polars-bloomberg 0.2.4

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

Polars + Bloomberg Open API

Polars + Bloomberg Open API is a Python library that facilitates integration of Bloomberg data into Polars DataFrames. Designed for users familiar with Pandas or Excel, it offers minimal-boilerplate functions such as bdp(), bdh(), and bql(). Leverage Polars' high-performance capabilities alongside the Bloomberg API for lightning-fast DataFrame operations and a minimal memory footprint.

Key Benefits:

• Intuitive "Excel-like" methods: bdp(), bdh(), bql()
• Outputs data as Polars DataFrames
• Lightweight design with no dependency on pandas
• Quickly prototype, test, and scale complex financial analyses.

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 Guide (5 Minutes)

Below is a simple example to get you started. For more comprehensive examples, please see the examples/ directory.

Concept:
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 - Bloomberg Data Point

Example: Fetching the Last Price of Apple and Microsoft

from polars_bloomberg import BQuery

with BQuery() as bq:
    df = bq.bdp(['AAPL US Equity', 'MSFT US Equity'], ['PX_LAST'])

shape: (2, 2)

security	PX_LAST
str	f64
"AAPL US Equity"	242.84
"MSFT US Equity"	443.57

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"])

shape: (2, 5)

security	SECURITY_DES	YAS_ZSPREAD	CRNCY	NXT_CALL_DT
str	str	f64	str	date
"XS2930103580 Corp"	"SEB 6 3/4 PERP"	327.309349	"USD"	2031-11-04
"USX60003AC87 Corp"	"NDAFH 6.3 PERP"	315.539222	"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")])

</style> shape: (1, 3)

security	PX_LAST	CRNCY_ADJ_PX_LAST
str	f64	f64
"IBM US Equity"	238.04	2607.401

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")])

shape: (1, 2)

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')]
               )

shape: (2, 3)

security	SETTLE_DT	PX_LAST
str	date	f64
"USDSEK Curncy"	2020-07-17	10.9343
"SEKCZK Curncy"	2020-07-17	2.1718

BDH - Bloomberg Data History

with BQuery() as bq:
    df = bq.bdh(['AAPL US Equity', 'TSLA US Equity'], 
                ['PX_LAST', 'VOLUME'], 
                start_date=date(2019, 1, 1), 
                end_date=date(2019, 1, 10))

shape: (14, 4)

security	date	PX_LAST	VOLUME
str	date	f64	f64
"AAPL US Equity"	2019-01-02	39.48	1.48158948e8
"AAPL US Equity"	2019-01-03	35.548	3.6524878e8
…	…	…	…
"TSLA US Equity"	2019-01-09	22.5687	8.1494175e7
"TSLA US Equity"	2019-01-10	22.998	9.084531e7

More BDH Examples

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"})

shape: (3, 3)

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 - Bloomberg Query Language

Allows to run complex bql queries and get result in wide polars.DataFramewith correct polars types

with BQuery() as bq:
    df = bq.bql("get(px_last) for(['IBM US Equity', 'OMX Index'])")

┌───────────────┬────────────┬──────────────┬──────────────────┐
│ ID            ┆ px_last    ┆ px_last.DATE ┆ px_last.CURRENCY │
│ ---           ┆ ---        ┆ ---          ┆ ---              │
│ str           ┆ f64        ┆ date         ┆ str              │
╞═══════════════╪════════════╪══════════════╪══════════════════╡
│ IBM US Equity ┆ 231.024994 ┆ 2024-12-10   ┆ USD              │
│ OMX Index     ┆ 2602.806   ┆ 2024-12-10   ┆ SEK              │
└───────────────┴────────────┴──────────────┴──────────────────┘

More BQL Examples

Actual and Forward EPS Estimates

df = bq.bql("""
    let(#eps=is_eps(fa_period_type='A',
                    fa_period_offset=range(-4,2));)
    get(#eps)
    for(['IBM US Equity'])
""")

shape: (7, 6)

ID	#eps	#eps.REVISION_DATE	#eps.AS_OF_DATE	#eps.PERIOD_END_DATE	#eps.CURRENCY
str	f64	date	date	date	str
"IBM US Equity"	10.63	2022-02-22	2024-12-07	2019-12-31	"USD"
"IBM US Equity"	6.28	2023-02-28	2024-12-07	2020-12-31	"USD"
…	…	…	…	…	…
"IBM US Equity"	9.236	2024-12-07	2024-12-07	2025-12-31	"USD"

ZSpread vs Duration on bonds from SRCH

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 = bq.bql(query)

shape: (6, 6)
┌───────────────┬─────────────────┬──────────┬────────────┬────────────┬─────────────┐
│ ID            ┆ name()          ┆ #dur     ┆ #dur.DATE  ┆ #zsprd     ┆ #zsprd.DATE │
│ ---           ┆ ---             ┆ ---      ┆ ---        ┆ ---        ┆ ---         │
│ str           ┆ str             ┆ f64      ┆ date       ┆ f64        ┆ date        │
╞═══════════════╪═════════════════╪══════════╪════════════╪════════════╪═════════════╡
│ BW924993 Corp ┆ SEB 6 ⅞ PERP    ┆ 2.244382 ┆ 2024-12-10 ┆ 229.930933 ┆ 2024-12-10  │
│ ZO703956 Corp ┆ SHBASS 4 ¾ PERP ┆ 4.958582 ┆ 2024-12-10 ┆ 269.963777 ┆ 2024-12-10  │
│ ZO703315 Corp ┆ SHBASS 4 ⅜ PERP ┆ 1.968658 ┆ 2024-12-10 ┆ 232.839648 ┆ 2024-12-10  │
│ YU819930 Corp ┆ SEB 6 ¾ PERP    ┆ 5.388785 ┆ 2024-12-10 ┆ 324.70196  ┆ 2024-12-10  │
│ ZQ349286 Corp ┆ SEB 5 ⅛ PERP    ┆ 0.409083 ┆ 2024-12-10 ┆ 165.405465 ┆ 2024-12-10  │
│ YV402592 Corp ┆ SEB Float PERP  ┆ 0.22527  ┆ 2024-12-10 ┆ 248.756    ┆ 2024-12-10  │
└───────────────┴─────────────────┴──────────┴────────────┴────────────┴─────────────┘

Average issuer 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 = bq.bql(query)

┌───────────┬─────────────────┬──────────────────────┬──────────────────────┬──────────────────────┐
│ ID        ┆ #average_spread ┆ #average_spread.DATE ┆ #average_spread.ORIG ┆ #average_spread.#BIN │
│ ---       ┆ ---             ┆ ---                  ┆ _IDS                 ┆ S                    │
│ str       ┆ f64             ┆ date                 ┆ ---                  ┆ ---                  │
│           ┆                 ┆                      ┆ str                  ┆ str                  │
╞═══════════╪═════════════════╪══════════════════════╪══════════════════════╪══════════════════════╡
│ (1) 0-3   ┆ 30.638311       ┆ 2024-12-10           ┆ QZ552396 Corp        ┆ (1) 0-3              │
│ (2) 3-9   ┆ 59.772151       ┆ 2024-12-10           ┆ null                 ┆ (2) 3-9              │
│ (3) 9-18  ┆ 106.722341      ┆ 2024-12-10           ┆ BH393780 Corp        ┆ (3) 9-18             │
│ (4) 18-30 ┆ 129.945414      ┆ 2024-12-10           ┆ BH393781 Corp        ┆ (4) 18-30            │
│ (5) 30+   ┆ 151.318634      ┆ 2024-12-10           ┆ BH393782 Corp        ┆ (5) 30+              │
└───────────┴─────────────────┴──────────────────────┴──────────────────────┴──────────────────────┘

Technical Analysis: stocks with 20d EMA > 200d EMA and RSI > 70

with BQuery() as bq:
    df = 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 > 70)))
        with(fill=PREV)
        """
    )

shape: (2, 10)

ID	name()	#ema20	#ema20.DATE	#ema20.CURRENCY	#ema200	#ema200.DATE	#ema200.CURRENCY	#rsi	#rsi.DATE
str	str	f64	date	str	f64	date	str	f64	date
"SKFB SS Equity"	"SKF AB"	210.185019	2024-12-08	"SEK"	204.16756	2024-12-08	"SEK"	72.255568	2024-12-08
"ABB SS Equity"	"ABB Ltd"	623.496942	2024-12-08	"SEK"	561.902577	2024-12-08	"SEK"	72.144556	2024-12-08

Swedish USD AT1 Bonds with Bid Axis

query="""
let(#ax=axes();)
get(ticker, cpn(), nxt_call_dt(), #ax)
for(filter(bondsuniv(ACTIVE), 
    crncy()=='USD' and 
    basel_iii_designation() == 'Additional Tier 1' and 
    country_iso() == 'SE' and 
    is_axed('Bid') == True))
"""
with BQuery() as bq:
    df = bq.bql(query)

shape: (8, 11)

ID	ticker	cpn()	cpn().MULTIPLIER	cpn().CPN_TYP	nxt_call_dt()	#ax	#ax.ASK_DEPTH	#ax.BID_DEPTH	#ax.ASK_TOTAL_SIZE	#ax.BID_TOTAL_SIZE
str	str	f64	f64	str	date	str	i64	i64	f64	f64
"YU819930 Corp"	"SEB"	6.75	1.0	"VARIABLE"	2031-11-04	"Y"	1	1	5e6	1.8e6
"ZQ349286 Corp"	"SEB"	5.125	1.0	"VARIABLE"	2025-05-13	"Y"	3	9	6.7e6	5e7
"ZF859199 Corp"	"SWEDA"	7.75	1.0	"VARIABLE"	2030-03-17	"Y"	1	2	5e6	7e6
"BW924993 Corp"	"SEB"	6.875	1.0	"VARIABLE"	2027-06-30	"Y"	2	3	8.2e6	1.1e7
"ZL122341 Corp"	"SWEDA"	7.625	1.0	"VARIABLE"	2028-03-17	"Y"	1	6	2.6e6	2.34e7
"ZO703956 Corp"	"SHBASS"	4.75	1.0	"VARIABLE"	2031-03-01	"Y"	1	2	3.2e6	6e6
"BR069680 Corp"	"SWEDA"	4.0	1.0	"VARIABLE"	2029-03-17	"Y"	null	1	null	3e6
"ZO703315 Corp"	"SHBASS"	4.375	1.0	"VARIABLE"	2027-03-01	"Y"	1	3	3e6	7.4e6

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 = bq.bql(query)

┌───────────────┬───────────────────┬──────────────────┬────────────┬─────────────┬────────────┐
│ ID            ┆ name()            ┆ #rank            ┆ #nxt_call  ┆ #oas        ┆ #oas.DATE  │
│ ---           ┆ ---               ┆ ---              ┆ ---        ┆ ---         ┆ ---        │
│ str           ┆ str               ┆ str              ┆ date       ┆ f64         ┆ date       │
╞═══════════════╪═══════════════════╪══════════════════╪════════════╪═════════════╪════════════╡
│ YX231113 Corp ┆ GTN 10 ½ 07/15/29 ┆ 1st Lien Secured ┆ 2026-07-15 ┆ 615.798149  ┆ 2024-12-10 │
│ BS116983 Corp ┆ GTN 5 ⅜ 11/15/31  ┆ Sr Unsecured     ┆ 2026-11-15 ┆ 1144.393892 ┆ 2024-12-10 │
│ AV438089 Corp ┆ GTN 7 05/15/27    ┆ Sr Unsecured     ┆ 2024-12-17 ┆ 389.022271  ┆ 2024-12-10 │
│ ZO860846 Corp ┆ GTN 4 ¾ 10/15/30  ┆ Sr Unsecured     ┆ 2025-10-15 ┆ 1184.969597 ┆ 2024-12-10 │
│ LW375188 Corp ┆ GTN 5 ⅞ 07/15/26  ┆ Sr Unsecured     ┆ 2025-01-06 ┆ 185.544312  ┆ 2024-12-10 │
└───────────────┴───────────────────┴──────────────────┴────────────┴─────────────┴────────────┘

Weekly (total) Returns

query="""
let(#rng = range(-3M, 0D);
    #rets = return_series(calc_interval=#rng,per=W);
    )
get(#rets)
for(filter(bonds('GTN US Equity'), series() == '144A'))
"""
with BQuery() as bq:
    df = bq.bql(query)

shape: (20, 3)
┌───────────────┬───────────┬────────────┐
│ ID            ┆ #rets     ┆ #rets.DATE │
│ ---           ┆ ---       ┆ ---        │
│ str           ┆ f64       ┆ date       │
╞═══════════════╪═══════════╪════════════╡
│ YX231113 Corp ┆ null      ┆ 2024-11-19 │
│ YX231113 Corp ┆ 0.005028  ┆ 2024-11-26 │
│ YX231113 Corp ┆ 0.000326  ┆ 2024-12-03 │
│ YX231113 Corp ┆ 0.000414  ┆ 2024-12-10 │
│ BS116983 Corp ┆ null      ┆ 2024-11-19 │
│ …             ┆ …         ┆ …          │
│ ZO860846 Corp ┆ -0.010198 ┆ 2024-12-10 │
│ LW375188 Corp ┆ null      ┆ 2024-11-19 │
│ LW375188 Corp ┆ -0.000997 ┆ 2024-11-26 │
│ LW375188 Corp ┆ 0.001294  ┆ 2024-12-03 │
│ LW375188 Corp ┆ 0.0011    ┆ 2024-12-10 │
└───────────────┴───────────┴────────────┘

API Documentation

Read the API documentation in examples/ directory

More Examples

Explore additional usage examples in the examples/ directory.

Bloomberg Documentation

For documentation on the Bloomberg API, check out the Bloomberg Developer's page.