Python Client for QUANTkiosk
Project description
[!IMPORTANT] FREE API keys are now enabled for all accounts. Get yours here!!
Paid plans will be open in the coming weeks as we finish backfill and add more features!
Python Client
Official interface to QUANTkiosk data api.
Features
- A true extension of
python- use the tools you are productive with. - Integrated symbology to easily map entities and instruments.
- Limited dependencies to ensure easy installation and no conflicts.
- Access to all data endpoints, including Ownership and Fundamentals.
- Parallel API requests for large downloads in a fraction of the time.
- Internal caching to minimize external requests and data usage
Installation
# install from PyPi (released version)
pip install qkiosk
# install from github (development version)
pip install git+https://github.com/quantkiosk/qkiosk-py.git
Set up your API key
All access to live and historical data requires a valid QK_API_KEY to be set. To get your
FREE key go to the account page. If you have not signed up for an account, you can
enter your email and your account key will be good for 250 credits a day. More than enough to explore and
make use of the API. If you need more data, just select an appropriate plan.
## Set your API key in the R session after you've load the freshly installed package
import qkiosk as qk
qk.set_apikey("<YOUR_API_KEY>")
[!TIP]
## you can also set up your key in your shell to avoid having to set it in R ## this is more permanent and definitely how you would do it in production export QK_API_KEY=<YOUR_API_KEY>
[!TIP] You don't actually need a key to get started with the package. We've included datasets that represent what each of the endpoints return to help get a feel for the breadth and depth of what QK does.
import qkiosk as qk qk.data? # Signature: qk.data() # Docstring: # Data sets in ‘qkiosk’: # # crox Crocs Institutional Holders Details By Issuer # deshaw D.E. Shaw Institutional Ownership Details (Including Submanagers) # nke Nike (NKE) Insider Ownership Data # nvda_intent Nvidia (NVDA) Restricted or Control Shares Intention to Sell # nvda_sales Nvidia (NVDA) Restricted or Control Shares Sold # pershing Pershing Square Beneficial and Activist Details # pfe Pfizer (PFE) Revenue Data # sgcap SG Capital Institutional Ownership Details (Aggregated) # # e.g. load_pershing() loads as a Pandas DataFrame
Get Started
Symbology drives everything.
The most important part of any institutional data is getting the mappings correct. The best hedge funds take having a security master for granted. It is also an incredible pain point that firms spend millions a year to manage, or tens of thousands (or more!) to buy.
Once you have access to a proper symbology - you won't understand how you ever lived without it.
QUANTkiosk is solving this once and for all by creating an open entity and security master to help leverage our data with as little effort as possible. You can read more about our project on the site, but in short, everything within the API is referenced by a QKID. To make that easy we have some helper functions. Both search and conversion is part of the batteries included mindset of QK.
Obviously you likely have some identifier to begin with. Most often this is a ticker, like AAPL or MRK. You can use this
to map to a QKID, but you can even search right from your python session. This is especially useful for things that
don't have ticker - e.g. a hedge fund you want to track.
>import qkiosk as qk
# You know the ticker or cik? Just use direct conversion:
qk.ticker("AAPL")
# AAPL
# 0000320193.0000.001S5N8V8
qk.ticker("AAPL")
# qkid: ['0000320193.0000.001S5N8V8']
qk.ticker(["AAPL","ABNB"])
# qkid: ['0000320193.0000.001S5N8V8', '0001559720.0000.001Y2XS16']
qk.cik(78003).to_name()
# ["PFIZER INC"]
qk.cik(78003).to_permid()
# ["4295904722"]
# use fuzzy search for a company like Alibaba
BABA = qk.search_co("alibaba")
#
# 1: ALIBABA GROUP HOLDING-SP ADR
# 2: ALITHYA USA INC
# 3: ALIGNMENT HEALTHCARE INC
# 4: ALTI GLOBAL INC
# 5: ALLY FINANCIAL INC
# 6: ALLEGIANT TRAVEL CO
#
#Selection: 1
BABA
# ALIBABA GROUP HOLDING-SP ADR
# 0001577552.0400.006G2JWB1
You can also search for fund managers in a similar way
janest = qk.search_mgr("jane street")
# 1: JANE STREET GROUP LLC
# 2: JANNEY MONTGOMERY SCOTT LLC
# 3: JANA PARTNERS MANAGEMENT LP
# 4: JANNEY CAPITAL MANAGEMENT LLC
# 5: JOURNEY STRATEGIC WEALTH LLC
# 6: JAMES INVESTMENT RESEARCH INC
# Selection: 1
janest
# JANE STREET GROUP LLC
# 0001595888.0000.E0000Y7E8
>
There is way more to know about the QKID, but this is the README, so we will move along. Be sure to try conversion tools like qk.ticker("AAPL") and to.ticker(qk.cik(320187)) to see the power
for yourself.
[!NOTE] A QKID is actually a just a unique combination of entity, class and an instrument:
[ENTITY].[CLS].[INSTRUMENT]
The entity is most often the CIK, the instrument is an OpenFIGI FIGI, and the class is something that helps to quickly identify what this instrument is. Pretty simple, but unlike most everything you might have seen in a security master.
Alibaba will serve as an example of what this looks like in practice
BABA # ALIBABA GROUP HOLDING-SP ADR # 0001577552.0400.006G2JWB1
- [ENTITY] the 10 digit CIK assigned by the SEC to any entity that files in the US. Even foreign firms need one at times.
- [CLS] is the classification of the instrument. In this case the 0400 is an ADR tradable in the US. 0000 is used for the common equity - often Class A Ordinary, and there are many others available
- [INSTRUMENT] is the variable portion of the FIGI from OpenFIGI, which is quickly becoming the de-facto identifier - covering a billion+ instruments
For entities that do not have instruments (e.g. a hedge fund or a CEO), we create a unique instrument part to allow for consistency (e.g. E0000Y7E8 like in Jane Street's case above).
Get some data.
QK's job is to let you do your job. Mapped, point-in-time, and even auditable - all from within your preferred platform - is how we make that happen. To get a feel for this, we'll take a look at two foundational areas - Ownership and Fundamentals:
- Universe
- Institutional Ownership
- Insider Ownership
- Activist Ownership
- Fundamentals
Let's get started
Start with a Universe
In all institutional settings, you are almost always working with a universe. This is nothing more than a collection of firms or instruments that are part of a strategy. These are often defined by a set of rules - e.g. minimum market cap and price, some limits on minimum daily volume, etc.
To get you started, we have created a set of universe definitions to showcase the design and data behind QUANTkiosk. We'll elaborate on methodologies at another time, but these represent the most widely held companies amongst institutional investors. From the top 100 to 3000 firms (QK100, QK1000, QK3000, with QK2000 being those firms less widley held than the top 1000)
qk100 = qk.univ("QK100")
qk100[:5]
qkid: ['0000001800.0000.001S5N9M6',
'0000002488.0000.001S5NN36',
'0000004962.0000.001S5P034',
'0000006951.0000.001S5NMM7',
'0000008670.0000.001S82KF6']
qk100[:5].to_ticker()
qk100[:5].to_name()
qk100[:5].to_cik()
# ticker
['ABT', 'AMD', 'AXP', 'AMAT', 'ADP']
# name
['ABBOTT LABORATORIES',
'ADVANCED MICRO DEVICES',
'AMERICAN EXPRESS CO',
'APPLIED MATERIALS INC',
'AUTOMATIC DATA PROCESSING']
# cik
['1800', '2488', '4962', '6951', '8670']
Of course, not everything you might want to request is a tradable entity. In fact, the owners of the above universe are private firms in many cases.
Institutional Ownership
What they call "smart money". Certain asset managers (those with over $100mm in reportable assets) are obliged to disclose their positions at the end of each quarter. The qkiosk package includes two
examples - deshaw and sgcap, both which provide good documentation on what the structure of the data is. For our purposes, lets just request some data to see how easy it is to get a picture of what held last quarter.
First, well get the manager that became particularly popular in the pandemic. The are a very large multistrat player, who reports for both the asset manager business and the securities business. To get a good feel for what they are doing for the investing side, it is very useful to be able to see submanager details, which QK does very well.
citadel = qk.search_mgr("citadel")
# 1: CITADEL ADVISORS LLC
# 2: CITADEL INVESTMENT ADVISORY INC.
# 3: CIT BANK NA WEALTH MANAGEMENT
# 4: CITY STATE BANK
# 5: CIC WEALTH LLC
# 6: CITIZENS FINANCIAL GROUP INC/RI
# 7: CITY CENTER ADVISORS LLC
# 8: CITY HOLDING CO
# 9: CITIZENS NATIONAL BANK TRUST DEPARTMENT
# 10: CI PRIVATE WEALTH LLC
# Selection: 1
citadel
# CITADEL ADVISORS LLC
# 0001423053.0000.E0000UI19
Now that we have our QKID, we can use it in the function called qk.institutional. Interface matters a lot to us. If you don't notice how easy it is to use, it is because we've done our job.
ca_202402_202501 = qk.institutional(citadel, yyyyqq=202501, qtrs=4, agg=False)
# fetching 0001423053 for 202501 ...done.
# fetching 0001423053 for 202404 ...done.
# fetching 0001423053 for 202403 ...done.
# fetching 0001423053 for 202402 ...done.
It's easy to see what Citadel Securities (otherManager==1 in this case) is holding, how much it has changed, and even see positions they no longer have.
# first set display width to size of terminal if needed
pd.set_option('display.width',0)
# force pandas to render all columns - you can also set this globally in Pandas
with pd.option_context('display.max_columns', None):
print(ca[ca["otherManager"]==1])
filerName filing submissionType reportPeriod filedDate inclMgrs issuer titleOfClass issuerSIC issuerSector \
1 CITADEL ADVISORS LLC edgar/data/1423053/0000950123-25-005687.txt 13F-HR 20250331 20250515 1 1 800 FLOWERS COM INC CL A 5990.0 CD
2 CITADEL ADVISORS LLC edgar/data/1423053/0000950123-25-005687.txt 13F-HR 20250331 20250515 1 1 800 FLOWERS COM INC CL A 5990.0 CD
3 CITADEL ADVISORS LLC edgar/data/1423053/0000950123-25-005687.txt 13F-HR 20250331 20250515 1 1 800 FLOWERS COM INC CL A 5990.0 CD
5 CITADEL ADVISORS LLC edgar/data/1423053/0000950123-25-005687.txt 13F-HR 20250331 20250515 1 10X GENOMICS INC CL A COM 3826.0 HC
6 CITADEL ADVISORS LLC edgar/data/1423053/0000950123-25-005687.txt 13F-HR 20250331 20250515 1 10X GENOMICS INC CL A COM 3826.0 HC
... ... ... ... ... ... ... ... ... ... ...
20300 CITADEL ADVISORS LLC edgar/data/1423053/0000950123-24-008735.txt 13F-HR 20240630 20240814 1 ZYMEWORKS INC COM 2834.0 HC
20301 CITADEL ADVISORS LLC edgar/data/1423053/0000950123-24-008735.txt 13F-HR 20240630 20240814 1 ZYMEWORKS INC COM 2834.0 HC
20302 CITADEL ADVISORS LLC edgar/data/1423053/0000950123-24-008735.txt 13F-HR 20240630 20240814 1 ZYNEX INC COM 3845.0 HC
20303 CITADEL ADVISORS LLC edgar/data/1423053/0000950123-24-008735.txt 13F-HR 20240630 20240814 1 ZYNEX INC COM 3845.0 HC
20304 CITADEL ADVISORS LLC edgar/data/1423053/0000950123-24-008735.txt 13F-HR 20240630 20240814 1 ZYNEX INC COM 3845.0 HC
issuerTicker issuerQkid value shrsOrPrnAmt putCall shrsOrPrnAmtType invDiscretion votingAuthSole votingAuthShared votingAuthNone portWgt hasOtherManager \
1 FLWS 0001084869.0000.001S60YV4 0 0 NaN SH DFND 0 0 0 0.0 True
2 FLWS 0001084869.000C.001S60YV4 487340 82600 CALL SH DFND 82600 0 0 9.2e-07 True
3 FLWS 0001084869.000P.001S60YV4 274350 46500 PUT SH DFND 46500 0 0 5.2e-07 True
5 TXG 0001770787.0000.007WX14Y9 703961 80637 NaN SH DFND 80637 0 0 1.33e-06 True
6 TXG 0001770787.000C.007WX14Y9 1250136 143200 CALL SH DFND 143200 0 0 2.37e-06 True
... ... ... ... ... ... ... ... ... ... ... ... ...
20300 ZYME 0001937653.000C.019XSYC98 114034 13400 CALL SH DFND 13400 0 0 2.3e-07 True
20301 ZYME 0001937653.000P.019XSYC98 71484 8400 PUT SH DFND 8400 0 0 1.4e-07 True
20302 ZYXIQ 0000846475.0000.001S7T7V0 0 0 NaN SH DFND 0 0 0 0.0 True
20303 ZYXIQ 0000846475.000C.001S7T7V0 0 0 CALL SH DFND 0 0 0 0.0 True
20304 ZYXIQ 0000846475.000P.001S7T7V0 0 0 PUT SH DFND 0 0 0 0.0 True
otherManager otherManagerName otherManagerFileNumber QtrsHeld QOQSshPrnAmt QOQValue QOQPortWgt newOrDel
1 1.0 Citadel Securities GP LLC 28-18870 2 -73419 -599833 -1.04e-06 DEL
2 1.0 Citadel Securities GP LLC 28-18870 23 37500 118873 2.9e-07 NaN
3 1.0 Citadel Securities GP LLC 28-18870 20 24400 93793 2.1e-07 NaN
5 1.0 Citadel Securities GP LLC 28-18870 8 -230384 -3762301 -6.39e-06 NaN
6 1.0 Citadel Securities GP LLC 28-18870 22 102300 662812 1.35e-06 NaN
... ... ... ... ... ... ... ... ...
20300 1.0 Citadel Securities GP LLC 28-18870 19 -7900 -110042 -2e-07 NaN
20301 1.0 Citadel Securities GP LLC 28-18870 7 -2700 -45288 -8e-08 NaN
20302 1.0 Citadel Securities GP LLC 28-18870 3 -89709 -1109700 -2.14e-06 DEL
20303 1.0 Citadel Securities GP LLC 28-18870 1 -1800 -22266 -4e-08 DEL
20304 1.0 Citadel Securities GP LLC 28-18870 4 -200 -2474 -0.0 DEL
[62433 rows x 30 columns]
There are of course, a million ways to use this data - and we promise to share videos as well as deep dives as we move forward. We also would love to have anyone using the API share how they are discovering insights into what the "Who's Who of Wall Street" are up to each quarter.
We can also explore details of company holders, large shareholders, and even corporate insiders with similar speed and ease.
# all holders of NVDA in 2022 Q1
nvda_holders = qk.holders(qk.ticker("NVDA"), yyyyqq=202201)
# NVDA insiders - (e.g. CEO, Sr.EVP, ...)
nvda_insiders = qk.insider(qk.ticker("NVDA"), yyyyqq=202201)
# Large NVDA block holders for 2022 (above 5%)
nvda_lg = qk.beneficial(qk.ticker("NVDA"), yyyyqq=202200)
Fundamentals
Fundamentals data is the lifeblood of a company. While prices are observable, financials are much harder to source consistently or correctly. We source directly from filings, in a way only people who have used this data for investment at scale know how to do.
Let's dig into a quick example to show off what you can do with a few lines of code.
# There are hundreds of line items that are generally used - though 10s of 1000s of GAAP items are available.
#
# To see common ones we currently map you can use our reference function qk.fncodes
qk.fncodes()
stmt QKCODE label
0 BS CASH Cash and Cash Equivalents
1 BS AOC Assets - Other Current
2 BS ATC Assets - Current
3 BS AOCI Accumulated Other Net Income
4 BS AONC Other Net Assets Including Intangibles
.. ... ... ...
169 IS STKDP Dividends Paid - Per Common Share
170 IS XO Total Operating Expenses
171 IS EPSDDO Diluted Earnings Loss Per Share - Discontinued...
172 IS EPSDO Basic Earnings Loss Per Share - Discontinued O...
173 IS NIDO Net Loss Income - Discontinued Operations
[174 rows x 3 columns]
From here, lets find Net Income (code NI) for UBER
uber_ni = qk.fn(qk.ticker("UBER"), "NI")
uber_ni.to_df().tail()
cik acceptance_time stmt item filed fpb fpe fqd fp fqtr cyqtr fq fytd ttm ann rstmt
28 1543151 20250214160634 IS NI 20250214 20240101 20241231 92.0 FY 4 20250331 6883000000.0 9856000000.0 9856000000.0 9856000000.0 0
29 1543151 20250507160717 IS NI 20250507 20250101 20250331 90.0 Q1 1 20250630 1776000000.0 1776000000.0 12286000000.0 NaN 0
30 1543151 20250806160750 IS NI 20250806 20250401 20250630 91.0 Q2 2 20250930 1355000000.0 3131000000.0 10196000000.0 NaN 0
31 1543151 20251104160441 IS NI 20251104 20250701 20250930 92.0 Q3 3 20251231 6626000000.0 9757000000.0 13870000000.0 NaN 0
32 1543151 20260213160817 IS NI 20260213 20250101 20251231 92.0 FY 4 20260331 296000000.0 10053000000.0 10053000000.0 10053000000.0 0
You can also easily see where this data comes from using our state of the art auditing tools
# show all 40 columns
qk.fn(qk.ticker("UBER"), "NI").full().to_df().tail()
## Python Support COMING SOON!
#qk.fn(qk.ticker("UBER"), "NI").to_df().tail().highlight(5).qk_audit()
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