterly() in lib/yi_fred.py,
then add tests/test_fred.py to test module.

As a result of pandas revised API for resampling, fecon235
must advance to v5 and require pandas 0.18 or higher.

Add bin/docker/rsvp_fecon235/Dockerfile for Docker image.

See public hub at https://hub.docker.com/r/rsvp/fecon235
regarding running our Docker container.

Move and rewrite ema() to close #5
since *pd.ewma() is deprecated* as of pandas 0.18.
Our old ema() has been commented out in lib/yi_1tools.py
then new ema() has been rewritten in lib/yi_timeseries.py
as a special case of holtlevel().
See issue #5 for details on pandas deprecation warning,
Exponential Moving Average and Holt-Winters smoothing:
#5

Add lib/ys_optimize.py featuring the following:

- minBrute(): non-convex problem: GLOBAL optimizers: Brute force grid search.
- minNelder(): if data is NOISY: Nelder-Mead simplex method.
- minBroyden(): WITHOUT knowledge of the gradient: L-BFGS-B, Broyden-Fletcher-Goldfarb-Shanno.
- optimize(): unifies the three above.

Add tests/test_optimize.py for ys_optimize module.
This also serves as a tutorial for optimization of loss functions,
given data and model, see Robust Estimation section.

yi_1tools.py: replace deprecated ols from pandas.stats.api,
revise regress() by using regressformula().
Introduce new intercept argument, used also for stat2().

Finalize fred-employ-nfp.ipynb for May 2016 release.
Forecast monthly change in NFP using a variety of optics:
baseline expectation since 1939, Holt-Winters method,
visual selection of local range, and regression against SPX -- but
standard errors are inherently very large due to survey measurement error.