Notebooks for financial economics. Keywords: IPython notebook pandas statistics GDP inflation CPI PCE Fed FRED Ferbus unemployment wage income debt Case-Shiller housing asset portfolio equities SPX bonds TIPS rates currency Eurozone euro yen FX USD EUR JPY XAU gold Brent WTI oil Holt-Winters time-series forecasting econometrics
pandas can now easily read and analyze SEC 13F -- examples examine Druckenmiller and Paulson portfolios -- see SEC-13F-parse.ipynb
BTW. the main repo is here: https://github.com/rsvp/fecon235 -- includes time-series data from the Federal Reserve.
v2.15.1102 introduces two new notebooks: first examines gold contango. The second forecasts the Fed Funds rate one-year out using expected changes in LIBOR: https://github.com/rsvp/fecon235/blob/master/nb/qdl-libor-fed-funds.ipynb
Shortened to https://git.io/fedfunds -- currently forecasts Fed funds effective rate at 50 bp for Dec 2016, so one rate hike then trade in upper target range, OR two rate hikes within one year then trade in lower target range.
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.
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!
Here's another illustration of "Fix issue #2 with v4 and p6 upgrades": https://git.io/georet which concerns comparative geometric mean returns.
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().
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).
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).
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.
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.
Thanks to @samfisher83 for submitting our repo to ycombinator Hacker News: https://news.ycombinator.com/item?id=11240344
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
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...
@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.
A fecon235 user looks at trading algo module zipline: http://conrad.pythonanywhere.com/pydata/QuantFinance.html
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.