Python library providing a Polars DataFrame interface for easy and intuitive access to Bloomberg API.

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Polars Bloomberg Logo

Polars + Bloomberg Open API

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.

Introduction
Prerequisites
Installation
Quick Start
Core Methods
Additional Documentation and Resources

Introduction

Working with Bloomberg data in Python often feels more complicated than using their well-known Excel interface. Great projects like blp, xbbg, and pdblp have made this easier by pulling data directly into pandas.

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

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 3.8+ recommended.

Installation

pip install polars-bloomberg

Quick Start

"Hello World" Example (under 1 minute):

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  │
└────────────────┴─────────┘

What this does:

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

If you see a price in df, your setup is working 🤩!!!

Core Methods

BQuery is your main interface. Using a context manager ensures the connection opens and closes cleanly. Within this session, you can use:

bq.bdp() for Bloomberg Data Points (single-value fields).
bq.bdh() for Historical Data (time series).
bq.bql() for complex Bloomberg Query Language requests.

BDP

Use Case: Fetch the latest single-value data points (like last price, currency, or descriptive fields).

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   │
└────────────────┴─────────┴───────┘

Expand for more BDP Examples

BDP with different column types

polars-bloomberg correctly infers column type as shown in this example:

with BQuery() as bq:
    df = bq.bdp(["XS2930103580 Corp", "USX60003AC87 Corp"],
                ["SECURITY_DES", "YAS_ZSPREAD", "CRNCY", "NXT_CALL_DT"])

┌───────────────────┬────────────────┬─────────────┬───────┬─────────────┐
│ security          ┆ SECURITY_DES   ┆ YAS_ZSPREAD ┆ CRNCY ┆ NXT_CALL_DT │
│ ---               ┆ ---            ┆ ---         ┆ ---   ┆ ---         │
│ 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  │
└───────────────────┴────────────────┴─────────────┴───────┴─────────────┘

BDP with overrides

User can submit list of tuples with overrides

with BQuery() as bq:
    df = bq.bdp(
        ["IBM US Equity"],
        ["PX_LAST", "CRNCY_ADJ_PX_LAST"],
        overrides=[("EQY_FUND_CRNCY", "SEK")],
    )

┌───────────────┬─────────┬───────────────────┐
│ security      ┆ PX_LAST ┆ CRNCY_ADJ_PX_LAST │
│ ---           ┆ ---     ┆ ---               │
│ str           ┆ f64     ┆ f64               │
╞═══════════════╪═════════╪═══════════════════╡
│ IBM US Equity ┆ 230.82  ┆ 2535.174          │
└───────────────┴─────────┴───────────────────┘

BDP with date overrides

Overrides for dates has to be in format YYYYMMDD

with BQuery() as bq:
    df = bq.bdp(["USX60003AC87 Corp"], ["SETTLE_DT"],
                overrides=[("USER_LOCAL_TRADE_DATE", "20241014")])

┌───────────────────┬────────────┐
│ security          ┆ SETTLE_DT  │
│ ---               ┆ ---        │
│ str               ┆ date       │
╞═══════════════════╪════════════╡
│ USX60003AC87 Corp ┆ 2024-10-15 │
└───────────────────┴────────────┘

with BQuery() as bq:
    df = bq.bdp(['USDSEK Curncy', 'SEKCZK Curncy'], 
                ['SETTLE_DT', 'PX_LAST'], 
                overrides=[('REFERENCE_DATE', '20200715')]
               )

┌───────────────┬────────────┬─────────┐
│ security      ┆ SETTLE_DT  ┆ PX_LAST │
│ ---           ┆ ---        ┆ ---     │
│ str           ┆ date       ┆ f64     │
╞═══════════════╪════════════╪═════════╡
│ USDSEK Curncy ┆ 2020-07-17 ┆ 10.9778 │
│ SEKCZK Curncy ┆ 2020-07-17 ┆ 2.1698  │
└───────────────┴────────────┴─────────┘

BDH

Use Case: Retrieve historical data over a date range, such as daily closing prices or volumes.

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  │
└───────────────┴────────────┴─────────┘

Expand for more BDH examples

BDH with multiple securities / fields

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    │
└───────────────┴────────────┴─────────┴──────────────┘

BDH with options - periodicitySelection: Monthly

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  │
└────────────────┴────────────┴─────────┘

BQL

Use Case: Run more advanced queries to screen securities, calculate analytics (like moving averages), or pull fundamental data with complex conditions.

Returns: list of polars dataframes, one per each data-item in get()statement.

Simple BQL Example

# resulting object is list of pl.DataFrames, extract and print the first one
with BQuery() as bq:
    df_lst = bq.bql("get(px_last) for(['IBM US Equity'])")
    print(df_lst[0])
┌───────────────┬─────────┬────────────┬──────────┐
│ ID            ┆ px_last ┆ DATE       ┆ CURRENCY │
│ ---           ┆ ---     ┆ ---        ┆ ---      │
│ str           ┆ f64     ┆ date       ┆ str      │
╞═══════════════╪═════════╪════════════╪══════════╡
│ IBM US Equity ┆ 230.82  ┆ 2024-12-14 ┆ USD      │
└───────────────┴─────────┴────────────┴──────────┘

Single Item with Multiple Securities

Another example with single data item but two securities. Still only one pl.DataFrame in resulting list (only one data item in get())

with BQuery() as bq:
    df_lst = bq.bql("get(px_last) for(['IBM US Equity', 'SEBA SS Equity'])")

> print(f"n={len(df_lst)}")
n=1

> print(df_lst[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      │
└────────────────┴─────────┴────────────┴──────────┘

Multiple data-items in `get`

Lets consider example with two data-items in get statement. Note that the resulting list has two pl.DataFrames.

with BQuery() as bq:
    df_lst = bq.bql("get(name, px_last) for(['IBM US Equity'])")
    
> print(f"n={len(df_lst)}")
n=2

> print(df_lst[0])
┌───────────────┬────────────────────────────────┐
│ ID            ┆ name                           │
│ ---           ┆ ---                            │
│ str           ┆ str                            │
╞═══════════════╪════════════════════════════════╡
│ IBM US Equity ┆ International Business Machine │
└───────────────┴────────────────────────────────┘

> print(df_lst[1])
shape: (1, 4)
┌───────────────┬─────────┬────────────┬──────────┐
│ ID            ┆ px_last ┆ DATE       ┆ CURRENCY │
│ ---           ┆ ---     ┆ ---        ┆ ---      │
│ str           ┆ f64     ┆ date       ┆ str      │
╞═══════════════╪═════════╪════════════╪══════════╡
│ IBM US Equity ┆ 230.82  ┆ 2024-12-14 ┆ USD      │
└───────────────┴─────────┴────────────┴──────────┘

Since both DataFrames have the same index ID one can join the results into single table.

>>> print(df_lst[0].join(df_lst[1], on='ID'))

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

As alternative one can use method combine on BqlResult which attempts to combine all dataframes in the list on common columns:

>>> print(df_lst.combine())
┌───────────────┬────────────────────────────────┬────────────┬────────────┬──────────┐
│ ID            ┆ name                           ┆ px_last    ┆ DATE       ┆ CURRENCY │
│ ---           ┆ ---                            ┆ ---        ┆ ---        ┆ ---      │
│ str           ┆ str                            ┆ f64        ┆ date       ┆ str      │
╞═══════════════╪════════════════════════════════╪════════════╪════════════╪══════════╡
│ IBM US Equity ┆ International Business Machine ┆ 229.429993 ┆ 2024-12-16 ┆ USD      │
└───────────────┴────────────────────────────────┴────────────┴────────────┴──────────┘

ZSpread vs Duration on SEB and SHBASS CoCo bonds from SRCH

In this example we have three data-items in getstatement. The universe is from Bloomberg SRCH function filtered only on tickers 'SEB' and 'SHBASS'.

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:
    df_lst = bq.bql(query)
    print(df_lst.combine())

┌───────────────┬─────────────────┬──────┬────────────┬────────┐
│ 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

This example shows aggregation (average) per group (sector) for members of an index. The reulting list has only one element since there is only one data-item in get

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

┌──────────────┬───────────┬──────────────┬────────────┬──────────────┬──────────────┬─────────────┐
│ ID           ┆ #avg_pe   ┆ REVISION_DAT ┆ AS_OF_DATE ┆ PERIOD_END_D ┆ ORIG_IDS     ┆ GICS_SECTOR │
│ ---          ┆ ---       ┆ 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 of all Swedish USD AT1 bonds

# Get current axes for Swedish AT1 bonds in USD
query="""
    let(#ax=axes();)
    get(security_des, #ax)
    for(filter(bondsuniv(ACTIVE),
        crncy()=='USD' and
        basel_iii_designation() == 'Additional Tier 1' and
        country_iso() == 'SE'))
"""

with BQuery() as bq:
    df_lst = bq.bql(query)
    print(df_lst.combine())

┌───────────────┬─────────────────┬─────┬───────────┬───────────┬────────────────┬────────────────┐
│ ID            ┆ security_des    ┆ #ax ┆ ASK_DEPTH ┆ BID_DEPTH ┆ ASK_TOTAL_SIZE ┆ BID_TOTAL_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          │
└───────────────┴─────────────────┴─────┴───────────┴───────────┴────────────────┴────────────────┘

Segments

The following example shows handling of two data-items with different length. The first dataframe describes the segments (and has length 5 in this case), while the second dataframe contains time series. One can join the dataframes on common columns and pivot the segments into columns as shown below:

# revenue per segment
query = """
    let(#segment=segment_name();
        #revenue=sales_Rev_turn(fpt=q, fpr=range(2023Q3, 2024Q3));
        )
    get(#segment, #revenue)
    for(segments('GTN US Equity',type=reported,hierarchy=PRODUCT, level=1))
"""
with BQuery() as bq:
    df_lst = bq.bql(query)
    df = (
        df_lst[0]
        .join(df_lst[1], on=["ID", "ID_DATE", "AS_OF_DATE"])
        .pivot(index="PERIOD_END_DATE", on="#segment", values="#revenue")
    )
    print(df)

┌─────────────────┬──────────────┬──────────────────────┬────────┬────────────┐
│ PERIOD_END_DATE ┆ 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:
    df_lst = bq.bql("""
        let(#eps=is_eps(fa_period_type='A',
                        fa_period_offset=range(-4,2));)
        get(#eps)
        for(['IBM US Equity'])
    """)
    print(df_lst[0])

┌───────────────┬───────┬───────────────┬────────────┬─────────────────┬──────────┐
│ ID            ┆ #eps  ┆ REVISION_DATE ┆ AS_OF_DATE ┆ PERIOD_END_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:
    df_lst = bq.bql(query)
    print(df_lst[0])

┌───────────┬─────────────────┬────────────┬───────────────┬───────────┐
│ ID        ┆ #average_spread ┆ DATE       ┆ ORIG_IDS      ┆ #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:
    df_lst = 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(df_lst.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

query = """
let(#rank=normalized_payment_rank();
    #oas=spread(st=oas);
    #nxt_call=nxt_call_dt();
    )
get(name(), #rank, #nxt_call, #oas)
for(filter(bonds('GTN US Equity'), series() == '144A'))
"""

with BQuery() as bq:
    df_lst = bq.bql(query)

    df = (
        df_lst[0]
        .join(df_lst[1], on="ID")
        .join(df_lst[2], on="ID")
        .join(df_lst[3], on="ID")
    )
    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 ┆ 597.329513  ┆ 2024-12-14 │
│ BS116983 Corp ┆ GTN 5 ⅜ 11/15/31  ┆ Sr Unsecured     ┆ 2026-11-15 ┆ 1192.83614  ┆ 2024-12-14 │
│ AV438089 Corp ┆ GTN 7 05/15/27    ┆ Sr Unsecured     ┆ 2024-12-23 ┆ 391.133436  ┆ 2024-12-14 │
│ ZO860846 Corp ┆ GTN 4 ¾ 10/15/30  ┆ Sr Unsecured     ┆ 2025-10-15 ┆ 1232.554695 ┆ 2024-12-14 │
│ LW375188 Corp ┆ GTN 5 ⅞ 07/15/26  ┆ Sr Unsecured     ┆ 2025-01-12 ┆ 171.708702  ┆ 2024-12-14 │
└───────────────┴───────────────────┴──────────────────┴────────────┴─────────────┴────────────┘

Bonds Total Returns

This is example of a single-item query returning total return for all GTN bonds in a long dataframe. We can easily pivot it into wide format, as in the example below

# Total Return of GTN Bonds
query="""
let(#rng = range(-1M, 0D);
    #rets = return_series(calc_interval=#rng,per=W);)
get(#rets)
for(filter(bonds('GTN US Equity'), series() == '144A'))
"""

with BQuery() as bq:
    df_lst = bq.bql(query)
    df = df_lst[0].pivot(on='ID', index='DATE', values='#rets')
    print(df)

┌────────────┬───────────────┬───────────────┬───────────────┬───────────────┬───────────────┐
│ DATE       ┆ YX231113 Corp ┆ BS116983 Corp ┆ AV438089 Corp ┆ ZO860846 Corp ┆ LW375188 Corp │
│ ---        ┆ ---           ┆ ---           ┆ ---           ┆ ---           ┆ ---           │
│ date       ┆ f64           ┆ f64           ┆ f64           ┆ f64           ┆ f64           │
╞════════════╪═══════════════╪═══════════════╪═══════════════╪═══════════════╪═══════════════╡
│ 2024-11-14 ┆ null          ┆ null          ┆ null          ┆ null          ┆ null          │
│ 2024-11-21 ┆ -0.002378     ┆ 0.016565      ┆ 0.022831      ┆ 0.000987      ┆ -0.002815     │
│ 2024-11-28 ┆ 0.002345      ┆ -0.005489     ┆ -0.004105     ┆ 0.011748      ┆ 0.00037       │
│ 2024-12-05 ┆ 0.001403      ┆ 0.016999      ┆ 0.002058      ┆ 0.013095      ┆ 0.001003      │
│ 2024-12-12 ┆ -0.000485     ┆ -0.040228     ┆ -0.000872     ┆ -0.038048     ┆ 0.001122      │
│ 2024-12-14 ┆ 0.000988      ┆ -0.003833     ┆ 0.000247      ┆ -0.004818     ┆ 0.00136       │
└────────────┴───────────────┴───────────────┴───────────────┴───────────────┴───────────────┘

Additional Documentation & Resources

API Documentation: Detailed documentation and function references are available in the API documentation file within the examples/ directory.
Additional Examples: Check out (examples/Examples.ipynb) 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.

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polars-bloomberg 0.4.0

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Polars + Bloomberg Open API

Table of Contents

Introduction

Prerequisites

Installation

Quick Start

Core Methods

BDP

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

BDP with different column types

BDP with overrides

BDP with date overrides

BDH

BDH with multiple securities / fields

BDH with options - periodicitySelection: Monthly

BQL

Simple BQL Example

Single Item with Multiple Securities

Multiple data-items in get

ZSpread vs Duration on SEB and SHBASS CoCo bonds from SRCH

Average PE per Sector

Axes

Segments

Actual and Forward EPS Estimates

Average issuer OAS spread per maturity bucket

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

Bond Universe from Equity Ticker

Bonds Total Returns

Additional Documentation & Resources

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