indiafactorlibrary 0.0.4

A Python library to fetch data from Invespar Factor library for Indian equities.

IndiaFactorLibrary

The IndiaFactorLibrary is a python package for remote data access for Invespar's Data Library: Fama-French Factors, Momentum, and Low-Risk Factors for the Indian Market.

Key Features

• Retrieve well-structured data sets with minimal code.
• Access both monthly and annual data.
• Focus on factors relevant to the Indian market.

Installation

To install IndiaFactorLibrary, ensure you have Python 3.6 or later and use pip:

pip install indiafactorlibrary

Requirements

Using IndiaFactorLibrary package requires the following packages:

• Python 3.6 or later
• pandas>=1.5.3
• lxml
• requests>=2.19.0

Usage

from indiafactorlibrary import IndiaFactorLibrary

# Create an instance of the library
ifl = IndiaFactorLibrary()

# Retrieve a list of available datasets
available_datasets = ifl.get_available_datasets()
print(available_datasets)

Available Datasets

The following datasets are available through the IndiaFactorLibrary as of April 2024. This list is automatically updated, and users can access each dataset by passing the relevant symbol to the read method.

Symbol	Description
ff4	Fama-French 4 factors
ff6	Fama-French 6 factors
size_value_portfolios	6 portfolios sorted by size and value (2x3)
size_op_portfolios	6 portfolios sorted by size and operating profitability (2x3)
size_inv_portfolios	6 portfolios sorted by size and investment (2x3)
size_mom_portfolios	6 portfolios sorted by size and momentum (2x3)
size_deciles	10 portfolios sorted by size
btm_deciles	10 portfolios sorted by book-to-market ratio
op_deciles	10 portfolios sorted by operating profitability
in_deciles	10 portfolios sorted by investment
mom_deciles	10 portfolios sorted by momentum
vol_deciles	10 portfolios sorted by volatility
size_btm_5x5	5x5 portfolios sorted by size and book-to-market ratio
size_op_5x5	5x5 portfolios sorted by size and operating profitability
size_inv_5x5	5x5 portfolios sorted by size and investment
size_mom_5x5	5x5 portfolios sorted by size and momentum
size_vol_5x5	5x5 portfolios sorted by size and volatility
low_risk_factors	Low-risk factors
low_risk_factors_vol	Low-risk factors based on realized volatility
low_risk_factors_bab_fp	Betting Against Beta (Frazzini-Pedersen methodology)
low_risk_factors_bab_ff	Ex-ante BAB methodology (Fama-French 2x3 construction)
low_risk_factors_bab_capm	BAB using CAPM Beta (FF 2x3 construction)
low_risk_factors_ivol	Low-risk factors using idiosyncratic volatility
f2_week_high	52-week high effect
cms_portfolios	CMS (Conservative Formula) portfolios
ff5_breakpoints	Breakpoints for Fama-French 5 factors
mom_breakpoints	Breakpoints for momentum factors
lovol_breakpoints	Breakpoints for low-volatility factors

Note: This list is updated automatically, and the datasets available may change over time as new data is added or removed.

Accessing Datasets

You can retrieve and analyze each dataset by calling the read method with the appropriate symbol:

from indiafactorlibrary import IndiaFactorLibrary

# Initialize the library
ifl = IndiaFactorLibrary()

# Read the Fama-French 4 factors dataset
dataset = ifl.read('ff4')

# Print the dataset description and an example DataFrame
print(dataset['DESCR'])
print(dataset[0].head())

The read method retrieves a dataset and returns it as a dictionary of DataFrames. Here's a detailed explanation of the output:

{0:                 MF     SMB     HML     WML      RF     MKT
 Dates                                                     
 2004-10-31  1.2168 -0.7685 -2.0792  2.7795  0.3877  1.6045
 2004-11-30  9.4784  2.2380  1.0583  1.6852  0.4361  9.9145
 ...         ...     ...     ...     ...     ...     ...
 2024-04-30  4.1476  7.2296  3.4824  3.0271  0.5466  4.6942

 [235 rows x 6 columns],
 1:           MF    SMB    HML    WML    RF    MKT
 Years                                         
 2005   34.40   9.26  -5.23  18.29  5.46  41.56
 2006   29.29  -6.02  -4.24  26.63  6.40  37.40
 ...     ...     ...     ...     ...     ...    ...
 2023   20.91  17.96  29.37  15.21  6.80  29.00,
 'DESCR': '"This file contains value-weighted monthly and annual returns for long-short factors. Annual factors are geometric "\n"January to December returns." "See Raju, Rajan, Four and Five-Factor Models in the Indian Equities Market (March 10, 2022). Available at SSRN: https://ssrn.com/abstract=4054146 for details."\n\n  0 : Long Short Returns -- Monthly (235 rows x 6 cols)\n  1 : Annual Factors: January-December (19 rows x 6 cols)'
}

Explanation: The `DESCR' dataframe shows the titles for the dataframes and their shape. In this case:

0 : Long Short Returns -- Monthly (235 rows x 6 cols)

1 : Annual Factors: January-December (19 rows x 6 cols)

Monthly Returns (0): Contains monthly returns for the long-short factors: MF, SMB, HML, WML, RF, and MKT. Indexed by Dates, with each row representing a monthly data point.

Annual Factors (1): Contains annual returns indexed by Years, with the same factors as above.

Description (DESCR): A brief textual explanation of the dataset, including methodology references and links to relevant research, where appropriate, and the keys for other dataframes in the dictionary. Note: The keys 0 and 1 represent different datasets returned from the same query. The structure may vary depending on the dataset queried.

Usage Tips

Accessing DataFrames: Use keys 0 and 1 to access the specific DataFrames directly, for example, in this instance:

monthly_returns = dataset[0]
annual_factors = dataset[1]

Viewing Metadata: Access the DESCR field to understand dataset structure and methodology.

Documentation

IndiaFactorLibrary Class (Invespar Data Library: Fama-French Factors, Momentum, and Low-Risk Factors for the Indian Market)

Methods:

• read(symbol):
  Read data for a given symbol.

  • Parameters:

    • symbol (str): The symbol for which to read the data.

  • Returns:

    • dict: A dictionary of DataFrames parsed from the data.

• get_available_datasets():
  Get the list of datasets available.

  • Returns:
    • list: A list of valid data files for the IndiaFactorLibrary.

Properties:

• url:
  API URL for data access.

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.