| Name | PYield JSON |
| Version |
0.7.14
JSON |
| download |
| home_page | None |
| Summary | A Python library for analysis of fixed income instruments in Brazil |
| upload_time | 2024-05-16 10:51:46 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.11 |
| license | MIT License
Copyright (c) 2023 Carlos Carvalho
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
| keywords |
fixed-income
brazil
finance
analysis
bonds
|
| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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[](https://pypi.python.org/pypi/pyield)
[](https://python.org "Go to Python homepage")
[](#license)
# PYield: Brazilian Fixed Income Toolkit
## 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:
```sh
pip install pyield
```
## How to use PYield
### Business Days Tools (Brazilian holidays are automatically considered)
```python
>>> 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
```python
# Fetch current DI Futures data from B3 (15 minutes delay)
>>> yd.fetch_asset(asset_code="DI1")
TradeTime TickerSymbol ExpirationDate BDaysToExp ... MaxRate LastAskRate LastBidRate CurrentRate
2024-04-21 13:37:39 DI1K24 2024-05-02 7 ... 0.10660 0.10652 0.10660 0.10660
2024-04-21 13:37:39 DI1M24 2024-06-03 28 ... 0.10518 0.10510 0.10516 0.10518
2024-04-21 13:37:39 DI1N24 2024-07-01 48 ... 0.10480 0.10456 0.10462 0.10460
... ... ... ... ... ... ... ... ...
2024-04-21 13:37:39 DI1F37 2037-01-02 3183 ... <NA> <NA> 0.11600 <NA>
2024-04-21 13:37:39 DI1F38 2038-01-04 3432 ... <NA> <NA> 0.11600 <NA>
2024-04-21 13:37:39 DI1F39 2039-01-03 3683 ... <NA> <NA> <NA> <NA>
# Fetch historical 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
```python
# 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
```python
# 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 -20.28
LTN 2024-04-11 2024-10-01 -10.19
LTN 2024-04-11 2025-01-01 -15.05
... ... ... ...
NTN-F 2024-04-11 2031-01-01 -0.66
NTN-F 2024-04-11 2033-01-01 -5.69
NTN-F 2024-04-11 2035-01-01 -1.27
```
### Indicators Data
```python
# Fetch the SELIC target rate from the Central Bank of Brazil
>>> yd.fetch_indicator(indicator_code="SELIC", reference_date='2024-04-12')
0.1075 # 10.75%
# Fetch the IPCA monthly inflation rate from IBGE
>>> yd.fetch_indicator(indicator_code="IPCA", reference_date='2024-03-18')
0.16 # 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
```
### Projections Data
```python
# Fetch current month projection for IPCA from IBGE API
>>> ipca = yd.fetch_projection(projection_code="IPCA_CM")
>>> print(ipca)
IndicatorProjection(
last_updated=Timestamp('2024-04-19 18:55:00'),
reference_month_ts=Timestamp('2024-04-01 00:00:00'),
reference_month_br='ABR/2024',
projected_value=0.0035 # 0.35%
)
>>> ipca.projected_value
0.0035 # 0.35%
```
Raw data
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"description": "[](https://pypi.python.org/pypi/pyield)\n[](https://python.org \"Go to Python homepage\")\n[](#license)\n\n# PYield: Brazilian Fixed Income Toolkit\n\n## Introduction\n\nWelcome 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.\n\n## Features\n\n- **Data Collection**: Automated fetching of data from ANBIMA and B3.\n- **Data Processing**: Efficient processing and normalization of fixed income data.\n- **Analysis Tools**: Built-in functions for common analysis tasks in fixed income markets.\n- **Easy Integration**: Seamless integration with pandas data analysis workflows.\n- **Type Hints**: Full support for static type checking, enhancing development experience and code quality.\n\n## Installation\n\nYou can install PYield using pip:\n```sh\npip install pyield\n```\n\n## How to use PYield\n\n### Business Days Tools (Brazilian holidays are automatically considered)\n```python\n>>> import pyield as yd\n\n# Count the number of business days between two dates\n# Start date is included, end date is excluded\n>>> yd.count_bdays(start='2023-12-29', end='2024-01-02')\n1\n\n# Get the next business day after a given date (offset=1)\n>>> yd.offset_bdays(dates=\"2023-12-29\", offset=1)\nTimestamp('2024-01-02 00:00:00')\n\n# Get the next business day if it is not a business day (offset=0)\n>>> yd.offset_bdays(dates=\"2023-12-30\", offset=0)\nTimestamp('2024-01-02 00:00:00')\n\n# Since 2023-12-29 is a business day, it returns the same date (offset=0)\n>>> yd.offset_bdays(dates=\"2023-12-29\", offset=0)\nTimestamp('2023-12-29 00:00:00')\n\n# Generate a pandas series with the business days between two dates\n>>> yd.generate_bdays(start='2023-12-29', end='2024-01-03')\n0 2023-12-29\n1 2024-01-02\n2 2024-01-03\ndtype: datetime64[ns]\n```\n\n### Futures Data\n```python\n# Fetch current DI Futures data from B3 (15 minutes delay)\n>>> yd.fetch_asset(asset_code=\"DI1\")\nTradeTime TickerSymbol ExpirationDate BDaysToExp ... MaxRate LastAskRate LastBidRate CurrentRate\n2024-04-21 13:37:39 DI1K24 2024-05-02 7 ... 0.10660 0.10652 0.10660 0.10660\n2024-04-21 13:37:39 DI1M24 2024-06-03 28 ... 0.10518 0.10510 0.10516 0.10518\n2024-04-21 13:37:39 DI1N24 2024-07-01 48 ... 0.10480 0.10456 0.10462 0.10460\n ... ... ... ... ... ... ... ... ...\n2024-04-21 13:37:39 DI1F37 2037-01-02 3183 ... <NA> <NA> 0.11600 <NA>\n2024-04-21 13:37:39 DI1F38 2038-01-04 3432 ... <NA> <NA> 0.11600 <NA>\n2024-04-21 13:37:39 DI1F39 2039-01-03 3683 ... <NA> <NA> <NA> <NA>\n\n# Fetch historical DI Futures data from B3\n>>> yd.fetch_asset(asset_code=\"DI1\", reference_date='2024-03-08')\nTradeDate TickerSymbol ExpirationDate BDaysToExp ... LastRate LastAskRate LastBidRate SettlementRate\n2024-03-08 DI1J24 2024-04-01 15 ... 10.952 10.952 10.956 10.956\n2024-03-08 DI1K24 2024-05-02 37 ... 10.776 10.774 10.780 10.777\n2024-03-08 DI1M24 2024-06-03 58 ... 10.604 10.602 10.604 10.608\n ... ... ... ... ... ... ... ... ...\n2024-03-08 DI1F37 2037-01-02 3213 ... <NA> <NA> <NA> 10.859\n2024-03-08 DI1F38 2038-01-04 3462 ... <NA> <NA> <NA> 10.859\n2024-03-08 DI1F39 2039-01-03 3713 ... <NA> <NA> <NA> 10.85\n```\n\n### Treasury Bonds Data\n```python\n# Fetch a DataFrame with the NTN-B data from ANBIMA\n# Anbima data is available for the last 5 working days\n# Obs: Anbima members have access to the full history\n>>> yd.fetch_asset(asset_code=\"NTN-B\", reference_date='2024-04-12')\n\nBondType ReferenceDate MaturityDate BidRate AskRate IndicativeRate Price\n NTN-B 2024-04-12 2024-08-15 0.07540 0.07504 0.07523 4,271.43565\n NTN-B 2024-04-12 2025-05-15 0.05945 0.05913 0.05930 4,361.34391\n NTN-B 2024-04-12 2026-08-15 0.05927 0.05897 0.05910 4,301.40082\n ... ... ... ... ... ... ...\n NTN-B 2024-04-12 2050-08-15 0.06039 0.06006 0.06023 4,299.28233\n NTN-B 2024-04-12 2055-05-15 0.06035 0.05998 0.06017 4,367.13360\n NTN-B 2024-04-12 2060-08-15 0.06057 0.06016 0.06036 4,292.26323\n```\n\n### Spreads Calculation\n```python\n# Calculate the spread between two DI Futures contracts and the pre-fix bonds\n>>> yd.calculate_spreads(spread_type=\"di_vs_pre\", reference_date=\"2024-4-11\")\n\nBondType ReferenceDate MaturityDate DISpread\n LTN 2024-04-11 2024-07-01 -20.28\n LTN 2024-04-11 2024-10-01 -10.19\n LTN 2024-04-11 2025-01-01 -15.05\n ... ... ... ...\n NTN-F 2024-04-11 2031-01-01 -0.66\n NTN-F 2024-04-11 2033-01-01 -5.69\n NTN-F 2024-04-11 2035-01-01 -1.27\n```\n\n### Indicators Data\n```python\n# Fetch the SELIC target rate from the Central Bank of Brazil\n>>> yd.fetch_indicator(indicator_code=\"SELIC\", reference_date='2024-04-12')\n0.1075 # 10.75%\n\n# Fetch the IPCA monthly inflation rate from IBGE\n>>> yd.fetch_indicator(indicator_code=\"IPCA\", reference_date='2024-03-18')\n0.16 # 0.16%\n\n# If no data is yet available for the indicator, the function returns None\n>>> yd.fetch_indicator(indicator_code=\"IPCA\", reference_date='2024-04-10')\nNone\n```\n\n### Projections Data\n```python\n# Fetch current month projection for IPCA from IBGE API\n>>> ipca = yd.fetch_projection(projection_code=\"IPCA_CM\")\n>>> print(ipca)\nIndicatorProjection(\n last_updated=Timestamp('2024-04-19 18:55:00'),\n reference_month_ts=Timestamp('2024-04-01 00:00:00'),\n reference_month_br='ABR/2024',\n projected_value=0.0035 # 0.35%\n)\n>>> ipca.projected_value\n0.0035 # 0.35%\n```\n\n",
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