PYield 0.7.3

A Python library for analysis of fixed income instruments in Brazil

PYield: Fixed Income Toolbox for Brazilian Markets

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 ANBIMA, BCB, IBGE and B3.

Features

• Data Collection: Automated fetching of data from 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 pandas data analysis workflows.
• Type Hints: Full support for static type checking, enhancing development experience and code quality.

Installation

You can install PYield using pip:

pip install pyield

How to use PYield

Business Days Tools (Brazilian holidays are automatically considered)

>>> import pyield as yd

# Count the number of business days between two dates
# Start date is included, end date is excluded
>>> yd.count_bdays(start='2023-12-29', end='2024-01-02')
1

# Get the next business day after a given date (offset=1)
>>> yd.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)
>>> yd.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)
>>> yd.offset_bdays(dates="2023-12-29", offset=0)
Timestamp('2023-12-29 00:00:00')

# Generate a pandas series with the business days between two dates
>>> yd.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]

Futures Data

# Fetch a DataFrame with the DI Futures data from B3
>>> yd.fetch_asset(asset_code="DI1", reference_date='2024-03-08')

TradeDate  TickerSymbol ExpirationDate BDaysToExp ... LastRate LastAskRate LastBidRate SettlementRate
2024-03-08 DI1J24            2024-04-01     15                ... 10.952   10.952      10.956      10.956
2024-03-08 DI1K24            2024-05-02     37                ... 10.776   10.774      10.780      10.777
2024-03-08 DI1M24            2024-06-03     58                ... 10.604   10.602      10.604      10.608
...        ...            ...            ...               ... ...      ...         ...         ...
2024-03-08 DI1F37            2037-01-02     3213              ... <NA>     <NA>        <NA>        10.859
2024-03-08 DI1F38            2038-01-04     3462              ... <NA>     <NA>        <NA>        10.859
2024-03-08 DI1F39            2039-01-03     3713              ... <NA>     <NA>        <NA>        10.85

Treasury Bonds Data

# Fetch a DataFrame with the NTN-B data from ANBIMA
# Anbima data is available for the last 5 working days
# Obs: Anbima members have access to the full history
>>> yd.fetch_asset(asset_code="NTN-B", reference_date='2024-04-12')

BondType ReferenceDate MaturityDate BidRate AskRate IndicativeRate Price
NTN-B    2024-04-12    2024-08-15   0.07540 0.07504 0.07523        4,271.43565
NTN-B    2024-04-12    2025-05-15   0.05945 0.05913 0.05930        4,361.34391
NTN-B    2024-04-12    2026-08-15   0.05927 0.05897 0.05910        4,301.40082
...      ...           ...          ...     ...     ...            ...
NTN-B    2024-04-12    2050-08-15   0.06039 0.06006 0.06023        4,299.28233
NTN-B    2024-04-12    2055-05-15   0.06035 0.05998 0.06017        4,367.13360
NTN-B    2024-04-12    2060-08-15   0.06057 0.06016 0.06036        4,292.26323

Spreads Calculation

# Calculate the spread between two DI Futures contracts and the pre-fix bonds
>>> yd.calculate_spreads(spread_type="di_vs_pre", reference_date="2024-4-11")

BondType ReferenceDate MaturityDate  DISpread
LTN      2024-04-11    2024-07-01    -20.28000
LTN      2024-04-11    2024-10-01    -10.19000
LTN      2024-04-11    2025-01-01    -15.05000
...      ...           ...           ...
NTN-F    2024-04-11    2031-01-01    -0.66000
NTN-F    2024-04-11    2033-01-01    -5.69000
NTN-F    2024-04-11    2035-01-01    -1.27000

Indicators Data

# Fetch the SELIC target rate from the Central Bank of Brazil
>>> yd.fetch_indicator(indicator_code="SELIC", reference_date='2024-04-12')
10.75

# Fetch the IPCA monthly inflation rate from IBGE
>>> yd.fetch_indicator(indicator_code="IPCA", reference_date='2024-03-18')
0.16

# If no data is yet available for the indicator, the function returns None
>>> yd.fetch_indicator(indicator_code="IPCA", reference_date='2024-04-10')
None