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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 Macquarie University, initially started as as a personal project during his postdoctoral research fellowship at the University of Sydney.

pip install frds

This library is still under development and breaking changes may be expected.

If there's any issue (likely), please contact me atmingze.gao@mq.edu.au

For a complete list of supported built-in measures, please checkfrds.io/measures/ andfrds.io/algorithms.

Supported Measures

• 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

Algorithms

• GARCH(1,1)
• GARCH(1,1) - CCC
• GARCH(1,1) - DCC
• GJR-GARCH(1,1)
• GJR-GARCH(1,1) - DCC

Some simple examples.

For example, Kritzman, Li, Page, and Rigobon (2010) propose anAbsorption 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 npfrom 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

Usefrds.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)