A Python library for analysis of fixed income instruments in Brazil
Project description
PYield: Brazilian Fixed Income Analysis Library
Introduction
Welcome to PYield, a Python library designed for the analysis of fixed income instruments in Brazil. This library is tailored for financial analysts, researchers, and enthusiasts interested in the Brazilian fixed income market. Leveraging the power of popular Python libraries like Pandas and Requests, PYield simplifies the process of obtaining and processing data from key sources such as Tesouro Nacional (TN), Banco Central (BC), ANBIMA, and B3.
Features
- Data Collection: Automated fetching of data from TN, BC, ANBIMA, and B3.
- Data Processing: Efficient processing and normalization of fixed income data.
- Analysis Tools: Built-in functions for common analysis tasks in fixed income markets.
- Easy Integration: Seamless integration with Python data analysis workflows.
Installation
You can install PYield using pip:
pip install pyield
How to use PYield
Getting DI Futures Data
import pyield as pyd
# Get a pandas dataframe with the DI raw data from B3 (first date available is 05-06-1991)
>>> pyd.di(reference_date='2024-01-15', raw=True)
VENCTO CONTR. ABERT.(1) ... ÚLT.OF. COMPRA ÚLT.OF. VENDA
G24 796903 ... 11.650 11.656
H24 548377 ... 11.346 11.352
... ... ... ... ...
# Get a pandas dataframe with the DI processed data from B3 (default)
>>> pyd.di(reference_date='2024-01-15')
contract_code expiration bdays ... last_offer settlement_rate
G24 2024-02-01 13 ... 11.656 11.650
H24 2024-03-01 32 ... 11.352 11.349
... ... ... ... ... ...
Business Days Tools (Brazilian holidays are automatically considered)
# Generate a pandas series with the business days between two dates
>>> pyd.generate_bdays(start='2023-12-29', end='2024-01-03')
0 2023-12-29
1 2024-01-02
2 2024-01-03
dtype: datetime64[ns]
# Get the next business day after a given date (offset=1)
>>> pyd.offset_bdays(dates="2023-12-29", offset=1)
Timestamp('2024-01-02 00:00:00')
# Get the next business day if it is not a business day (offset=0)
>>> pyd.offset_bdays(dates="2023-12-30", offset=0)
Timestamp('2024-01-02 00:00:00')
# Since 2023-12-29 is a business day, it returns the same date (offset=0)
>>> pyd.offset_bdays(dates="2023-12-29", offset=0)
Timestamp('2023-12-29 00:00:00')
# Count the number of business days between two dates
# Start date is included, end date is excluded
>>> pyd.count_bdays(start='2023-12-29', end='2024-01-02')
1
Documentation
For detailed documentation on all features and functionalities, please visit PYield Documentation. Contributing
Contributions to PYield are welcome! Please read our Contributing Guidelines for details on how to submit pull requests, report issues, or suggest enhancements. License
PYield is licensed under the MIT License. Acknowledgments
PYield was developed with the support of the Python community and financial analysts in Brazil. Special thanks to the maintainers of Pandas and Requests for their invaluable libraries.
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