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Adriano
@rsvp
FOMC: Two more hours until Yellen speaks... in the meantime, check out our new Fed score: https://git.io/fed which is also useful to visualize the Phillips curve via sequential scatter plot with color heat map.
Adriano
@rsvp
As of v4.15.1230 all modules are Python 2 and 3 compatible. Import and preamble in notebooks have new cross-platform formats: please see https://git.io/fecon-intro for details. Bug report #2 filed for updating pre-2016 notebooks. -- Other than that: HAPPY NEW YEAR!
Adriano
@rsvp
Here's another illustration of "Fix issue #2 with v4 and p6 upgrades": https://git.io/georet which concerns comparative geometric mean returns.
Adriano
@rsvp
Fixed issue #2 for https://git.io/fedfunds -- NEW forecast for Fed Funds one-year out: 0.76% . Given the 2015-12-16 rate hike, this implies in terms of expected Fed action: one further rate hike of 25 bp (keeping actual in the upper range), or two rate hikes of 25 bp (keeping actual in the lower [0.75, 1.00] range). The findings from the notebook is now distilled into a NEW single convenient function forefunds().
Adriano
@rsvp
v4.16.0123 has newly revised position indicators for CFTC Commitment of Traders Report -- see https://git.io/cotr esp. for the Bonds/Equities joint narrative using color heat map. Normalized indicator for Equities has exceeded two standard deviations on the bearish side (for futures and options position, not relative to the price action).
Adriano
@rsvp
Using forefunds(), the forecast for Fed Funds one-year out: 0.55% which means NO hikes in the meanwhile (keeping actual at upper range), or slight chance of maybe one further rate hike (keeping actual at lower bound):
forefunds_2016-02-04
Adriano
@rsvp
BTW, we used the March cycle for the previous chart, i.e. forefunds('16h', '17h') -- the market's reversal of the 2015-12-16 rate hike is quite dramatic, since the Fed implied three more 25 bp rate hikes in their blue dot plot.
Adriano
@rsvp
Re: Housing economy https://git.io/housing completely revises hscore which roughly expresses the number of new "affordable" housing units in terms of percentage of total population. It confirms we are midway between the last housing bubble and the subprime mortgage crisis (leading to the Great Recession).
Adriano
@rsvp
Notebook on gold versus real rates updated: https://git.io/gold -- conjecture that real gold is a stationary time-series bound by real interest rates.
Adriano
@rsvp
Parsing SEC 13F filings, https://git.io/13F updated: we estimate that Paulson liquidated the equivalent of about 333,416 troy ounces at a weighted average price of $ 1059 at the end of 2015 -- that 's represents a -37.6% reduction in his GLD holdings, a gold ETF.
Adriano
@rsvp
Thanks to @samfisher83 for submitting our repo to ycombinator Hacker News: https://news.ycombinator.com/item?id=11240344
Adriano
@rsvp
NEW: on separable components of total return for equities, using Shiller's S&P stock market data, dating back to the year 1871: http://nbviewer.jupyter.org/github/rsvp/fecon235/blob/master/nb/qdl-spx-earn-div.ipynb
Adriano
@rsvp
Problem solely at GitHub, "Math Processing Error": rsvp/fecon235#3
Adriano
@rsvp
Cross-posted issue: GitHub LaTeX "Math Processing Error" when rendering Jupyter notebooks jupyter/help#21
Adriano
@rsvp
fecon235 now appears in a Chinese directory of Jupyter projects: http://docklet.unias.org/docklet-book/userguide/_book/zh/notebook/gallery.html -- so how to say thanks...
mmeanwe
@mmeanwe
what FRED tag/ID's are you using for fred-debt-pop.ipynb - specifically m4debt? Thx
Adriano
@rsvp
@gitmmeanwell hi, m4debt is pandas resampled monthly based on quarterly q4debt, whose fredcode is 'GFDEBTN', non-seasonally adjusted U.S. Federal debt in millions USD. The details are in the lib/yi_fred module, including the resampler monthly() which essentially does the linear interpolation.
mmeanwe
@mmeanwe
Got it - thx!
Adriano
@rsvp
A fecon235 user looks at trading algo module zipline: http://conrad.pythonanywhere.com/pydata/QuantFinance.html
Adriano
@rsvp
v4.16.0525 features new optimization module. Also, we finished up employment data, focusing on forecasting NFP, Nonfarm Payroll -- see http://nbviewer.jupyter.org/github/rsvp/fecon235/blob/master/nb/fred-employ-nfp.ipynb for details.
Adriano
@rsvp
Just based on the most recent NFP Nonfarm Payroll number, the market is no longer expecting the Fed to hike in July. The error bounds are important as detailed in our notebook... as it seems that the Federal Reserve is becoming data-point dependent for its monetary policies.
Adriano
@rsvp
Wondering if there was interest in running fecon235 via Docker container? Probably the quickest way for demos. Your thoughts?
Adriano
@rsvp
We set up the public repo for the fecon235 Docker image at https://hub.docker.com/r/rsvp/fecon235 -- just pull rsvp/fecon235 and then run this command, $ docker run -p 8888:8888 -it rsvp/fecon235 -- very simple, plus all the complicated dependencies are resolved. It is excellent for a quick replicable test environment, and a super-easy gateway for Mac/Windows users since Docker works cross-platform.
Adriano
@rsvp

Closed #6 ... As a result of pandas revised API for resampling, fecon235 must advance to v5 and require pandas 0.18 or higher.

No more deprecation warnings from using resampling functions: daily(), monthly(), quarterly()

Adriano
@rsvp
Updated the Fed's score on their dual mandate: see https://git.io/fed -- they are on target! The post-election yield curve shift seems to have Dec rate hike built-in.
Adriano
@rsvp
Touched up GDP/wage study, given two more years of data, esp. regression which includes time-variant multiplier: https://git.io/gdpwage -- Yellen seems generally satisfied about the labor market conditions, but still very concerned about wage growth.
Adriano
@rsvp

The 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.

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.

Short URL: https://git.io/xau-contango

Adriano
@rsvp
CRITICAL bug fix for our newly revised ema() function for exponential moving averages: rsvp/fecon235@35d3783 -- REMEDY: pull the latest master branch.
Adriano
@rsvp

This site has charted our forks and stars over time: https://porter.io/github.com/rsvp/fecon235 -- "Hacker News Personalized and Delivered"

And here are the search results for "financial economics" repos at their site: https://porter.io/search/?q=financial%20economics

Adriano
@rsvp
Another site which mimics our repository (but not as a clone): https://devhub.io/repos/rsvp-fecon235 -- it even has rewritten my profile. Somewhat strange...
Adriano
@rsvp

Happy Holidays -- new v5.16.1225

Huge revision: qdl-libor-fed-funds.ipynb re Fed rate hikes -- major clarification using transposition and tenor assumptions. Include 2016-12-14 Fed rate hike, and 2017 policy forecast; please see: https://git.io/fedfunds for details. Market is currently aligned with Fed's announced intention of three 25 bp rate hikes during 2017. Implied Fed Funds one-year forward: 1.15%

Adriano
@rsvp
forecast() has been generalized to include an optimization option via grids argument, and will also now accept stock slang, see rsvp/fecon235@dfddf5e
Adriano
@rsvp
Trump inauguration day: 2017-01-20, Implied Fed Funds one-year forward: 1.19% -- output from forefunds('17h', '18h')
Adriano
@rsvp
Pandas will deprecate .ix as of 0.20, so use .loc or .iloc instead. This makes usage more explicit (relying less on magic). fecon235 does not use .ix so no worries.
Adriano
@rsvp
Yellen considering rate hike in March if economy remains strong: 2017-02-15 forefunds('17h', '18h') at 1.25% implied Fed Funds one-year forward (only 10 bp higher from 2016 year-end).
Adriano
@rsvp
Tensorflow has gone 1.0 -- and Keras is now an integral part (see tf.keras) -- the API should be stable for neural networks.
Adriano
@rsvp
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
Adriano
@rsvp
Latest release: https://github.com/rsvp/fecon235/releases/tag/v5.17.0221 -- the different underlying database sources should now be operational compatible with among themselves. Thus out-of-the-box fecon235 will not require awkward renaming of columns and indexes, especially for time-series data not in our lexicon.
Adriano
@rsvp
Yellen expects rate hike on March 15... 2017-03-02 forefunds('17m', '18m') at 1.22% implied Fed Funds one-year forward -- which is in line with street expectations of "two and a half" 25 bp increases in the coming year.
Adriano
@rsvp
Remember when pizza cost 1,000 Bitcoins? Well, that now gets you about 1,000 troy oz. of gold, roughly worth $1.22 million USD -- see our Notebook https://git.io/xbt and rerun the XBTXAU section.
Adriano
@rsvp

Polished https://git.io/gdpspx -- We examine the US gross domestic product's relationship to the US equity market (S&P500), in real terms. Forecasts for both are demonstrated using Holt-Winters time-series model. We derive the most likely range for real GDP growth, and identify extreme equity valuations aside from inflationary pressures.

Our analysis would suggest the following back of the envelope calculation: say, SPX nominally has an annual gain of 6% and inflation stands at 2%, then the real SPX gain is 4%. Take half of that to arrive at real GDP growth: 2%. Helpful because GDP numbers are announced after months of lag.

Adriano
@rsvp
Awesome listing... best-of Python libraries: https://github.com/vinta/awesome-python -- and our PR for inclusion is up for voting: vinta/awesome-python#878
Adriano
@rsvp
Equities are near all-time highs, yet over the decade, money managers are increasingly short. That capacity against the "uninformed" is being tested. Here we plot(get(w4cotr_equities)) which derives from CFTC Commitment of Traders Reports for combined pool of SP and ES futures and options representative of the S&P 500:
COTR equities 2017-04-26
Adriano
@rsvp
If fecon235 has been helpful to you, please show us +1 :+1: support at https://git.io/fe-awesome -- that awesome-python project lists many excellent resources worth checking out.
Adriano
@rsvp
NY Fed Reserve's general equilibrium model, rewrite in Julia: http://frbny-dsge.github.io/DSGE.jl/latest/julia_forecasting.html / Repo at https://github.com/FRBNY-DSGE/DSGE.jl but their Kalman state space models are refactored at https://github.com/FRBNY-DSGE/StateSpaceRoutines.jl
Adriano
@rsvp
By the way, details for the CFTC Commitment of Traders Reports Jupyter notebook is here: https://git.io/cotr discerns how various asset classes are positioned in the market.
Adriano
@rsvp
Nice post by @willkurt Kullback-Leibler Divergence Explained
Mark Song
@lnsongxf
Hi, I just want to know how to install fecon235, I tried "pip install fecon235", but it doesn't work. Thx.
Adriano
@rsvp

@lnsongxf hi, the preferred way for installation is just to clone the project under a directory in your PYTHONPATH. Best way to establish that editable variable is installing Anaconda beforehand which will resolve complex package dependencies. Then https://git.io/fecon-intro will help. Please message me if there are any other problems -- will revise the documentation accordingly.

Another way is non-native but direct cross-platform, Docker container : https://hub.docker.com/r/rsvp/fecon235/ -- Anaconda and git are pre-installed so just pull from origin/master to get latest fecon235 updates.

Adriano
@rsvp
The anomalies literature is infested with widespread p-hacking.” Even Fama and French are indicted… http://theinvestmentcapm.com/Slides_Replication_2017May_OhioState.pdf / Paper: http://www.nber.org/papers/w23394 [Hou 2017]