FRDS - Financial Research Data Services
frds is a Python library to simplify the complexities often encountered in financial research. It provides a collection of ready-to-use methods for computing a wide array of measures in the literature.
It is developed by Dr. Mingze Gao from the University of Sydney, as a personal project during his postdoctoral research fellowship.
Installation
pip install frds
Note
This library is still under development and breaking changes may be expected.
If there's any issue (likely), please contact me at mingze.gao@sydney.edu.au
Supported measures and algorithms
For a complete list of supported built-in measures, please check frds.io/measures/ and frds.io/algorithms.
- Absorption Ratio
- Contingent Claim Analysis
- Distress Insurance Premium
- Lerner Index (Banks)
- Long-Run Marginal Expected Shortfall (LRMES)
- Marginal Expected Shortfall
- Option Prices
- SRISK
- Systemic Expected Shortfall
- Z-score
- GARCH(1,1)
- GARCH(1,1) - CCC
- GARCH(1,1) - DCC
- GJR-GARCH(1,1)
- GJR-GARCH(1,1) - DCC
Examples
Some simple examples.
Absorption Ratio
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 AbsorptionRatio
>>> 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],
... ]
... )
ar = AbsorptionRatio(data)
ar.estimate()
0.7746543307660252
Bivariate GARCH-CCC
Use frds.algorithms.GARCHModel_CCC to estimate a bivariate Constant Conditional Correlation (CCC) GARCH model. The results are as good as those obtained in Stata, marginally better based on log-likelihood.
>>> import pandas as pd
>>> from pprint import pprint
>>> from frds.algorithms import GARCHModel_CCC
>>> data_url = "https://www.stata-press.com/data/r18/stocks.dta"
>>> df = pd.read_stata(data_url, convert_dates=["date"])
>>> nissan = df["nissan"].to_numpy() * 100
>>> toyota = df["toyota"].to_numpy() * 100
>>> model_ccc = GARCHModel_CCC(toyota, nissan)
>>> res = model_ccc.fit()
>>> pprint(res)
Parameters(mu1=0.02745814255283541,
omega1=0.03401400758840226,
alpha1=0.06593379740524756,
beta1=0.9219575443861723,
mu2=0.009390068254041505,
omega2=0.058694325049554734,
alpha2=0.0830561828957614,
beta2=0.9040961791372522,
rho=0.6506770477876749,
loglikelihood=-7281.321453218112)
1 maintainer
Release files for frds 2.4.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| frds-2.4.1.tar.gz | 182.2 kB | Details |
Built distributions (wheels)
Total release size: 10.0 MB
Release files / frds-2.4.1.tar.gz
| Download URL | frds-2.4.1.tar.gz |
|---|---|
| Size | 182.2 kB |
| Tags | Source |
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Release files / frds-2.4.1-cp311-cp311-win_amd64.whl
| Download URL | frds-2.4.1-cp311-cp311-win_amd64.whl |
|---|---|
| Size | 228.5 kB |
| Tags | CPython 3.11 Windows x86-64 |
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Release files / frds-2.4.1-cp311-cp311-win32.whl
| Download URL | frds-2.4.1-cp311-cp311-win32.whl |
|---|---|
| Size | 224.4 kB |
| Tags | CPython 3.11 Windows x86-32 |
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| Download URL | frds-2.4.1-cp311-cp311-musllinux_1_1_x86_64.whl |
|---|---|
| Size | 1.2 MB |
| Tags | CPython 3.11 Linux musl 1.1+ x86-64 |
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| Download URL | frds-2.4.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl |
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| Size | 603.7 kB |
| Tags | CPython 3.11 Linux glibc 2.17+ x86-64 |
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| Download URL | frds-2.4.1-cp310-cp310-win_amd64.whl |
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