frds 0.8.0

Financial Research Data Services

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