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[](https://pypi.org/project/ffn/)
[](https://pypi.org/project/ffn/)
# ffn - Financial Functions for Python
Alpha release - please let me know if you find any bugs!
If you are looking for a full backtesting framework, please check out [bt](https://github.com/pmorissette/bt).
bt is built atop ffn and makes it easy and fast to backtest quantitative strategies.
## Overview
ffn is a library that contains many useful functions for those who work in **quantitative
finance**. It stands on the shoulders of giants (Pandas, Numpy, Scipy, etc.) and provides
a vast array of utilities, from performance measurement and evaluation to
graphing and common data transformations.
```python
import ffn
returns = ffn.get('aapl,msft,c,gs,ge', start='2010-01-01').to_returns().dropna()
returns.calc_mean_var_weights().as_format('.2%')
aapl 62.54%
c -0.00%
ge 36.19%
gs -0.00%
msft 1.26%
dtype: object
```
## Installation
The easiest way to install `ffn` is from the [Python Package Index](https://pypi.python.org/pypi/ffn/)
using `pip`.
```bash
pip install ffn
```
Since ffn has many dependencies, we strongly recommend installing the [Anaconda Scientific Python Distribution](https://store.continuum.io/cshop/anaconda/). This distribution comes with many of the required packages pre-installed, including pip. Once Anaconda is installed, the above command should complete the installation.
## Documentation
Read the docs at http://pmorissette.github.io/ffn
- [Quickstart](http://pmorissette.github.io/ffn/quick.html)
- [Full API](http://pmorissette.github.io/ffn/ffn.html)
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"description": "\n\n[](https://github.com/pmorissette/ffn/actions/)\n[](https://pypi.org/project/ffn/)\n[](https://pypi.org/project/ffn/)\n\n# ffn - Financial Functions for Python\n\nAlpha release - please let me know if you find any bugs!\n\nIf you are looking for a full backtesting framework, please check out [bt](https://github.com/pmorissette/bt).\nbt is built atop ffn and makes it easy and fast to backtest quantitative strategies.\n\n## Overview\n\nffn is a library that contains many useful functions for those who work in **quantitative\nfinance**. It stands on the shoulders of giants (Pandas, Numpy, Scipy, etc.) and provides\na vast array of utilities, from performance measurement and evaluation to\ngraphing and common data transformations.\n\n```python\nimport ffn\nreturns = ffn.get('aapl,msft,c,gs,ge', start='2010-01-01').to_returns().dropna()\nreturns.calc_mean_var_weights().as_format('.2%')\n aapl 62.54%\n c -0.00%\n ge 36.19%\n gs -0.00%\n msft 1.26%\n dtype: object\n```\n\n\n## Installation\n\nThe easiest way to install `ffn` is from the [Python Package Index](https://pypi.python.org/pypi/ffn/)\nusing `pip`.\n\n```bash\npip install ffn\n```\n\nSince ffn has many dependencies, we strongly recommend installing the [Anaconda Scientific Python Distribution](https://store.continuum.io/cshop/anaconda/). This distribution comes with many of the required packages pre-installed, including pip. Once Anaconda is installed, the above command should complete the installation.\n\n## Documentation\n\nRead the docs at http://pmorissette.github.io/ffn\n\n- [Quickstart](http://pmorissette.github.io/ffn/quick.html)\n- [Full API](http://pmorissette.github.io/ffn/ffn.html)\n",
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