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## fecon235 :: Computational data tools for financial economics ##
## fecon235 : Computational data tools 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)

This is a free open source project for software tools
useful in financial economics. We develop research notebooks
in financial economics. We develop code for research notebooks
which are executable scripts capable of statistical
computations, as well as collecting raw data in real-time.
This serves to directly verify theoretical ideas and
practical methods.
computations, as well as, collection of raw data in real-time.
This serves to verify theoretical ideas and
practical methods interactively.

The project name derives from the seminar series held at the
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 in-depth studies.
Selected topics are treated for replicable analysis.


### What is this repository for? ###

- Source data, both historical and the most current.
- Economic and financial data, both historical and the most current.

- Easy data munging, e.g. resampling and alignment of time series.
- Data munging, for example, resampling and alignment of time series.

- Exploration of data using graphical packages.
- Analysis using techniques from econometrics and statistical machine learning.

- Analysis using techniques from econometrics and machine learning.
- Visualization of data using graphical packages.

- Presentation of mathematical results (in LaTex) and symbolic models.
- Reproducible research which is collaborative and openly accessible.

- Reproducible research which is collaborative and publicly 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.

**Database:** currently our main source is [FRED], the U.S. Federal Reserve
Economic Data bank which is accessed directly online using our programs.
Some specialized data can be directly retrieved using our [Quandl] API
module, for example, futures prices. Data for stocks, ETFs, and mutual
funds comes directly from Yahoo Finance, and falls back on Google Finance.
**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.

**Models:** we emphasize original research where the baseline comparison is to
*Ferbus*, the model used internally by the Federal Reserve Bank. Accuracy of
out-of-sample forecasts takes precedence over traditional measures of model
fit.

### How do I get set up? ###
### How does one get started? ###

* Deployment: for introduction to the Python ecosystem for financial
economists, the best reference for getting started is the
Quantitative Economics site by Thomas [Sargent].
* We rely primarily on Python, especially the Jupyter/IPython notebook
and pandas packages (though the R kernel may be used as needed).

* We rely primarily on Python, especially the Jupyter notebook and pandas
packages. The code is designed to be cross-platform for users, however,
for developers we assume a Linux environment.
R code may be used as needed, usually indirectly within wrappers.
* Deployment: the best reference to the Python ecosystem for financial economists
is the Quantitative Economics site by Thomas [Sargent].

* Configuration: we strongly recommend [Anaconda], a completely 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.
* Dependencies: pandas > 0.15 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).

* Dependencies: pandas > 0.15 is suggested.
All modules are now operational under
both Python 2 and 3. Also, code has been rewritten for
cross-platform performance (Linux, Mac, and Windows).
Our tests under the *Jupyter* fork of the IPython notebook
has not encountered any problems thus far.
* 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.

* Updates: for 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.

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.


### Examples of code ###

The best way to see the code in action is to run the
primary notebooks in the `nb` directory.
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.)
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 starts, home prices and affordibility](https://github.com/rsvp/fecon235/blob/master/nb/fred-housing.ipynb).
If you locally executed that notebook, it would
also seek out the latest available data to bring
the research up-to-date.
If you had executed that notebook locally, it would
have also retrieved the latest available data and
recomputed the results.

To see how we score the Federal Reserve's performance under its dual
To score the Federal Reserve's performance under its dual
mandate for inflation and unemployment, see https://git.io/fed
where the Phillips curve is discredited by constructing
heat map scatter plots.
(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.

Some basic commands,
e.g. get() and plot() in the fecon235 module, will do a lot of the heavy lifting
to get you started immediately.
They 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
for any clarifications.
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.

### Useful modules ###

These standalone Python *lib* modules are frequently imported
into our Python scripts:
### Useful modules ###

* yi_1tools.py : essential utility functions.
* yi_plot.py : essential plot functions.
* yi_timeseries : useful time series functions.
* yi_simulation : useful functions for simulation studies.
* yi_fred.py : Access FRED with pandas for plots and analysis.
* yi_quandl.py : Access Quandl with pandas for plots and analysis.
* yi_stocks.py : Access stocks and funds with pandas for plots and analysis.
These are some of our Python modules in the `lib` directory:

However, for Jupyter notebooks and interactive sesssions,
only one generalized module **fecon235.py** needs to be imported.
please see https://git.io/fecon-intro in *docs* for details.
The commands are very easy to learn and customize,
producing relatively sophisticated results quickly
without detailed knowledge of the underlying numerical packages.
* 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.

### Development ###
For Jupyter notebooks and interactive sesssions,
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.

* Code revisions: please kindly follow [Github flow].
You are also invited to directly add commentary
to existing notebooks.

* Running tests: details are in the `tests` directory.
For integration testing, we run all notebooks in batch mode.
This also syncs temporary notebooks with current data.
### Development and contacts ###

* Guidelines: we welcome your [pull request] to improve our code.
Please be sure to pull origin/master and rebase beforehand.

* Documents and data for fecon235 seminars: contact us if you need
help incorporating your material into this repository.
Details are outlined in [Development].

* For fecon235 presentations: contact us if you need help
incorporating your material into an auxiliary repository.

### Contact info ###

Repo [admin]
Lead developer is Adriano rsvp.github.com: [admin].
Please join our chat with fellow users and developers at [Gitter].


### Partial contents of nb directory ###
Expand Down Expand Up @@ -202,7 +193,7 @@ excessive equity valuations aside from inflationary pressures.
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](https://github.com/rsvp/fecon235/blob/master/nb/fred-georeturns.ipynb) : Geometric mean returns on FRED series
##### [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
Expand Down Expand Up @@ -277,17 +268,15 @@ Holt-Winters method.

- - - -

Revision date : 2015-12-30

Revision date : 2016-01-23

[admin]: https://rsvp.github.com "Adriano rsvp.github.com"
[wiki]: https://github.com/rsvp/fecon235/wiki "Wiki for fecon235"
[Anaconda]: http://continuum.io/downloads "Anaconda Python distribution"
[FRED]: http://research.stlouisfed.org/fred2/ "Federal Reserve Economic Data"
[Quandl]: https://www.quandl.com "Quandl, financial and economic data"
[Github flow]: http://scottchacon.com/2011/08/31/github-flow.html "Github Flow"
[admin]: https://rsvp.github.com "Adriano rsvp.github.com"
[Anaconda]: http://continuum.io/downloads "Anaconda Python distribution"
[Development]: https://github.com/rsvp/fecon235/blob/master/CONTRIBUTING.md "Development"
[FRED]: http://research.stlouisfed.org/fred2/ "Federal Reserve Economic Data"
[Gitter]: https://gitter.im/rsvp/fecon235 "Gitter fecon235"
[intro page]: http://rsvp.github.com/pg/fecon235-intro.html "fecon235 Introduction"
[pull request]: https://help.github.com/articles/using-pull-requests/ "Pull request"
[Sargent]: http://quant-econ.net/py "Thomas Sargent, Quantitative Economics"


[Quandl]: https://www.quandl.com "Quandl, financial and economic data"
[Sargent]: http://quant-econ.net/py "Thomas Sargent, Quantitative Economics"
[wiki]: https://github.com/rsvp/fecon235/wiki "Wiki for fecon235"

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