polars-bloomberg is a Python library that extracts Bloomberg's financial data directly into Polars DataFrames. If you’re a quant financial analyst, data scientist, or quant developer working in capital markets, this library makes it easy to fetch, transform, and analyze Bloomberg data right in Polars—offering speed, efficient memory usage, and a lot of fun to use!

Why use polars-bloomberg?

• User-Friendly Functions: Shortcuts like bdp(), bdh(), and bql() (inspired by Excel-like Bloomberg calls) let you pull data with minimal boilerplate.
• High-Performance Analytics: Polars is a lightning-fast DataFrame library. Combined with Bloomberg’s rich dataset, you get efficient data retrieval and minimal memory footprint
• No Pandas Dependency: Enjoy a clean integration that relies solely on Polars for speed and simplicity.

Table of Contents

• Introduction
• Prerequisites
• Installation
• Quick Start
• Core Methods

• Additional Documentation and Resources

Introduction

With polars-bloomberg, you can enjoy the speed and simplicity of Polars DataFrames—accessing both familiar Excel-style calls (bdp, bdh ,bdip, bsrch) and advanced bql queries—without extra pandas conversions.

For detailed documentation and function references, visit the documentation site https://marekozana.github.io/polars-bloomberg.

I hope you enjoy using it as much as I had fun building it!

Prerequisites

• Bloomberg Access: A valid Bloomberg terminal license.
• Bloomberg Python API: The blpapi library must be installed. See the Bloomberg API Library for guidance.
• Python Version: Python 3.12+ recommended.

Installation

pip install polars-bloomberg

Quick Start

from polars_bloomberg import BQuery
Fetch the latest price for Apple (AAPL US Equity)
with BQuery() as bq:
    df = bq.bdp(["AAPL US Equity"], ["PX_LAST"])
    print(df)
┌────────────────┬─────────┐ │ security       ┆ PX_LAST │ │ ---            ┆ ---     │ │ str            ┆ f64     │ ╞════════════════╪═════════╡ │ AAPL US Equity ┆ 248.13  │ └────────────────┴─────────┘

• Establishes a Bloomberg connection using the context manager.
• Retrieves the last price of Apple shares.
• Returns the result as a Polars DataFrame.

Core Methods

• bq.bdp() for Bloomberg Data Points (single-value fields).
• bq.bdh() for Historical Data (time series).
• bq.bql() for complex Bloomberg Query Language requests.
• bq.bsrch() for saved custom SRCH searches and BI templates.
• bq.bdip() for intraday bars

BDP

Example: Fetching the Last Price & Currency of Apple and SEB

with BQuery() as bq:
    df = bq.bdp(["AAPL US Equity", "SEBA SS Equity"], ["PX_LAST", "CRNCY"])
    print(df)
┌────────────────┬─────────┬───────┐ │ security       ┆ PX_LAST ┆ CRNCY │ │ ---            ┆ ---     ┆ ---   │ │ str            ┆ f64     ┆ str   │ ╞════════════════╪═════════╪═══════╡ │ AAPL US Equity ┆ 248.13  ┆ USD   │ │ SEBA SS Equity ┆ 155.2   ┆ SEK   │ └────────────────┴─────────┴───────┘

with BQuery() as bq:
    df = bq.bdp(["XS2930103580 Corp", "USX60003AC87 Corp"],
                ["SECURITYDES", "YASZSPREAD", "CRNCY", "NXTCALLDT"])
┌───────────────────┬────────────────┬─────────────┬───────┬─────────────┐ │ security          ┆ SECURITYDES   ┆ YASZSPREAD ┆ CRNCY ┆ NXTCALLDT │ │ ---               ┆ ---            ┆ ---         ┆ ---   ┆ ---         │ │ str               ┆ str            ┆ f64         ┆ str   ┆ date        │ ╞═══════════════════╪════════════════╪═════════════╪═══════╪═════════════╡ │ XS2930103580 Corp ┆ SEB 6 3/4 PERP ┆ 304.676112  ┆ USD   ┆ 2031-11-04  │ │ USX60003AC87 Corp ┆ NDAFH 6.3 PERP ┆ 292.477506  ┆ USD   ┆ 2031-09-25  │ └───────────────────┴────────────────┴─────────────┴───────┴─────────────┘

with BQuery() as bq:
    df = bq.bdp(
        ["IBM US Equity"],
        ["PXLAST", "CRNCYADJPXLAST"],
        overrides=[("EQYFUNDCRNCY", "SEK")],
    )
┌───────────────┬─────────┬───────────────────┐ │ security      ┆ PXLAST ┆ CRNCYADJPXLAST │ │ ---           ┆ ---     ┆ ---               │ │ str           ┆ f64     ┆ f64               │ ╞═══════════════╪═════════╪═══════════════════╡ │ IBM US Equity ┆ 230.82  ┆ 2535.174          │ └───────────────┴─────────┴───────────────────┘

with BQuery() as bq:
    df = bq.bdp(["USX60003AC87 Corp"], ["SETTLE_DT"],
                overrides=[("USERLOCALTRADE_DATE", "20241014")])
┌───────────────────┬────────────┐ │ security          ┆ SETTLE_DT  │ │ ---               ┆ ---        │ │ str               ┆ date       │ ╞═══════════════════╪════════════╡ │ USX60003AC87 Corp ┆ 2024-10-15 │ └───────────────────┴────────────┘

with BQuery() as bq:
    df = bq.bdp(['USDSEK Curncy', 'SEKCZK Curncy'],
                ['SETTLEDT', 'PXLAST'],
                overrides=[('REFERENCE_DATE', '20200715')]
               )
┌───────────────┬────────────┬─────────┐ │ security      ┆ SETTLEDT  ┆ PXLAST │ │ ---           ┆ ---        ┆ ---     │ │ str           ┆ date       ┆ f64     │ ╞═══════════════╪════════════╪═════════╡ │ USDSEK Curncy ┆ 2020-07-17 ┆ 10.9778 │ │ SEKCZK Curncy ┆ 2020-07-17 ┆ 2.1698  │ └───────────────┴────────────┴─────────┘

BDH

with BQuery() as bq:
    df = bq.bdh(
        ["TLT US Equity"],
        ["PX_LAST"],
        start_date=date(2019, 1, 1),
        end_date=date(2019, 1, 7),
    )
    print(df)
┌───────────────┬────────────┬─────────┐ │ security      ┆ date       ┆ PX_LAST │ │ ---           ┆ ---        ┆ ---     │ │ str           ┆ date       ┆ f64     │ ╞═══════════════╪════════════╪═════════╡ │ TLT US Equity ┆ 2019-01-02 ┆ 122.15  │ │ TLT US Equity ┆ 2019-01-03 ┆ 123.54  │ │ TLT US Equity ┆ 2019-01-04 ┆ 122.11  │ │ TLT US Equity ┆ 2019-01-07 ┆ 121.75  │ └───────────────┴────────────┴─────────┘

with BQuery() as bq:
    df = bq.bdh(
        securities=["SPY US Equity", "TLT US Equity"],
        fields=["PX_LAST", "VOLUME"],
        start_date=date(2019, 1, 1),
        end_date=date(2019, 1, 10),
        options={"adjustmentSplit": True},
    )
    print(df)
shape: (14, 4) ┌───────────────┬────────────┬─────────┬──────────────┐ │ security      ┆ date       ┆ PX_LAST ┆ VOLUME       │ │ ---           ┆ ---        ┆ ---     ┆ ---          │ │ str           ┆ date       ┆ f64     ┆ f64          │ ╞═══════════════╪════════════╪═════════╪══════════════╡ │ SPY US Equity ┆ 2019-01-02 ┆ 250.18  ┆ 1.26925199e8 │ │ SPY US Equity ┆ 2019-01-03 ┆ 244.21  ┆ 1.44140692e8 │ │ SPY US Equity ┆ 2019-01-04 ┆ 252.39  ┆ 1.42628834e8 │ │ SPY US Equity ┆ 2019-01-07 ┆ 254.38  ┆ 1.031391e8   │ │ SPY US Equity ┆ 2019-01-08 ┆ 256.77  ┆ 1.02512587e8 │ │ …             ┆ …          ┆ …       ┆ …            │ │ TLT US Equity ┆ 2019-01-04 ┆ 122.11  ┆ 1.2970226e7  │ │ TLT US Equity ┆ 2019-01-07 ┆ 121.75  ┆ 8.498104e6   │ │ TLT US Equity ┆ 2019-01-08 ┆ 121.43  ┆ 7.737103e6   │ │ TLT US Equity ┆ 2019-01-09 ┆ 121.24  ┆ 9.349245e6   │ │ TLT US Equity ┆ 2019-01-10 ┆ 120.46  ┆ 8.22286e6    │ └───────────────┴────────────┴─────────┴──────────────┘

with BQuery() as bq:
    df = bq.bdh(['AAPL US Equity'],
                ['PX_LAST'],
                start_date=date(2019, 1, 1),
                end_date=date(2019, 3, 29),
                options={"periodicitySelection": "MONTHLY"})
┌────────────────┬────────────┬─────────┐ │ security       ┆ date       ┆ PX_LAST │ │ ---            ┆ ---        ┆ ---     │ │ str            ┆ date       ┆ f64     │ ╞════════════════╪════════════╪═════════╡ │ AAPL US Equity ┆ 2019-01-31 ┆ 41.61   │ │ AAPL US Equity ┆ 2019-02-28 ┆ 43.288  │ │ AAPL US Equity ┆ 2019-03-29 ┆ 47.488  │ └────────────────┴────────────┴─────────┘

BDIB

with BQuery() as bq:  # set debug=False for normal usage
    df = bq.bdib(
        "OMX Index",
        event_type="TRADE",
        interval=60,
        start_datetime=datetime(2025, 11, 5),
        end_datetime=datetime(2025, 11, 5, 12),
    )
    print(df)

Output:

shape: (4, 9) ┌───────────┬──────────────┬──────────┬──────────┬───┬──────────┬────────┬───────────┬───────┐ │ security  ┆ time         ┆ open     ┆ high     ┆ … ┆ close    ┆ volume ┆ numEvents ┆ value │ │ ---       ┆ ---          ┆ ---      ┆ ---      ┆   ┆ ---      ┆ ---    ┆ ---       ┆ ---   │ │ str       ┆ datetime[μs] ┆ f64      ┆ f64      ┆   ┆ f64      ┆ i64    ┆ i64       ┆ f64   │ ╞═══════════╪══════════════╪══════════╪══════════╪═══╪══════════╪════════╪═══════════╪═══════╡ │ OMX Index ┆ 2025-11-05   ┆ 2726.603 ┆ 2742.014 ┆ … ┆ 2739.321 ┆ 0      ┆ 3591      ┆ 0.0   │ │           ┆ 08:00:00     ┆          ┆          ┆   ┆          ┆        ┆           ┆       │ │ OMX Index ┆ 2025-11-05   ┆ 2739.466 ┆ 2739.706 ┆ … ┆ 2733.836 ┆ 0      ┆ 3600      ┆ 0.0   │ │           ┆ 09:00:00     ┆          ┆          ┆   ┆          ┆        ┆           ┆       │ │ OMX Index ┆ 2025-11-05   ┆ 2733.747 ┆ 2734.827 ┆ … ┆ 2731.724 ┆ 0      ┆ 3600      ┆ 0.0   │ │           ┆ 10:00:00     ┆          ┆          ┆   ┆          ┆        ┆           ┆       │ │ OMX Index ┆ 2025-11-05   ┆ 2731.721 ┆ 2742.015 ┆ … ┆ 2741.185 ┆ 0      ┆ 3600      ┆ 0.0   │ │           ┆ 11:00:00     ┆          ┆          ┆   ┆          ┆        ┆           ┆       │ └───────────┴──────────────┴──────────┴──────────┴───┴──────────┴────────┴───────────┴───────┘

Each row is a 60-minute bar built from TRADE events, and the time column is returned in UTC (matching Bloomberg's wide format).

BSRCH

Small example: two COCO bonds (limit = 2)

with BQuery() as bq:
    df = bq.bsrch("FI:SRCHEX.@COCO", overrides={"LIMIT": 2})
    print(df)

shape: (2, 1)
┌──────────────┐
│ id           │
│ ---          │
│ str          │
╞══════════════╡
│ DA785784 Corp│
│ DA773901 Corp│
└──────────────┘

Larger example: BI template (BI:TPD) with BIKEY and LIMIT

with BQuery() as bq:
    df = bq.bsrch(
        "BI:TPD",
        overrides={
            "BIKEY": "DKOCVGXJVU8II8M90W8JSQEKR",
            "LIMIT": 20000,  # avoid ReachMax warning
        },
    )
    print(df.head())

shape: (16, 6)
┌──────────┬──────────┬─────────┬──────────┬──────────┬─────────┐
│ MainCat ┆ Bclass3 ┆ Category┆ 06/30/20 ┆ 03/31/20 ┆ 12/31/20│
│ ...      ┆ ...      ┆ ...     ┆ 25       ┆ 25       ┆ 24      │
│ str      ┆ str      ┆ str     ┆ f64      ┆ f64      ┆ f64     │
╞══════════╪══════════╪═════════╪══════════╪══════════╪═════════╡
│ Leverage ┆ Non-Fin… ┆ B       ┆ 3.956051 ┆ 4.118212 ┆ 4.269732│
│ …        ┆ …        ┆ …       ┆ …        ┆ …        ┆ …       │
└──────────┴──────────┴─────────┴──────────┴──────────┴─────────┘

BQL

Returns: The bql() method returns a BqlResult object, which:

• Acts like a list of Polars DataFrames (one for each item in BQL get statement).
• Provides a .combine() method to merge DataFrames. With no arguments it preserves

1. Basic Example: Single Item and Single Security

# Fetch the last price of IBM stock
with BQuery() as bq:
    results = bq.bql("get(px_last) for(['IBM US Equity'])")
    print(results[0])  # Access the first DataFrame

┌───────────────┬─────────┬────────────┬──────────┐
│ ID            ┆ px_last ┆ DATE       ┆ CURRENCY │
│ ---           ┆ ---     ┆ ---        ┆ ---      │
│ str           ┆ f64     ┆ date       ┆ str      │
╞═══════════════╪═════════╪════════════╪══════════╡
│ IBM US Equity ┆ 230.82  ┆ 2024-12-14 ┆ USD      │
└───────────────┴─────────┴────────────┴──────────┘

2. Multiple Securities with a Single Item

# Fetch the last price for IBM and SEB
with BQuery() as bq:
    results = bq.bql("get(px_last) for(['IBM US Equity', 'SEBA SS Equity'])")
    print(results[0])

┌────────────────┬─────────┬────────────┬──────────┐
│ ID             ┆ px_last ┆ DATE       ┆ CURRENCY │
│ ---            ┆ ---     ┆ ---        ┆ ---      │
│ str            ┆ f64     ┆ date       ┆ str      │
╞════════════════╪═════════╪════════════╪══════════╡
│ IBM US Equity  ┆ 230.82  ┆ 2024-12-14 ┆ USD      │
│ SEBA SS Equity ┆ 155.2   ┆ 2024-12-14 ┆ SEK      │
└────────────────┴─────────┴────────────┴──────────┘

3. Multiple Items

# Fetch name and last price of IBM (two items)
with BQuery() as bq:
    results = bq.bql("get(name, px_last) for(['IBM US Equity'])")

>>> print(len(results))  # 2 DataFrames
n=2
>>> print(results[0])    # First DataFrame: 'name' ┌───────────────┬────────────────────────────────┐ │ ID            ┆ name                           │ │ ---           ┆ ---                            │ │ str           ┆ str                            │ ╞═══════════════╪════════════════════════════════╡ │ IBM US Equity ┆ International Business Machine │ └───────────────┴────────────────────────────────┘
>>> print(results[1])    # Second DataFrame: 'px_last' ┌───────────────┬─────────┬────────────┬──────────┐ │ ID            ┆ px_last ┆ DATE       ┆ CURRENCY │ │ ---           ┆ ---     ┆ ---        ┆ ---      │ │ str           ┆ f64     ┆ date       ┆ str      │ ╞═══════════════╪═════════╪════════════╪══════════╡ │ IBM US Equity ┆ 230.82  ┆ 2024-12-14 ┆ USD      │ └───────────────┴─────────┴────────────┴──────────┘

Combining Results

>>> combined_df = results.combine()
>>> print(combined_df)

┌───────────────┬────────────────────────────────┬─────────┬────────────┬──────────┐
│ ID            ┆ name                           ┆ px_last ┆ DATE       ┆ CURRENCY │
│ ---           ┆ ---                            ┆ ---     ┆ ---        ┆ ---      │
│ str           ┆ str                            ┆ f64     ┆ date       ┆ str      │
╞═══════════════╪════════════════════════════════╪═════════╪════════════╪══════════╡
│ IBM US Equity ┆ International Business Machine ┆ 230.82  ┆ 2024-12-14 ┆ USD      │
└───────────────┴────────────────────────────────┴─────────┴────────────┴──────────┘

By default, combine() keeps the historical behavior and joins each result DataFrame on all common column names. This is convenient for simple BQL results, but it can be too broad when several item tables share metadata columns such as DATE, CURRENCY, PERIOD, VALUE, or MULTIPLIER.

For safer joins, pass the intended key columns explicitly:

# Join only by security ID. Other common columns are kept as data columns.
combined_df = results.combine(on="ID")
Join by a compound key when both columns are true row identifiers.
combined_df = results.combine(on=["ID", "DATE"])
Use Polars join names. "full" is the default for explicit joins.
combined_df = results.combine(on="ID", how="inner")

If non-key columns overlap when using on=..., they are preserved with suffixes based on the BQL item name, for example DATE#ret1d. To fail instead of suffixing overlapping non-key columns, use:

combineddf = results.combine(on="ID", allowcommon_columns=False)

4. Advanced Example: Screening Securities

query="""
    let(#dur=duration(duration_type=MODIFIED);
        #zsprd=spread(spread_type=Z);)
    get(name(), #dur, #zsprd)
    for(filter(screenresults(type=SRCH, screen_name='@COCO'),
            ticker in ['SEB', 'SHBASS']))
"""
with BQuery() as bq:     results = bq.bql(query)     combined_df = results.combine()     print(combined_df)

┌───────────────┬─────────────────┬──────┬────────────┬────────┐ │ ID            ┆ name()          ┆ #dur ┆ DATE       ┆ #zsprd │ │ ---           ┆ ---             ┆ ---  ┆ ---        ┆ ---    │ │ str           ┆ str             ┆ f64  ┆ date       ┆ f64    │ ╞═══════════════╪═════════════════╪══════╪════════════╪════════╡ │ BW924993 Corp ┆ SEB 6 ⅞ PERP    ┆ 2.23 ┆ 2024-12-16 ┆ 212.0  │ │ YV402592 Corp ┆ SEB Float PERP  ┆ 0.21 ┆ 2024-12-16 ┆ 233.0  │ │ ZQ349286 Corp ┆ SEB 5 ⅛ PERP    ┆ 0.39 ┆ 2024-12-16 ┆ 186.0  │ │ ZO703315 Corp ┆ SHBASS 4 ⅜ PERP ┆ 1.95 ┆ 2024-12-16 ┆ 213.0  │ │ ZO703956 Corp ┆ SHBASS 4 ¾ PERP ┆ 4.94 ┆ 2024-12-16 ┆ 256.0  │ │ YU819930 Corp ┆ SEB 6 ¾ PERP    ┆ 5.37 ┆ 2024-12-16 ┆ 309.0  │ └───────────────┴─────────────────┴──────┴────────────┴────────┘

Average PE per Sector

query = """
    let(#avgpe=avg(group(peratio(), gicssectorname()));)
    get(#avg_pe)
    for(members('OMX Index'))
"""
with BQuery() as bq:
    results = bq.bql(query)
    print(results[0].head(5))

┌──────────────┬───────────┬──────────────┬────────────┬──────────────┬──────────────┬─────────────┐
│ ID           ┆ #avgpe   ┆ REVISIONDAT ┆ ASOFDATE ┆ PERIODENDD ┆ ORIGIDS     ┆ GICSSECTOR │
│ ---          ┆ ---       ┆ E            ┆ ---        ┆ ATE          ┆ ---          ┆ _NAME()     │
│ str          ┆ f64       ┆ ---          ┆ date       ┆ ---          ┆ str          ┆ ---         │
│              ┆           ┆ date         ┆            ┆ date         ┆              ┆ str         │
╞══════════════╪═══════════╪══════════════╪════════════╪══════════════╪══════════════╪═════════════╡
│ Communicatio ┆ 19.561754 ┆ 2024-10-24   ┆ 2024-12-14 ┆ 2024-09-30   ┆ null         ┆ Communicati │
│ n Services   ┆           ┆              ┆            ┆              ┆              ┆ on Services │
│ Consumer Dis ┆ 19.117295 ┆ 2024-10-24   ┆ 2024-12-14 ┆ 2024-09-30   ┆ null         ┆ Consumer    │
│ cretionary   ┆           ┆              ┆            ┆              ┆              ┆ Discretiona │
│              ┆           ┆              ┆            ┆              ┆              ┆ ry          │
│ Consumer     ┆ 15.984743 ┆ 2024-10-24   ┆ 2024-12-14 ┆ 2024-09-30   ┆ ESSITYB SS   ┆ Consumer    │
│ Staples      ┆           ┆              ┆            ┆              ┆ Equity       ┆ Staples     │
│ Financials   ┆ 6.815895  ┆ 2024-10-24   ┆ 2024-12-14 ┆ 2024-09-30   ┆ null         ┆ Financials  │
│ Health Care  ┆ 22.00628  ┆ 2024-11-12   ┆ 2024-12-14 ┆ 2024-09-30   ┆ null         ┆ Health Care │
└──────────────┴───────────┴──────────────┴────────────┴──────────────┴──────────────┴─────────────┘

Axes

# Get current axes for Swedish AT1 bonds in USD
query="""
    let(#ax=axes();)
    get(security_des, #ax)
    for(filter(bondsuniv(ACTIVE),
        crncy()=='USD' and
        baseliiidesignation() == 'Additional Tier 1' and
        country_iso() == 'SE'))
"""
with BQuery() as bq:     results = bq.bql(query)     print(results.combine())
┌───────────────┬─────────────────┬─────┬───────────┬───────────┬────────────────┬────────────────┐ │ ID            ┆ securitydes    ┆ #ax ┆ ASKDEPTH ┆ BIDDEPTH ┆ ASKTOTALSIZE ┆ BIDTOTAL_SIZE │ │ ---           ┆ ---             ┆ --- ┆ ---       ┆ ---       ┆ ---            ┆ ---            │ │ str           ┆ str             ┆ str ┆ i64       ┆ i64       ┆ f64            ┆ f64            │ ╞═══════════════╪═════════════════╪═════╪═══════════╪═══════════╪════════════════╪════════════════╡ │ YU819930 Corp ┆ SEB 6 ¾ PERP    ┆ Y   ┆ 2         ┆ null      ┆ 5.6e6          ┆ null           │ │ ZO703315 Corp ┆ SHBASS 4 ⅜ PERP ┆ Y   ┆ 1         ┆ 2         ┆ 5e6            ┆ 6e6            │ │ BR069680 Corp ┆ SWEDA 4 PERP    ┆ Y   ┆ null      ┆ 1         ┆ null           ┆ 3e6            │ │ ZL122341 Corp ┆ SWEDA 7 ⅝ PERP  ┆ Y   ┆ null      ┆ 6         ┆ null           ┆ 2.04e7         │ │ ZQ349286 Corp ┆ SEB 5 ⅛ PERP    ┆ Y   ┆ 2         ┆ 4         ┆ 5.5e6          ┆ 3e7            │ │ ZF859199 Corp ┆ SWEDA 7 ¾ PERP  ┆ Y   ┆ 1         ┆ 1         ┆ 2e6            ┆ 2e6            │ │ ZO703956 Corp ┆ SHBASS 4 ¾ PERP ┆ Y   ┆ 1         ┆ 3         ┆ 1.2e6          ┆ 1.1e7          │ │ BW924993 Corp ┆ SEB 6 ⅞ PERP    ┆ Y   ┆ 1         ┆ 3         ┆ 5e6            ┆ 1.1e7          │ └───────────────┴─────────────────┴─────┴───────────┴───────────┴────────────────┴────────────────┘

Axes with all columns

# RT1 Axes with all columns
query = """
let(#ax=axes();)
get(name, #ax, amt_outstanding)
for(filter(bondsuniv(ACTIVE),
    crncy() in ['USD', 'EUR'] and
    solvencyiidesignation() == 'Restricted Tier 1' and
    amt_outstanding() > 7.5e8 and
    is_axed('Bid') == True))
preferences(addcols=all)
"""
with BQuery() as bq:     results = bq.bql(query)     print(results.combine())

Segments

# revenue per segment
query = """
    let(#segment=segment_name();
        #revenue=salesRevturn(fpt=q, fpr=range(2023Q3, 2024Q3));
        )
    get(#segment, #revenue)
    for(segments('GTN US Equity',type=reported,hierarchy=PRODUCT, level=1))
"""
with BQuery() as bq:
    results = bq.bql(query)
    df = results.combine().pivot(
        index="PERIODENDDATE", on="#segment", values="#revenue"
    )
    print(df)

┌─────────────────┬──────────────┬──────────────────────┬────────┬────────────┐
│ PERIODENDDATE ┆ Broadcasting ┆ Production Companies ┆ Other  ┆ Adjustment │
│ ---             ┆ ---          ┆ ---                  ┆ ---    ┆ ---        │
│ date            ┆ f64          ┆ f64                  ┆ f64    ┆ f64        │
╞═════════════════╪══════════════╪══════════════════════╪════════╪════════════╡
│ 2023-09-30      ┆ 7.83e8       ┆ 2e7                  ┆ 1.6e7  ┆ null       │
│ 2023-12-31      ┆ 8.13e8       ┆ 3.2e7                ┆ 1.9e7  ┆ null       │
│ 2024-03-31      ┆ 7.8e8        ┆ 2.4e7                ┆ 1.9e7  ┆ null       │
│ 2024-06-30      ┆ 8.08e8       ┆ 1.8e7                ┆ 0.0    ┆ null       │
│ 2024-09-30      ┆ 9.24e8       ┆ 2.6e7                ┆ 1.7e7  ┆ null       │
└─────────────────┴──────────────┴──────────────────────┴────────┴────────────┘

Actual and Forward EPS Estimates

with BQuery() as bq:
    results = bq.bql("""
        let(#eps=iseps(faperiod_type='A',
                        faperiodoffset=range(-4,2));)
        get(#eps)
        for(['IBM US Equity'])
    """)
    print(results[0])
┌───────────────┬───────┬───────────────┬────────────┬─────────────────┬──────────┐ │ ID            ┆ #eps  ┆ REVISIONDATE ┆ ASOFDATE ┆ PERIODEND_DATE ┆ CURRENCY │ │ ---           ┆ ---   ┆ ---           ┆ ---        ┆ ---             ┆ ---      │ │ str           ┆ f64   ┆ date          ┆ date       ┆ date            ┆ str      │ ╞═══════════════╪═══════╪═══════════════╪════════════╪═════════════════╪══════════╡ │ IBM US Equity ┆ 10.63 ┆ 2022-02-22    ┆ 2024-12-14 ┆ 2019-12-31      ┆ USD      │ │ IBM US Equity ┆ 6.28  ┆ 2023-02-28    ┆ 2024-12-14 ┆ 2020-12-31      ┆ USD      │ │ IBM US Equity ┆ 6.41  ┆ 2023-02-28    ┆ 2024-12-14 ┆ 2021-12-31      ┆ USD      │ │ IBM US Equity ┆ 1.82  ┆ 2024-03-18    ┆ 2024-12-14 ┆ 2022-12-31      ┆ USD      │ │ IBM US Equity ┆ 8.23  ┆ 2024-03-18    ┆ 2024-12-14 ┆ 2023-12-31      ┆ USD      │ │ IBM US Equity ┆ 7.891 ┆ 2024-12-13    ┆ 2024-12-14 ┆ 2024-12-31      ┆ USD      │ │ IBM US Equity ┆ 9.236 ┆ 2024-12-13    ┆ 2024-12-14 ┆ 2025-12-31      ┆ USD      │ └───────────────┴───────┴───────────────┴────────────┴─────────────────┴──────────┘

Average issuer OAS spread per maturity bucket

# Example: Average OAS-spread per maturity bucket
query = """
let(
    #bins = bins(maturity_years,
                 [3,9,18,30],
                 ['(1) 0-3','(2) 3-9','(3) 9-18','(4) 18-30','(5) 30+']);
    #average_spread = avg(group(spread(st=oas),#bins));
)
get(#average_spread)
for(filter(bonds('NVDA US Equity', issuedby = 'ENTITY'),
           maturity_years != NA))
"""
with BQuery() as bq:     results = bq.bql(query)     print(results[0])

┌───────────┬─────────────────┬────────────┬───────────────┬───────────┐ │ ID        ┆ #averagespread ┆ DATE       ┆ ORIGIDS      ┆ #BINS     │ │ ---       ┆ ---             ┆ ---        ┆ ---           ┆ ---       │ │ str       ┆ f64             ┆ date       ┆ str           ┆ str       │ ╞═══════════╪═════════════════╪════════════╪═══════════════╪═══════════╡ │ (1) 0-3   ┆ 31.195689       ┆ 2024-12-14 ┆ QZ552396 Corp ┆ (1) 0-3   │ │ (2) 3-9   ┆ 59.580383       ┆ 2024-12-14 ┆ null          ┆ (2) 3-9   │ │ (3) 9-18  ┆ 110.614416      ┆ 2024-12-14 ┆ BH393780 Corp ┆ (3) 9-18  │ │ (4) 18-30 ┆ 135.160279      ┆ 2024-12-14 ┆ BH393781 Corp ┆ (4) 18-30 │ │ (5) 30+   ┆ 150.713405      ┆ 2024-12-14 ┆ BH393782 Corp ┆ (5) 30+   │ └───────────┴─────────────────┴────────────┴───────────────┴───────────┘

Technical Analysis: stocks with 20d EMA > 200d EMA and RSI > 53

with BQuery() as bq:
    results = bq.bql(
        """
        let(#ema20=emavg(period=20);
            #ema200=emavg(period=200);
            #rsi=rsi(close=px_last());)
        get(name(), #ema20, #ema200, #rsi)
        for(filter(members('OMX Index'),
                    and(#ema20 > #ema200, #rsi > 53)))
        with(fill=PREV)
        """
    )
    print(results.combine())

┌─────────────────┬──────────────────┬────────────┬────────────┬──────────┬────────────┬───────────┐
│ ID              ┆ name()           ┆ #ema20     ┆ DATE       ┆ CURRENCY ┆ #ema200    ┆ #rsi      │
│ ---             ┆ ---              ┆ ---        ┆ ---        ┆ ---      ┆ ---        ┆ ---       │
│ str             ┆ str              ┆ f64        ┆ date       ┆ str      ┆ f64        ┆ f64       │
╞═════════════════╪══════════════════╪════════════╪════════════╪══════════╪════════════╪═══════════╡
│ ERICB SS Equity ┆ Telefonaktiebola ┆ 90.152604  ┆ 2024-12-16 ┆ SEK      ┆ 75.072151  ┆ 56.010028 │
│                 ┆ get LM Ericsso   ┆            ┆            ┆          ┆            ┆           │
│ ABB SS Equity   ┆ ABB Ltd          ┆ 630.622469 ┆ 2024-12-16 ┆ SEK      ┆ 566.571183 ┆ 53.763102 │
│ SEBA SS Equity  ┆ Skandinaviska    ┆ 153.80595  ┆ 2024-12-16 ┆ SEK      ┆ 150.742394 ┆ 56.460733 │
│                 ┆ Enskilda Banken  ┆            ┆            ┆          ┆            ┆           │
│ ASSAB SS Equity ┆ Assa Abloy AB    ┆ 339.017591 ┆ 2024-12-16 ┆ SEK      ┆ 317.057573 ┆ 53.351619 │
└─────────────────┴──────────────────┴────────────┴────────────┴──────────┴────────────┴───────────┘

Bond Universe from Equity Ticker

# Get Bond Universe from Equity Ticker
query = """
let(#rank=normalizedpaymentrank();
    #oas=spread(st=oas);
    #nxtcall=nxtcall_dt();
    )
get(name(), #rank, #nxt_call, #oas)
for(filter(bonds('GTN US Equity'), series() == '144A'))
"""
with BQuery() as bq:     results = bq.bql(query)     df = results.combine()     print(df)

┌───────────────┬───────────────────┬──────────────────┬────────────┬────────────┬────────────┐ │ ID            ┆ name()            ┆ #rank            ┆ #nxt_call  ┆ #oas       ┆ DATE       │ │ ---           ┆ ---               ┆ ---              ┆ ---        ┆ ---        ┆ ---        │ │ str           ┆ str               ┆ str              ┆ date       ┆ f64        ┆ date       │ ╞═══════════════╪═══════════════════╪══════════════════╪════════════╪════════════╪════════════╡ │ YX231113 Corp ┆ GTN 10 ½ 07/15/29 ┆ 1st Lien Secured ┆ 2026-07-15 ┆ 598.66491  ┆ 2024-12-17 │ │ BS116983 Corp ┆ GTN 5 ⅜ 11/15/31  ┆ Sr Unsecured     ┆ 2026-11-15 ┆ 1193.17529 ┆ 2024-12-17 │ │ AV438089 Corp ┆ GTN 7 05/15/27    ┆ Sr Unsecured     ┆ 2024-12-24 ┆ 400.340456 ┆ 2024-12-17 │ │ ZO860846 Corp ┆ GTN 4 ¾ 10/15/30  ┆ Sr Unsecured     ┆ 2025-10-15 ┆ 1249.34346 ┆ 2024-12-17 │ │ LW375188 Corp ┆ GTN 5 ⅞ 07/15/26  ┆ Sr Unsecured     ┆ 2025-01-13 ┆ 173.761744 ┆ 2024-12-17 │ └───────────────┴───────────────────┴──────────────────┴────────────┴────────────┴────────────┘

Bonds Total Returns

# Total Return of GTN Bonds
query = """
let(#rng = range(-1M, 0D);
    #rets = returnseries(calcinterval=#rng,per=W);)
get(#rets)
for(filter(bonds('GTN US Equity'), series() == '144A'))
"""
with BQuery() as bq:     results = bq.bql(query)     df = results[0].pivot(on="ID", index="DATE", values="#rets")     print(df)

shape: (6, 6) ┌────────────┬───────────────┬───────────────┬───────────────┬───────────────┬───────────────┐ │ DATE       ┆ YX231113 Corp ┆ BS116983 Corp ┆ AV438089 Corp ┆ ZO860846 Corp ┆ LW375188 Corp │ │ ---        ┆ ---           ┆ ---           ┆ ---           ┆ ---           ┆ ---           │ │ date       ┆ f64           ┆ f64           ┆ f64           ┆ f64           ┆ f64           │ ╞════════════╪═══════════════╪═══════════════╪═══════════════╪═══════════════╪═══════════════╡ │ 2024-11-17 ┆ null          ┆ null          ┆ null          ┆ null          ┆ null          │ │ 2024-11-24 ┆ 0.001653      ┆ 0.051179      ┆ 0.020363      ┆ 0.001371      ┆ -0.002939     │ │ 2024-12-01 ┆ 0.002837      ┆ 0.010405      ┆ -0.001466     ┆ 0.007275      ┆ 0.000581      │ │ 2024-12-08 ┆ -0.000041     ┆ 0.016145      ┆ 0.000766      ┆ 0.024984      ┆ 0.000936      │ │ 2024-12-15 ┆ 0.001495      ┆ -0.047        ┆ -0.000233     ┆ -0.043509     ┆ 0.002241      │ │ 2024-12-17 ┆ 0.00008       ┆ -0.000004     ┆ -0.0035       ┆ -0.007937     ┆ 0.000064      │ └────────────┴───────────────┴───────────────┴───────────────┴───────────────┴───────────────┘

Maturity Wall for US HY Bonds

query = """
let(#mv=sum(group(amt_outstanding(currency=USD),
                  by=[year(maturity()), industry_sector()]));)
get(#mv)
for(members('LF98TRUU Index'))
"""
with BQuery() as bq:
    results = bq.bql(query)
df = results.combine().rename(
    {"YEAR(MATURITY())": "maturity", "INDUSTRY_SECTOR()": "sector", "#mv": "mv"}
)
print(df.pivot(index="maturity", on="sector", values="mv").head())

shape: (5, 11) ┌──────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐ │ maturity ┆ Basic     ┆ Consumer, ┆ Energy    ┆ … ┆ Financial ┆ Technolog ┆ Utilities ┆ Diversifi │ │ ---      ┆ Materials ┆ Non-cycli ┆ ---       ┆   ┆ ---       ┆ y         ┆ ---       ┆ ed        │ │ i64      ┆ ---       ┆ cal       ┆ f64       ┆   ┆ f64       ┆ ---       ┆ f64       ┆ ---       │ │          ┆ f64       ┆ ---       ┆           ┆   ┆           ┆ f64       ┆           ┆ f64       │ │          ┆           ┆ f64       ┆           ┆   ┆           ┆           ┆           ┆           │ ╞══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡ │ 2025     ┆ 1.5e8     ┆ 5.34916e8 ┆ 5e8       ┆ … ┆ null      ┆ null      ┆ null      ┆ null      │ │ 2026     ┆ 4.4013e9  ┆ 9.3293e9  ┆ 8.2931e9  ┆ … ┆ 1.3524e10 ┆ 4.0608e9  ┆ 2.5202e9  ┆ null      │ │ 2027     ┆ 8.3921e9  ┆ 2.3409e10 ┆ 1.2427e10 ┆ … ┆ 1.9430e10 ┆ 4.3367e9  ┆ 3.6620e9  ┆ null      │ │ 2028     ┆ 1.4701e10 ┆ 3.7457e10 ┆ 2.2442e10 ┆ … ┆ 2.3341e10 ┆ 9.9143e9  ┆ 7.6388e9  ┆ 5e8       │ │ 2029     ┆ 1.6512e10 ┆ 5.7381e10 ┆ 3.9286e10 ┆ … ┆ 4.2337e10 ┆ 2.2660e10 ┆ 5.8558e9  ┆ null      │ └──────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘

Additional Documentation & Resources

• API Documentation: Detailed documentation and function references are available in https://marekozana.github.io/polars-bloomberg

• Additional Examples: Check out examples/ for hands-on notebooks demonstrating a variety of use cases.

• Bloomberg Developer Resources: For more details on the Bloomberg API itself, visit the Bloomberg Developer's page.