This is a free open source project for software tools in financial economics. We develop code for research notebooks which are executable scripts capable of statistical computations, as well as, collection of raw data in real-time. This serves to verify theoretical ideas and practical methods interactively.
The project derives from the seminar series held at the University of California at Berkeley, jointly sponsored by the Department of Economics and the Haas School of Business. Selected topics are treated for replicable analysis.
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Economic and financial data, both historical and the most current.
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Data munging, for example, resampling and alignment of time series.
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Analysis using techniques from econometrics and statistical machine learning.
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Visualization of data using graphical packages.
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Reproducible research which is collaborative and openly accessible.
Database: the primary source is FRED, the U.S. Federal Reserve Economic Data bank which is accessed directly online using our interface. Other specialized data can be directly retrieved using our Quandl API module, for example, futures prices. Data for stocks, mutual funds, and ETFs is sourced from Yahoo Finance, but falls back on Google Finance. All data access is designed to be completely free of charge.
Models: our baseline is Ferbus, the model used internally by the Federal Reserve Bank, however at fecon235, the accuracy of out-of-sample forecasts takes precedence over traditional measures of model fit. We also develop tools for asset pricing and portfolio optimization, in addition to econometric models.
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We rely primarily on Python, especially the Jupyter/IPython notebook and pandas packages (though the R kernel may be used as needed).
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Deployment: the best reference to the Python ecosystem for financial economists is the Quantitative Economics site by Thomas Sargent.
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Dependencies: pandas > 0.16 is highly recommended. All modules are tested against both Python 2.7 and 3 series. User code has been rewritten for cross-platform performance (Linux, Mac, and Windows).
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Configuration: we strongly recommend Anaconda, a free Python distribution which includes about 200 of the most useful Python packages for science, math, engineering, data analysis. It will resolve your headaches due to dependency hell.
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Updates: for pre-2016 notebooks, please use import style discussed in docs README: https://git.io/fecon-intro
Some basic commands, e.g. get() and plot() in the fecon235 top module, will do a lot of the heavy lifting to get you started immediately. The commands are designed for scripts (not necessarily within Jupyter notebooks) and any Python IDE interactive development environment.
The docs directory and our wiki should be gradually adding tutorials and FAQs. The source code, in the meantime, is thoroughly self-documenting.
The best way to see the code in action is to
run the notebooks in the nb directory.
Some of them are described at the end of this page.
Note that GitHub can render Jupyter notebooks directly in the browser,
however, they will not be executable.
Here is a rendering of a notebook at GitHub for Housing economy, home prices and affordibility https://git.io/housing If you had executed that notebook locally, it would have also retrieved the latest available data and recomputed the results.
To score the Federal Reserve's performance under its dual mandate for inflation and unemployment, see https://git.io/fed (where tangentially the Phillips curve is discredited by constructing heat map scatter plots). Please see https://git.io/fedfunds to forecast the Fed Funds rate using futures contracts on LIBOR.
The notebook https://git.io/cotr discerns how various asset classes are positioned in the market. In contrast, an overview of asset prices is given in https://git.io/georet using geometric mean returns.
In https://git.io/gold we make a conjecture that real gold prices is a stationary time-series bound by real interest rates.
SEC 13F filings can be easily parsed, see https://git.io/13F where we track asset managers Stanley Druckenmiller and John Paulson.
In https://git.io/equities we examine the separable components of total return for equities, especially due to enterprise earnings and market speculation, using S&P data assembled by Robert Shiller which goes back to the year 1871.
These are some of our Python modules in the lib directory:
- yi_1tools : essential utility functions.
- yi_plot : plot functions and visualizations.
- yi_timeseries : time series functions and filters.
- yi_simulation : building blocks for simulations.
- yi_fred : Freely access FRED Federal Reserve data with pandas.
- yi_quandl : Access free Quandl and government data with pandas.
- yi_stocks : Get stock, mutual fund, and ETF quotes with pandas.
For Jupyter notebooks and interactive sessions, only one module fecon235 needs to be imported; please consult https://git.io/fecon-intro for details. The commands are very easy to customize, producing sophisticated results quickly without tweaking the underlying numerical packages.
- ys_optimize : global "optimize" function integrates a coarse grid search, then unconstrained Nelder-Mead simplex method, and finally the refined L-BFGS-B method which approximates a low-rank Hessian so that we can work in high (>250) dimensions. Easy to use for estimating model parameters with arbitrary loss functions; see tests/test_optimize.py for quick tutorial.
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Guidelines: we welcome your pull request to improve our code. Details are outlined in Development.
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For fecon235 presentations: contact us if you need help incorporating your material into an auxiliary repository.
Lead developer is Adriano rsvp.github.com: admin. Please join our chat with fellow users and developers at Gitter.
qdl-spx-earn-div.ipynb : Separable components of total return for equities
We specify a model for equity returns by decomposition into enterprise and speculative returns, plus dividend yield. That model is then tested using stock market data going back to the year 1871 (well-known database assembled by Robert Shiller). An understanding of their respective contributions helps us to form better informed expectations of total return for equities. We demonstrate that the (arithmetic) percentage reasoning is prone is large errors, whereas a logarithmic (geometric) version is exact. Shortcut: https://git.io/equities or https://git.io/spx
qdl-libor-fed-funds.ipynb : Use pandas to analyze short-term rates
We examine the spread between two interest rates: LIBOR and Fed Funds. The former has a much greater depth in the futures market in terms of volume and maturity horizon, implying richer information content. Modeling their relationship, we construct a synthetic forward Fed Funds rate, useful in gauging market sentiment regarding Fed policy. Estimate is given for the change in Fed Funds rate over the next 12 months. Shortcut: https://git.io/fedfunds
qdl-xau-contango.ipynb : Use pandas to analyze gold contango
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 using gold futures and LIBOR. This supply/demand indicator is then compared against spot prices.
qdl-COTR-positions.ipynb : Use pandas to read CFTC COTR
Commitment of Traders Report (COTR) is useful to extract market positions in precious metals, US dollar, interest rates, and equities markets. We develop our own scale-free measures to gauge market sentiment across time which can diverge from price directionality at interesting points. Shortcut: https://git.io/cotr
SEC-13F-parse.ipynb : Use pandas to read 13F filings from SEC
Sort percentage allocation to long equities. Caveats are noted for portfolio management. Module yi_secform easily sums up 13F filings by one function. For illustration, we follow asset managers with significant positions in GLD, a gold ETF; see Stanley Druckenmiller's sudden accumulation, and John Paulson's dramatic liquidation. Shortcut: https://git.io/13F
fred-debt-pop.ipynb : Growth of Federal debt, its burden on the US population
We examine government debt in real terms, and the current debt per capita.
fred-employ-nfp.ipynb : US employment data, Nonfarm Payroll
We focus on forecasting the monthly change in NFP using a variety of optics: baseline expectation since 1939, Holt-Winters method, visual selection of local range, regression against economic activity (SPX) -- but the standard errors are inherently very large due to survey measurement error.
fred-eur-fx.ipynb : Euro currency qua Foreign Exchange
We examine euro FX data from the Fed Reserve FRED database. Our synthetic time-series, which takes us far back as 1971, give additional perspective to observe the cross-rates against U.S. dollar and Japanese yen.
fred-eurozone.ipynb : Eurozone economics
We examine the usual suspects: unemployment, inflation, real interest rate, foreign exchange rate, comparative GDP. Appendix 1 concisely explains the euro crisis in a video.
fred-gdp-spx.ipynb : US real GDP vs. SPX: Holt-Winters time series forecasting
We examine the US gross domestic product's relationship to the US equity market, in real terms. Forecasts for both are demonstrated using Holt-Winters technique. We derive the most likely range for real GDP growth, and identify excessive equity valuations aside from inflationary pressures.
fred-gdp-wage.ipynb : U.S. GDP vs. Wage Income
For every wage dollar paid, what is GDP output? In answering this question, we derive a model for GDP growth based on observations from wage growth.
fred-georeturns.ipynb : Comparative geometric mean returns
We examine economic and financial time series where Holt-Winters is used to forecast one-year ahead. Daily data for bonds, equity, and gold is then analyzed. The focus is on geometric mean returns because they optimally express mean-variance under logarithmic utility. Shortcut: https://git.io/georet
fred-housing.ipynb : Housing economy, home prices and affordibility
Alan Greenspan in 2014 pointed out that there was never a recovery from recession without improvements in housing construction. Here we examine some relevant data, including the Case-Shiller series, and derive an insightful measure of the housing economy, hscore, which takes affordibility into account. Shortcut: https://git.io/housing
fred-inflation.ipynb : Inflation data from FRED using pandas
We examine inflation data: CPI and PCE, including the core versions, along with the 10-year BEI rate (break-even inflation). We also examine gold returns and its correlations to inflation. A combined inflation statistic m4infl is defined, and we make some forecasts. Shortcut: https://git.io/infl
fred-infl-unem-fed.ipynb : Score for the Fed's dual mandate
We examine unemployment and inflation data to construct a time-series which gives a numerical score to the Fed's performance on its dual mandate. The key is to find comparable units to measure performance and a suitable scalar measure to show deviation from the dual mandate. The visualization includes sequential scatter plots using color heat map, which can be extended to studies of the Phillips curve.
fred-infl-velocity.ipynb : Inflation, money velocity, and interest rates
We examine and attempt to replicate the results of two interesting articles by Yi Wen and Maria Arias -- along the way, we take a detour and explore the connection between money velocity and bond rates. This will tie together their relationship with GDP and the money supply in a fitted equation.
fred-oil-brent-wti.ipynb : Oil: Brent vs. West Texas Intermediate (WTI)
We examine the history of oil prices, and their spreads. Real prices give additional insight, along with some of the statistical characteristics used in financial economics.
fred-usd-RTB-xau.ipynb : Real trade-weighted indexes for USD, gold, and SPX
We examine the value of USD against a basket of 26 foreign currencies using real trade numbers. Trade statistics are released annually, however, the Fed uses international inflation data to adjust the weights monthly.
fred-wage-capital.ipynb : Real capital equivalence to wage-income
We determine how much real capital has been necessary for risk-free interest to match annual wage.
fred-xau-spx.ipynb : Gold vs. SP500 returns, XAU vs. SPX
Long-term comparison of two asset classes: we boxplot their return distribution and also compute geometric mean returns. Correlation between the two is shown to be nil. We then look at the history of projected returns using Holt-Winters method, which also gives the latest forecasts. To conclude, we closely examine the relative value of the two assets in terms of gold troy ounces per equity share. Analytically short equities vs. long gold is favorable for an investor with log utility, but hardly profitable over the long haul.
fred-xau-tips.ipynb : Gold and Treasury TIPS, their daily relationship
Using monthly data we previously found that there is strong correlation between gold and real rates, so we investigate this relationship on a daily frequency. We then use this correlation to help make forecasts using the Holt-Winters time-series method. Lastly, we show the history of gold prices in real terms which leads to our conjecture that real gold is a stationary time-series bound by real interest rates. Shortcut: https://git.io/gold
Revision date : 2016-05-25