FRDS - Financial Research Data Services

frds is an open-sourced Python package for computing a collection of major academic measures used in the finance literature in a simple and straightforward way.

Installation

Install via `pip`

pip install frds -U

Install from source

git clone https://github.com/mgao6767/frds.git

Build and install the package locally.

cd frds
python setup.py build_ext --inplace
pip install -e .

On Windows, Microsoft Visual C++ Build Tools 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 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.

>>> 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) 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.

>>> 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/.

Data source integration

Additionally, frds provides an interface to load data from common data sources such as

Wharton Research Data Services (WRDS)
Refinitiv Tick History (formerly Thomson Reuters Tick History)
more to come...

WRDS

As an example, let's say we want to download the Compustat Fundamentals Annual dataset.

>>> 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:

>>> 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:

$ 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

ndinaemasutha/frds