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https://github.com/rsvp/fecon235

Notebooks for financial economics. Keywords: Jupyter notebook pandas Federal Reserve FRED Ferbus GDP CPI PCE inflation unemployment wage income debt Case-Shiller housing asset portfolio equities SPX bonds TIPS rates currency FX euro EUR USD JPY yen XAU gold Brent WTI oil Holt-Winters time-series forecasting statistics econometrics
https://github.com/rsvp/fecon235

asset-pricing bonds econometrics economics employment equities fecon236 federal-reserve finance fx gdp gold housing inflation interest-rates jupyter-notebook pandas python statistics time-series

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Notebooks for financial economics. Keywords: Jupyter notebook pandas Federal Reserve FRED Ferbus GDP CPI PCE inflation unemployment wage income debt Case-Shiller housing asset portfolio equities SPX bonds TIPS rates currency FX euro EUR USD JPY yen XAU gold Brent WTI oil Holt-Winters time-series forecasting statistics econometrics

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## fecon235 :: Notebooks for financial economics



[![Join the chat at https://gitter.im/rsvp/fecon235](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/rsvp/fecon235?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

/ [CHANGELOG] / [BSD License and TOS][235li]



**Spin-off Notice:** ***the modules and functions used by our research notebooks

have been refactored into another repository, [fecon236].***

The home for *our Jupyter notebooks will remain here at [fecon235]*

in the `nb` directory.



### What is this repository for?



**fecon235** provides an interface for ***financial economics*** to the Python

ecosystem, especially packages for mathematics, statistics, science,

engineering, and data analysis.

Complex packages such as *numpy, pandas, statsmodels, scipy, and matplotlib*

are seamlessly integrated at a high-level with APIs of various data hosts for:



- Essential commands which correctly handle annoying low-level pitfalls.



- Retrieval of economic and financial data, both historical and the most current. 



- Data munging, for example, resampling and alignment of time-series data

  from hosts using mutually incompatible formats.



- Analysis using techniques from econometrics, time-series analysis,

  and statistical machine learning.



- Abstraction and software optimization of mathematical operators,

  for example, linear algebra used in portfolio analysis.



- Visualization of data using graphical packages. 



- *Reproducible research which is collaborative and openly accessible

  at zero cost.*



To practically test theoretical ideas interactively,

our code can employed with any Python IDE interactive development

environment, IPython console, or with a Jupyter notebook.

The code has been tested against both python27 and python3 since 2014,

and works across major platforms: Linux, Mac, and Windows.



**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 retrieved from the best available sources using `pandas_datareader`.

Data is designed to be accessible *free* of charge,

and *interoperable* in different time frequencies.



### How does one get started?



* Good introductory lectures for economists to Python and its ecosystem:

  Quantitative Economics by Thomas [Sargent].



* For the fecon235 and fecon236 installation FAQ, please

  see https://git.io/econ which also covers external dependencies.



* For the older pre-2016 notebooks, please use import style 

  discussed in https://git.io/fecon-intro 



* Docker container (optional): instantly run fully-configured programs and 

  interactive notebooks; start by: `docker pull rsvp/fecon235` # see our 

  [Docker] image for details.  



Some basic commands like `get()` and `plot()`

will do a lot of the heavy lifting to get you started immediately. 

The commands are very easy to customize, 

producing sophisticated results quickly 

without tweaking the underlying numerical packages.



The *docs* directory should be gradually adding tutorials. 

The source code, in the meantime, is thoroughly annotated.



### Examples of code



The best way to see the code in action is to 

run the notebooks in the `nb` directory

which are described further below. 

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.



How is worker's wage correlated with GDP output? See https://git.io/gdpwage

How much Federal debt must each worker assume? And how fast is the

US government debt increasing? https://git.io/debtpop



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). 

Notebook https://git.io/infl gives an in-depth analysis of inflation,

including a combined forecast using three orthogonal methods.



Elevated default risk across bond markets is indicative of a

weak economy. But how can a policy maker calibrate credit spreads

to assess changes in interest rates? We consider mortgage and corporate

credit spreads to construct a *robust* Unified Credit Profile

(a tutorial on MAD, Median Absolute Deviation, in

rescaling non-Gaussian time-series), see https://git.io/creditprof



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.

In https://git.io/xbt Bitcoin is statistically analyzed as a financial asset.

We examine the crude oil markets, specifically the Brent over WTI spread,

and construct an optimal portfolio, in https://git.io/oil .



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.

In https://git.io/gdpspx we examine the close relationship between the

real economy and the equities market, while demonstrating

the Holt-Winters time-series model for predictions.



In https://git.io/gmix we analytically and visually show how a Gaussian

Mixture model handles "fat tail" risk of leptokurtotic financial assets

under small-sample conditions.

Markowitz portfolios, designed in the arithmetic mean-variance framework

for a static period, are notoriously fragile when markets change.

In contrast, our Boltzmann portfolios are adaptive over multi-periods to

*geometrically* maximize wealth using techniques from reinforcement learning.

Part 1: https://git.io/boltz1 Part 2: https://git.io/boltz2



Bootstrapping has two benefits: small-sample statistics

and simulation from controlled population.

In https://git.io/bootspx we simulate alternate histories

for leptokurtotic SPX equity returns:

to visualize sample price paths, and for

estimating probabilities of events such as investment loss.



### Development and contacts ###



* Guidelines: we welcome your [pull request] to improve our code. 

  Details are outlined in [Development].



* Lead developer is Adriano [rsvp.github.com][admin].

  Please join our chat with fellow users and developers at [Gitter].



This project is a derivative 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.

We are also grateful to [BIDS], Berkeley Institute for Data Science,

and the [Mathematical Sciences Group][MathSci] for their technical support.



![fecon235-wordclouds.jpg](https://git.io/fecon235words)



### Partial contents of nb directory ###



##### [gauss-mix-kurtosis.ipynb](https://github.com/rsvp/fecon235/blob/master/nb/gauss-mix-kurtosis.ipynb) : Gaussian Mixture and Leptokurtotic Assets



Gaussian Mixture GM(n) can create distributions with leptokurtosis ("fat

tails"). Our problem is the inverse: from observable statistics, deduce the

model parameters analytically. We demonstrate how a GM(2) model can

synthesize Gaussian risk-equivalence for leptokurtotic financial assets.

A numerical solution provides accurate probabilities which can be used to

experimentally understand how kurtosis itself is distributed under

small-sample conditions. The non-Gaussian distributions are visualized through

quantile-quantile probability plots. Shortcut: https://git.io/gmix



##### [boots-eq-spx.ipynb](https://github.com/rsvp/fecon235/blob/master/nb/boots-eq-spx.ipynb) : Bootstrap leptokurtotic SPX equity returns



Bootstrapping has two benefits: small-sample statistics

and simulation from controlled population.

We simulate alternate histories: to visualize sample price paths,

and for estimating probabilities of events such as investment loss.

Bootstrapping facilitates study of small-sample behaviour for which

asymptotic statistical theory is unsuitable, or where closed-form

mathematical analysis is intractable, for example, *geovolatility*

which is the volatility of the geometric mean rate.

Shortcut: https://git.io/bootspx



##### [prtf-boltzmann-1.ipynb](https://github.com/rsvp/fecon235/blob/master/nb/prtf-boltzmann-1.ipynb) : Boltzmann portfolios



We develop an alternative to the Markowitz framework

called Boltzmann portfolios which handle uncertainty from the 

standpoint of cross-entropy and optimal sequential decisions.

The improved result is a faster online algorithm which is more robust.

Markowitz portfolios are designed in the arithmetic mean-variance framework

for a static period, and are fragile to changing market conditions.

In contrast, Boltzmann portfolios are adaptive over multi-periods to

geometrically maximize wealth using techniques from reinforcement learning.

Part 1: https://git.io/boltz1 Part 2: https://git.io/boltz2



##### [qdl-spx-earn-div.ipynb](https://github.com/rsvp/fecon235/blob/master/nb/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](https://github.com/rsvp/fecon235/blob/master/nb/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](https://github.com/rsvp/fecon235/blob/master/nb/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 

change in spot prices. Observed strong correlations appear 

to be artificial. Shortcut: https://git.io/xau-contango



##### [qdl-xbt-bitcoin.ipynb](https://github.com/rsvp/fecon235/blob/master/nb/qdl-xbt-bitcoin.ipynb) : Statistical analysis of Bitcoin as financial asset



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



##### [qdl-COTR-positions.ipynb](https://github.com/rsvp/fecon235/blob/master/nb/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](https://github.com/rsvp/fecon235/blob/master/nb/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-credit-spreads.ipynb](https://github.com/rsvp/fecon235/blob/master/nb/fred-credit-spreads.ipynb) : Robust profiles of credit spreads



Serves as a tutorial on MAD, Median Absolute Deviation,

for 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. Shortcut: https://git.io/creditprof



##### [fred-debt-pop.ipynb](https://github.com/rsvp/fecon235/blob/master/nb/fred-debt-pop.ipynb) : Growth of Federal debt, its burden on the US population  



We examine US government debt in real terms, and the current Federal debt per capita. 

Shortcut: https://git.io/debtpop



##### [fred-employ-nfp.ipynb](https://github.com/rsvp/fecon235/blob/master/nb/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](https://github.com/rsvp/fecon235/blob/master/nb/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](https://github.com/rsvp/fecon235/blob/master/nb/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](https://github.com/rsvp/fecon235/blob/master/nb/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 (S&P 500), in real terms. Forecasts for both are demonstrated 

using the **Holt-Winters time-series model**. We derive the most likely range 

for real GDP growth, and identify extreme equity valuations aside from 

inflationary pressures. Shortcut: https://git.io/gdpspx



##### [fred-gdp-wage.ipynb](https://github.com/rsvp/fecon235/blob/master/nb/fred-gdp-wage.ipynb) : U.S. GDP vs. Wage Income 



How is wage correlated with GDP output?  In answering this question,

we derive a model for GDP growth based on observations from wage growth. 

Shortcut: https://git.io/gdpwage



##### [fred-georeturns.ipynb](https://github.com/rsvp/fecon235/blob/master/nb/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](https://github.com/rsvp/fecon235/blob/master/nb/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](https://github.com/rsvp/fecon235/blob/master/nb/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) from the bond market.

A unified inflation statistic *m4infl* is introduced,

which leads to the estimation of the geometric mean rate.

A robust optimized Holt-Winters model is used for forecasting.

Shortcut: https://git.io/infl



##### [fred-infl-unem-fed.ipynb](https://github.com/rsvp/fecon235/blob/master/nb/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. Shortcut: https://git.io/phillips



##### [fred-infl-velocity.ipynb](https://github.com/rsvp/fecon235/blob/master/nb/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](https://github.com/rsvp/fecon235/blob/master/nb/fred-oil-brent-wti.ipynb) : Oil: Brent vs. West Texas Intermediate (WTI)



We examine the history of crude oil prices, and their spreads.

A Boltzmann portfolio is computed for *optimal* financial positions.

Deflated prices give additional insight, along with some of the statistical

tools useful in financial economics.

Although WTI is more desirable than Brent from a petrochemical perspective,

that preference is reversed when the metrics are financial.

Shortcut: https://git.io/oil



##### [fred-usd-RTB-xau.ipynb](https://github.com/rsvp/fecon235/blob/master/nb/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](https://github.com/rsvp/fecon235/blob/master/nb/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](https://github.com/rsvp/fecon235/blob/master/nb/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](https://github.com/rsvp/fecon235/blob/master/nb/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



---



[BSD License and TOS][235li] / This page, last update : 2018-12-03



[admin]:        https://rsvp.github.com "Adriano rsvp.github.com"

[Anaconda]:     https://www.anaconda.com/download "Anaconda Python distribution"

[Development]:  https://github.com/MathSci/fecon236/blob/develop/.github/CONTRIBUTING.md "Development"

[Docker]:       https://hub.docker.com/r/rsvp/fecon235 "Docker image rsvp/fecon235"

[FRED]:         https://fred.stlouisfed.org "Federal Reserve Economic Data"

[Gitter]:       https://gitter.im/rsvp/fecon235 "Gitter fecon235"

[pull request]: https://help.github.com/articles/using-pull-requests/ "Pull request"

[Quandl]:       https://www.quandl.com  "Quandl, financial and economic data"

[Sargent]:      https://lectures.quantecon.org/py "Thomas Sargent, Quantitative Economics"

[wiki]:         https://github.com/rsvp/fecon235/wiki  "Wiki for fecon235"

[rsvp]: https://rsvp.github.com "Adriano rsvp.github.com"

[MathSci]: https://github.com/MathSci "Mathematical Sciences Group"

[BIDS]: https://bids.berkeley.edu "Berkeley Institute for Data Science"

[235is9]: https://github.com/rsvp/fecon235/issues/9 "fecon235 issue 9"

[fecon235]: https://github.com/rsvp/fecon235 "fecon235 repository"

[CHANGELOG]: https://git.io/235log "fecon235 Change Log"

[235li]: https://git.io/235li "fecon235 BSD License and TOS"

[fecon236]: https://github.com/MathSci/fecon236 "fecon236 repository"

[236li]: https://git.io/236li "fecon236 BSD License and TOS"

[236is]: https://git.io/236is "fecon236 issues"

[Travis]: https://travis-ci.org/MathSci/fecon236 "fecon236 at Travis CI"

[PyPI]: https://pypi.org/project/fecon236 "fecon236 at PyPI"