📊 Metric	🔢 Value
PyPI Version
Python Versions
License

📊 Metric	🔢 Value
Total Downloads
Monthly
Weekly
GitHub Stars
GitHub Forks

• Equity Data: Historical prices, fundamentals, dividend history
• Futures Data: Active contracts, contract tables, roll analysis
• Options Data: Implied volatility surfaces, option chains
• Fixed Income: Bond information, yield curves, credit spreads
• FX Data: Currency rates and volatility
• Index Data: Constituents and weights
• Fundamentals: Balance sheet data, financial ratios

Install using

pip install bbg_fetch

Upgrade using

pip install --upgrade bbg_fetch

Close using

git clone https://github.com/ArturSepp/BloombergFetch.git

1. Bloomberg Terminal Access: You need access to a Bloomberg Terminal or BPIPE
2. Bloomberg API: Install the Bloomberg API library:

   pip install --index-url=https://blpapi.bloomberg.com/repository/releases/python/simple blpapi

pip install numpy pandas xbbg

import pandas as pd
from bbg_fetch import (
    fetch_field_timeseries_per_tickers,
    fetch_fundamentals,
    fetch_last_prices
)

# Fetch historical prices for multiple tickers
prices = fetch_field_timeseries_per_tickers(
    tickers=['AAPL US Equity', 'MSFT US Equity'],
    field='PX_LAST',
    start_date=pd.Timestamp('2020-01-01')
)

# Get fundamental data
fundamentals = fetch_fundamentals(
    tickers=['AAPL US Equity', 'GOOGL US Equity'],
    fields=['security_name', 'gics_sector_name', 'market_cap']
)

# Fetch current FX rates
fx_rates = fetch_last_prices()
print(fx_rates)

from bbg_fetch import fetch_vol_timeseries, IMPVOL_FIELDS_DELTA

# Fetch SPX implied volatility surface
vol_data = fetch_vol_timeseries(
    ticker='SPX Index',
    vol_fields=IMPVOL_FIELDS_DELTA,
    start_date=pd.Timestamp('2023-01-01')
)

from bbg_fetch import fetch_futures_contract_table, fetch_active_futures

# Get futures contract table
contracts = fetch_futures_contract_table(ticker="ES1 Index")

# Fetch active futures data
front_month, second_month = fetch_active_futures(generic_ticker='ES1 Index')

from bbg_fetch import fetch_bonds_info

# Get bond information by ISIN
bond_data = fetch_bonds_info(
    isins=['US03522AAJ97', 'US126650CZ11'],
    fields=['name', 'px_last', 'yas_bond_yld', 'yas_mod_dur']
)

• fetch_field_timeseries_per_tickers(): Historical data for multiple tickers
• fetch_fields_timeseries_per_ticker(): Multiple fields for single ticker
• fetch_last_prices(): Current market prices

• fetch_fundamentals(): Company fundamentals and metadata
• fetch_balance_data(): Balance sheet and financial ratios
• fetch_dividend_history(): Historical dividend payments
• fetch_div_yields(): Dividend yield calculations

• fetch_vol_timeseries(): Options implied volatility
• fetch_futures_contract_table(): Futures contract specifications
• fetch_active_futures(): Active futures price series

• fetch_bonds_info(): Bond specifications and pricing
• fetch_cds_info(): Credit default swap information

• fetch_index_members_weights(): Index constituents and weights
• fetch_tickers_from_isins(): Convert ISINs to Bloomberg tickers

The package includes predefined field mappings for common data types:

FX_DICT = {
    'EURUSD Curncy': 'EUR',
    'GBPUSD Curncy': 'GBP',
    'JPYUSD Curncy': 'JPY',
    # ... more currencies
}

• IMPVOL_FIELDS_MNY_30DAY: 30-day moneyness-based vol fields
• IMPVOL_FIELDS_MNY_60DAY: 60-day moneyness-based vol fields
• IMPVOL_FIELDS_DELTA: Delta-based vol fields for FX options

DEFAULT_START_DATE = pd.Timestamp('01Jan1959')  # Default start date for historical data
VOLS_START_DATE = pd.Timestamp('03Jan2005')     # Default start for volatility data

Most price functions support Bloomberg's corporate action adjustments:

• CshAdjNormal: Normal cash adjustments (default: True)
• CshAdjAbnormal: Abnormal cash adjustments (default: True)
• CapChg: Capital changes (default: True)

The package includes comprehensive unit tests. Run specific tests:

from bbg_fetch import run_unit_test, LocalTests

# Test different functionalities
run_unit_test(LocalTests.FIELD_TIMESERIES_PER_TICKERS)
run_unit_test(LocalTests.IMPLIED_VOL_TIME_SERIES)
run_unit_test(LocalTests.BOND_INFO)

Available test categories:

• FIELD_TIMESERIES_PER_TICKERS
• FUNDAMENTALS
• ACTIVE_FUTURES
• IMPLIED_VOL_TIME_SERIES
• BOND_INFO
• BALANCE_DATA
• And more...

The package includes robust error handling:

• Automatic retries for failed Bloomberg requests
• Warning messages for missing data
• Graceful handling of empty datasets
• Data validation and cleaning

• pandas: Data manipulation and analysis
• numpy: Numerical computing
• xbbg: Bloomberg data access library
• blpapi: Bloomberg API (requires special installation)

Common Bloomberg fields used in this package:

• PX_LAST: Last price
• PX_OPEN/HIGH/LOW: OHLC prices
• VOLUME: Trading volume
• MARKET_CAP: Market capitalization
• YAS_BOND_YLD: Bond yield
• GICS_SECTOR_NAME: GICS sector classification

1. Bloomberg Terminal Required: This package requires access to Bloomberg Terminal or BPIPE
2. Rate Limits: Bloomberg API has rate limits - the package includes retry logic
3. Data Availability: Not all fields are available for all instruments
4. Time Zones: Default timezone handling is UTC, but can be configured

Contributions are welcome! Please ensure:

• Code follows existing style conventions
• Add unit tests for new functionality
• Update documentation for new features
• Test with multiple Bloomberg data types

[Add your license information here]

For Bloomberg API issues:

• Check Bloomberg Terminal connection
• Verify field names using Bloomberg's FLDS function
• Ensure proper instrument formatting (e.g., "AAPL US Equity")

For package-specific issues:

• Check unit tests for usage examples
• Verify data availability for requested date ranges
• Review Bloomberg's data licensing terms

@software{bloombergfetch2024,
  author={Sepp, Artur},
  title={{BloombergFetch}: A Python Package for Bloomberg Terminal Data Access},
  year={2024},
  publisher={GitHub},
  journal={GitHub repository},
  howpublished={\url{https://github.com/ArturSepp/BloombergFetch}},
  commit={main}
}

@software{sepp2024bbgfetch,
  title={bbg-fetch: Bloomberg fetching analytics wrapping xbbg package},
  author={Sepp, Artur},
  year={2024},
  url={https://github.com/ArturSepp/BloombergFetch},
  note={Python package for Bloomberg Terminal data access, wrapping xbbg library},
  version={1.0.27},
  publisher={PyPI},
  howpublished={\url{https://pypi.org/project/bbg-fetch/}},
  keywords={quantitative, investing, portfolio optimization, systematic strategies, volatility, bloomberg, financial data}
}

@misc{sepp2024bloombergfetch_academic,
  author={Sepp, Artur},
  title={BloombergFetch: Python functionality for getting different data from Bloomberg: prices, implied vols, fundamentals},
  year={2024},
  eprint={GitHub},
  archivePrefix={arXiv},
  primaryClass={q-fin.CP},
  url={https://github.com/ArturSepp/BloombergFetch},
  note={A comprehensive Python package for fetching financial data from Bloomberg Terminal using the xbbg library}
}

@techreport{sepp2024bloombergfetch_tech,
  author={Sepp, Artur},
  title={BloombergFetch: A Python Package for Bloomberg Terminal Data Access},
  institution={GitHub},
  year={2024},
  type={Software Documentation},
  url={https://github.com/ArturSepp/BloombergFetch},
  note={Comprehensive wrapper functions for accessing Bloomberg data including equities, futures, bonds, options, FX rates}
}

@inproceedings{sepp2024bloombergfetch_proc,
  author={Sepp, Artur},
  title={BloombergFetch: Simplifying Bloomberg Terminal Data Access in Python},
  booktitle={Proceedings of Financial Software Engineering},
  year={2024},
  publisher={GitHub},
  url={https://github.com/ArturSepp/BloombergFetch},
  note={Open-source Python package for Bloomberg data fetching and analytics}
}

@manual{bloomberg_api_2024,
  title={Bloomberg API Documentation},
  organization={Bloomberg L.P.},
  year={2024},
  note={Bloomberg Terminal API for financial data access},
  url={https://www.bloomberg.com/professional/support/api-library/}
}

@software{xbbg2024,
  title={xbbg: Bloomberg API for Python},
  author={{Bloomberg contributors}},
  year={2024},
  url={https://github.com/alpha-xone/xbbg},
  note={Python library for Bloomberg data access}
}

Software reference:

"Financial data was obtained using the BloombergFetch package (Sepp, 2024), which provides a Python wrapper for Bloomberg Terminal data access."

Methodology section:

"Data collection was implemented using BloombergFetch v1.0.27 (Sepp, 2024), enabling efficient retrieval of equity prices, options data, and fundamental information from Bloomberg Terminal."

Data section:

"Historical market data, implied volatility surfaces, and fundamental data were sourced via Bloomberg Terminal using the BloombergFetch Python package (Sepp, 2024)."

APA Style:

Sepp, A. (2024). BloombergFetch: A Python Package for Bloomberg Terminal Data Access [Computer software]. GitHub. https://github.com/ArturSepp/BloombergFetch

IEEE Style:

A. Sepp, "BloombergFetch: A Python Package for Bloomberg Terminal Data Access," 2024. [Online]. Available: https://github.com/ArturSepp/BloombergFetch

Chicago Style:

Sepp, Artur. "BloombergFetch: A Python Package for Bloomberg Terminal Data Access." Computer software, 2024. https://github.com/ArturSepp/BloombergFetch.

\documentclass{article}
\usepackage[utf8]{inputenc}
\usepackage{url}
\usepackage{natbib}

\begin{document}

The financial data analysis was conducted using the BloombergFetch package \citep{bloombergfetch2024}, 
which provides comprehensive access to Bloomberg Terminal data through a simplified Python interface.

\bibliographystyle{plainnat}
\bibliography{references}

\end{document}

\begin{thebibliography}{1}
\bibitem{bloombergfetch2024}
Artur Sepp.
\newblock {BloombergFetch}: A Python Package for Bloomberg Terminal Data Access.
\newblock GitHub repository, 2024.
\newblock \url{https://github.com/ArturSepp/BloombergFetch}.
\end{thebibliography}

• Author: Artur Sepp
• Title: BloombergFetch: A Python Package for Bloomberg Terminal Data Access
• Year: 2024
• Repository: https://github.com/ArturSepp/BloombergFetch
• PyPI Package: https://pypi.org/project/bbg-fetch/
• License: MIT License
• Version: 1.0.27 (latest)
• Keywords: quantitative, investing, portfolio optimization, systematic strategies, volatility, bloomberg, financial data
• Dependencies: xbbg, blpapi, pandas, numpy
• Python Support: >=3.8, <3.11

If you use BloombergFetch in your research, please cite it as:

@software{sepp2024bloombergfetch_doi,
  author={Sepp, Artur},
  title={BloombergFetch: A Python Package for Bloomberg Terminal Data Access},
  year={2024},
  url={https://github.com/ArturSepp/BloombergFetch},
  version={1.0.27}
}