Each file in this project generally has a detailed change log contained within itself. This file simply gives a grand overview of such details and the annotations in the commits and tags.
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.
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.
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.
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.
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
qdl-spx-earn-div.ipynb: remedy for issue #3 Math Processing Error. GitHub choking on LaTeX equations, so provide alternative view link at Jupyter. No problems if notebook is locally executed.
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.
lib/yi_1tools.py: add lagdf() to create lagged DataFrame, useful data structure for vector autoregressions.
Add tests/test_1tools.py esp. to test lagdf(), other functions in module yi_1tools are tested along the way.
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.
SEC-13F-parse.ipynb: Fix issue #2 by v4 and p6 updates. Noteworthy dramatic GLD liquidation in 2016-Q4 by Paulson.
Add NEW nb/qdl-spx-earn-div.ipynb which examines the three separable components of total return for equities: enterprise and speculative returns plus dividend yield, using the Shiller database dating back to 1871. Shortcut: https://git.io/equities or https://git.io/spx
Add directory .github for issue and PR templates,
see 2016-02-17 GitHub post
- Rename CONTRIBUTING.md -> .github/CONTRIBUTING.md
- Add .github/ISSUE_TEMPLATE.md
- Add .github/PULL_REQUEST_TEMPLATE.md
Fix issue #2 by v4 and p6 updates:
- fred-housing.ipynb
- fred-xau-tips.ipynb
We conjecture that real gold is a stationary time-series bound by real interest rates.
We adopt use of group dictionaries where key serves as name, and value is its data code. Some new functions in the fecon235 module: groupget, grouppc, groupgeoret, groupholtf -- are helpful in clarifying logic and reducing notebook clutter. The function groupfun() is mathematically an operator.
For example, cotr4w is a group, and further usage is explained in nb/qdl-COTR-positions.ipynb for CFTC Commitment of Traders Reports. One command: groupcotr() will summarize results, with optional smoothing parameter.
For fecon235.py: add forefunds() to forecast Fed Funds directly. Its derivation is explained in qdl-libor-fed-funds.ipynb.
Append sample size and dates to georet() output, making it suitable for logging purposes. Geometric mean returns are ranked in groupgeoret().
Procedure plotdf() has a todf pre-filter for convenience so that Series type can be plotted directly. That procedure is now tried first in plot().
Fix issue 2 with v4 and p6 upgrades:
- fred-georeturns.ipynb
- qdl-libor-fed-funds.ipynb
To update pre-2016 notebooks, sections for import and the preamble must be modified, please see issue 2.
Major v4 benefits from the python3 compatibility changes made during v3. All modules are now operational under both Python 2 and 3. Also, code has been rewritten for cross-platform performance (Linux, Mac, and Windows).
We MOVED the yi-modules from nb to a new directory: lib. Python 3 uses absolute import and our python2 code now conforms to that practice.
To update pre-2016 notebooks, please use import style discussed in docs README: https://git.io/fecon-intro The top-level module fecon235.py (formerly known as nb/fecon.py) is also explained in that introduction. With adoption of python3 print_function, the python2 print statement must be rewritten as a function.
We also highly recommend inclusion of PREAMBLE-p6.15.1223 which gives versioning requirements for successful notebook replication. With those fixes, our notebooks should run under both Python kernels in Jupyter.
Make friends with np.true_divide() and np.floor_divide(), avoiding np.divide() like the plague: call our convenient div() directly instead of numpy.
The directory tests is no longer a package. Thus one can run tests easily against an installed version of the main package, and independently. Our tests should be nosetests and pytest compatible.
Module yi_0sys encourages cross-platform execution. This should help eliminate dependence on Linux for most casual users.
That module is also useful for python3 compatibility. Jupyter project is now calling python2 a "legacy." Code base is now clarified in README.md
Score for the Federal Reserve is computed in fred-infl-unem-fed.ipynb leading to a discussion of the Phillips curve, i.e. the inflation vs unemployment relationship.
The major change to v3 marks our adoption of Jupyter which has now become fully independent of IPython, although it is still known as IPython v4.0 ("conda update ipython-notebook" still works as expected for existing Anaconda distributions). Creating R notebooks is now very easy.
pandas 0.17 now requires a new package called pandas-datareader which was refactored from deprecated pandas.io -- so please install that package if you intend to use the yi_stocks module.
The yi_plot module now includes sequential heatmap scatter plotting. This is great for visualizing the points in a scatter plot over time. Currently the color sequence will go from blue to green to red, like the MATLAB rainbow. Soon that we shall switch that color map to viridis, which is perceptually uniform, as it becomes the default in matplotlib.
Introduce two new notebooks:
-
qdl-libor-fed-funds.ipynb : examines the spread between LIBOR and Fed Funds. By constructing a synthetic forward Fed Funds rate, estimate is given for the change in Fed Funds rate over the next 12 months.
-
qdl-xau-contango.ipynb : London Bullion Market Association ceased publishing daily data on their Gold Forward Offered Rate (GOFO), as of 30 January 2015 -- so we develop an observable proxy called tango using gold futures and LIBOR.
Futures prices can be retrieved using our yi_quandl module. Data for stocks, ETFs, and mutual funds comes directly from Yahoo Finance, and falls back on Google Finance: see yi_stocks module. All data is easily retrieved using fecon.get() and our internal slang.
As our own API stabilizes, tests will cover more units.
Currently we rely on Python nose and doctest.
All fred- notebooks are stable. Henceforth, they will be automatically converted from IPython notebook format v3 to v4.
Add module yi_secform.py, derived from nb/SEC-13F-parse.ipynb, so that pandas can read 13F filings from SEC and easily sort portfolio allocations.
MAJOR change: During August 2015 we added Quandl API and module to get data. Module yi_quandl.py is our wrapper over the API yi_quandl_api.py for creating easily accessible synthetic series.
Add qdl-COTR-positions.ipynb to demonstrate reading CFTC Commitment of Traders Reports, retrieved using our yi_quandl module.
MAJOR change: new module fecon conveniently unifies necessary yi modules to especially simplify interactive commands.
Recommend pandas > 0.15
Included modules have passed tests. Imperative that pandas > 0.13 with recommended dependencies on numpy.