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frds-0.8.0
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Financial Research Data Services
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | frds-0.8.0 |
| نام | frds |
| نسخه کتابخانه | 0.8.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Mingze Gao |
| ایمیل نویسنده | adrian.gao@outlook.com |
| آدرس صفحه اصلی | https://github.com/mgao6767/frds/ |
| آدرس اینترنتی | https://pypi.org/project/frds/ |
| مجوز | MIT |
# FRDS - Financial Research Data Services
 [](https://github.com/psf/black) 
[frds](https://github.com/mgao6767/frds/) is an open-sourced Python package for computing [a collection of major academic measures](https://frds.io/measures/) used in the finance literature in a simple and straightforward way.
## Installation
### Install via `pip`
```bash
pip install frds -U
```
### Install from source
``` bash
git clone https://github.com/mgao6767/frds.git
```
Build and install the package locally.
``` bash
cd frds
python setup.py build_ext --inplace
pip install -e .
```
On Windows, [Microsoft Visual C++ Build Tools](https://visualstudio.microsoft.com/downloads/#build-tools-for-visual-studio-2019) may need to be installed so that the C/C++ extensions in the package can be compiled.
## Note
This library is still under development and breaking changes may be expected.
## Built-in measures
The primary purpose of `frds` is to offer ready-to-use functions used in researches.
For example, Kritzman, Li, Page, and Rigobon (2010) propose an [Absorption Ratio](https://frds.io/measures/absorption_ratio/) that measures the fraction of the total variance of a set of asset returns explained or absorbed by a fixed number of eigenvectors. It captures the extent to which markets are unified or tightly coupled.
``` python
>>> import numpy as np
>>> from frds.measures import absorption_ratio
>>> data = np.array( # Hypothetical 6 daily returns of 3 assets.
... [
... [0.015, 0.031, 0.007, 0.034, 0.014, 0.011],
... [0.012, 0.063, 0.027, 0.023, 0.073, 0.055],
... [0.072, 0.043, 0.097, 0.078, 0.036, 0.083],
... ]
... )
>>> absorption_ratio(data, fraction_eigenvectors=0.2)
0.7746543307660252
```
Another example, [Distress Insurance Premium (DIP)](https://frds.io/measures/distress_insurance_premium/) proposed by Huang, Zhou, and Zhu (2009) as a systemic risk measure of a hypothetical insurance premium against a systemic financial distress, defined as total losses that exceed a given threshold, say 15%, of total bank liabilities.
``` python
>>> from frds.measures import distress_insurance_premium
>>> # hypothetical implied default probabilities of 6 banks
>>> default_probabilities = np.array([0.02, 0.10, 0.03, 0.20, 0.50, 0.15]
>>> correlations = np.array(
... [
... [ 1.000, -0.126, -0.637, 0.174, 0.469, 0.283],
... [-0.126, 1.000, 0.294, 0.674, 0.150, 0.053],
... [-0.637, 0.294, 1.000, 0.073, -0.658, -0.085],
... [ 0.174, 0.674, 0.073, 1.000, 0.248, 0.508],
... [ 0.469, 0.150, -0.658, 0.248, 1.000, -0.370],
... [ 0.283, 0.053, -0.085, 0.508, -0.370, 1.000],
... ]
... )
>>> distress_insurance_premium(default_probabilities, correlations)
0.28661995758
```
For a complete list of supported built-in measures, please check [frds.io/measures/](https://frds.io/measures/).
## Data source integration
Additionally, `frds` provides an interface to load data from common data sources such as
- [Wharton Research Data Services (WRDS)](https://wrds-web.wharton.upenn.edu/wrds/)
- [Refinitiv Tick History (formerly Thomson Reuters Tick History)](https://www.refinitiv.com/en/market-data/data-feeds/tick-history)
- more to come...
### WRDS
As an example, let's say we want to download the Compustat Fundamentals Annual dataset.
``` python
>>> from frds.data.wrds.comp import Funda
>>> from frds.io.wrds import load
>>> FUNDA = load(Funda, use_cache=True, obs=100)
>>> FUNDA.data.head()
FYEAR INDFMT CONSOL POPSRC DATAFMT TIC CUSIP CONM ... PRCL_F ADJEX_F RANK AU AUOP AUOPIC CEOSO CFOSO
GVKEY DATADATE ...
001000 1961-12-31 00:00:00.000000 1961.0 INDL C D STD AE.2 000032102 A & E PLASTIK PAK INC ... NaN 3.341831 NaN None None None None None
1962-12-31 00:00:00.000000 1962.0 INDL C D STD AE.2 000032102 A & E PLASTIK PAK INC ... NaN 3.341831 NaN None None None None None
1963-12-31 00:00:00.000000 1963.0 INDL C D STD AE.2 000032102 A & E PLASTIK PAK INC ... NaN 3.244497 NaN None None None None None
1964-12-31 00:00:00.000000 1964.0 INDL C D STD AE.2 000032102 A & E PLASTIK PAK INC ... NaN 3.089999 NaN None None None None None
1965-12-31 00:00:00.000000 1965.0 INDL C D STD AE.2 000032102 A & E PLASTIK PAK INC ... NaN 3.089999 NaN None None None None None
[5 rows x 946 columns]
```
We can then compute some measures on the go:
``` python
>>> tangibility = FUNDA.PPENT / FUNDA.AT
>>> type(tangibility)
<class 'pandas.core.series.Series'>
>>> tangibility.sample(10).sort_index()
GVKEY DATADATE
001000 1965-12-31 00:00:00.000000 0.604762
1967-12-31 00:00:00.000000 0.539495
1968-12-31 00:00:00.000000 0.654171
1977-12-31 00:00:00.000000 0.452402
001001 1985-12-31 00:00:00.000000 0.567439
001003 1980-12-31 00:00:00.000000 NaN
1988-01-31 00:00:00.000000 0.073495
001004 1967-05-31 00:00:00.000000 0.175518
1980-05-31 00:00:00.000000 0.183682
1982-05-31 00:00:00.000000 0.286231
dtype: float64
```
### Refinitiv Tick History
`frds` provides a dedicated command-line tool `frds-mktstructure`.
Use `-h` or `--help` to see the usage instruction:
``` bash title="frds-mktstructure can be used without programming"
$ frds-mktstructure -h
usage: frds-mktstructure [OPTION]...
Download data from Refinitiv Tick History and compute some market microstructure measures.
optional arguments:
-h, --help show this help message and exit
-v, --version show program's version number and exit
Sub-commands:
Choose one from the following. Use `frds-mktstructure subcommand -h` to see help for each sub-command.
{download,clean,classify,compute}
download Download data from Refinitiv Tick History
clean Clean downloaded data
classify Classify ticks into buy and sell orders
compute Compute market microstructure measures
```
نحوه نصب
نصب پکیج whl frds-0.8.0:
pip install frds-0.8.0.whl
نصب پکیج tar.gz frds-0.8.0:
pip install frds-0.8.0.tar.gz