IB Fundamental

Interactive Brokers Fundamental data for humans.

This package will bring all fundamental data available through IBKR TWS API into ready-to-use pandas data frames.

Installation

You can install ib_fundamental using pip

pip install ib-fundamental

Usage

You will need a TWS API port available

import ib_async
from ib_fundamental import CompanyFinancials
ib_async.util.startLoop() # if you are in a notebook
connect to TWS API for ex on localhost:7497
ib = ib_async.IB().connect('localhost',7497)
create your company financials instance
aapl = CompanyFinancials(ib=ib,symbol="AAPL")
or specify exchange and currency
aapl = CompanyFinancials(ib=ib,symbol="AAPL",exchange='SMART',currency='USD)
get company info
aapl.company_information
                        0
ticker               AAPL
company_name    Apple Inc
cik            0000320193
exchange_code        NASD
exchange           NASDAQ
irs             942404110
Annual income statement, while aapl.income_quarter will pull the quarterly report
aapl.income_annual
mapitem  2018-09-29  2019-09-28  2020-09-26  2021-09-25  2022-09-24  2023-09-30    statementtype line_id 0                                        period      Annual      Annual      Annual      Annual      Annual      Annual  Income Statement 1                                      end_date  2018-09-29  2019-09-28  2020-09-26  2021-09-25  2022-09-24  2023-09-30  Income Statement 2                                   fiscal_year        2018        2019        2020        2021        2022        2023  Income Statement 4                                      date_10Q                                                                          Income Statement 5                                      date_10K  2018-11-05  2019-10-31  2020-10-30  2021-10-29  2022-10-28  2023-11-03  Income Statement 100                                     Revenue    265595.0    260174.0    274515.0    365817.0    394328.0    383285.0  Income Statement 310                               Total Revenue    265595.0    260174.0    274515.0    365817.0    394328.0    383285.0  Income Statement 360                      Cost of Revenue, Total    163756.0    161782.0    169559.0    212981.0    223546.0    214137.0  Income Statement 370                                Gross Profit    101839.0     98392.0    104956.0    152836.0    170782.0    169148.0  Income Statement 550      Selling/General/Admin. Expenses, Total     16705.0     18245.0     19916.0     21973.0     25094.0     24932.0  Income Statement 560                      Research & Development     14236.0     16217.0     18752.0     21914.0     26251.0     29915.0  Income Statement 830                     Total Operating Expense    194697.0    196244.0    208227.0    256868.0    274891.0    268984.0  Income Statement 840                            Operating Income     70898.0     63930.0     66288.0    108949.0    119437.0    114301.0  Income Statement 911      Interest Inc.(Exp.),Net-Non-Op., Total      2446.0      1385.0       890.0       198.0      -106.0      -183.0  Income Statement 1270                                 Other, Net      -441.0       422.0       -87.0        60.0      -228.0      -382.0  Income Statement 1280                    Net Income Before Taxes     72903.0     65737.0     67091.0    109207.0    119103.0    113736.0  Income Statement 1290                 Provision for Income Taxes     11872.0     10481.0      9680.0     14527.0     19300.0     16741.0  Income Statement 1300                     Net Income After Taxes     61031.0     55256.0     57411.0     94680.0     99803.0     96995.0  Income Statement 1340             Net Income Before Extra. Items     61031.0     55256.0     57411.0     94680.0     99803.0     96995.0  Income Statement 1400                                 Net Income     59531.0     55256.0     57411.0     94680.0     99803.0     96995.0  Income Statement 1470      Income Available to Com Excl ExtraOrd     61031.0     55256.0     57411.0     94680.0     99803.0     96995.0  Income Statement 1480      Income Available to Com Incl ExtraOrd     59531.0     55256.0     57411.0     94680.0     99803.0     96995.0  Income Statement 1530                         Diluted Net Income     59531.0     55256.0     57411.0     94680.0     99803.0     96995.0  Income Statement 1540            Diluted Weighted Average Shares   20000.436   18595.652   17528.214   16864.919   16325.819   15812.547  Income Statement 1550       Diluted EPS Excluding ExtraOrd Items     3.05148     2.97145     3.27535     5.61402      6.1132     6.13405  Income Statement 1570           DPS - Common Stock Primary Issue        0.68        0.75       0.795        0.85         0.9        0.94  Income Statement 1770                     Diluted Normalized EPS     3.05148     2.97145     3.27535     5.61402      6.1132     6.13405  Income Statement
get earnings per share, appl.eps_ttm will pull trailing twelve months eps
aapl.eps_ttm
report_type period     eps asofdate 2017-06-30         TTM    12M   8.870 2017-09-30         TTM    12M   9.270 2017-12-31         TTM    12M   9.810 2018-03-31         TTM    12M  10.460 2018-06-30         TTM    12M  11.160 2018-09-30         TTM    12M  12.010 2018-12-31         TTM    12M  12.310 2019-03-31         TTM    12M  12.020 2019-06-30         TTM    12M  11.860 2019-09-30         TTM    12M  11.970 2019-12-31         TTM    12M  12.790 2020-03-31         TTM    12M  12.900 2020-06-30         TTM    12M   3.325 2020-09-30         TTM    12M   3.310 2020-12-31         TTM    12M   3.750 2021-03-31         TTM    12M   4.510 2021-06-30         TTM    12M   5.170 2021-09-30         TTM    12M   5.670 2021-12-31         TTM    12M   6.080 2022-03-31         TTM    12M   6.210 2022-06-30         TTM    12M   6.110 2022-09-30         TTM    12M   6.150 2022-12-31         TTM    12M   5.930 2023-03-31         TTM    12M   5.920 2023-06-30         TTM    12M   5.980 2023-09-30         TTM    12M   6.160 2023-12-31         TTM    12M   6.460 2024-03-31         TTM    12M   6.460
get data in json format
from ibfundamental.utils import tojson
CompanyFinancials.data property contains all data in dataclass format
tojson(aapl.data.epsttm)
'[{"asofdate": "2024-03-31T00:00:00", "reporttype": "TTM", "period": "12M", "eps": 6.46}, {"asofdate": "2023-12-31T00:00:00", "reporttype": "TTM", "period": "12M", "eps": 6.46}, ...'
and much more

What fundamental data is available?

ib_fundamental is a wrapper around IBKR TWS API. It will connect to a running TWS or ibgateway instance and request fundamental data through [reqFundamentalData][reqFundamental] method and ticker 258. TWS API will return a set of XML files with all the fundamental data. ib_fundamental will parse and transform all those XMLs into python dataclasses and pandas data frames.

Available data includes:

Financial Statements

- Balance sheet, annual/quarter - Income Statement, annual/quarter - Cash flow, annual/quarter

Financial ratios

- earnings per share, quarterly and trailing twelve months (ttm) - revenue, quarterly and trailing twelve months(ttm) - dividends and dividends per share, quarterly and trailing twelve months(ttm) - financial ratios like ROE, ROC, EV, BVPS, CSHPS, etc - full list of [financial ratios][fin_ratios] - analyst forecast - forward year estimates and actuals

Company ownership

This is the full list of methods of CompanyFinancials class

analyst_forecast
balance_annual
balance_quarter
cashflow_annual
cashflow_quarter
company_information
dividends
dividendspsq
dividendspsttm
eps_q
eps_ttm
fundamental_ratios
fy_actuals
fy_estimates
income_annual
income_quarter
ownership
ratios
revenue_q
revenue_tt

You can use FundamentalData class that will return company fundamental information in dataclass format. Each instance of CompanyFinancials contains an instance of FundamentalData in its data property.

from ib_fundamental.fundamental import FundamentalData
[m for m in dir(FundamentalData) if m[:1] != ""]
['analyst_forecast',  'balance_annual',  'balance_quarter',  'cashflow_annual',  'cashflow_quarter',  'company_info',  'divpsq',  'divpsttm',  'dividend',  'dividend_summary',  'eps_q',  'eps_ttm',  'fundamental_ratios',  'fy_actuals',  'fy_estimates',  'income_annual',  'income_quarter',  'ownership_report',  'ratios',  'revenue_q',  'revenue_ttm']
get quarterly revenue
aapl.data.revenue_q
[Revenue(asofdate=datetime.datetime(2024, 3, 31, 0, 0), report_type='R', period='3M', revenue=90753000000.0),  Revenue(asofdate=datetime.datetime(2023, 12, 31, 0, 0), report_type='R', period='3M', revenue=119575000000.0),  Revenue(asofdate=datetime.datetime(2023, 9, 30, 0, 0), report_type='R', period='3M', revenue=89498000000.0),  Revenue(asofdate=datetime.datetime(2023, 6, 30, 0, 0), report_type='R', period='3M', revenue=81797000000.0),  Revenue(asofdate=datetime.datetime(2023, 3, 31, 0, 0), report_type='R', period='3M', revenue=94836000000.0),  Revenue(asofdate=datetime.datetime(2022, 12, 31, 0, 0), report_type='R', period='3M', revenue=117154000000.0),  Revenue(asofdate=datetime.datetime(2022, 9, 30, 0, 0), report_type='R', period='3M', revenue=90146000000.0),  Revenue(asofdate=datetime.datetime(2022, 6, 30, 0, 0), report_type='R', period='3M', revenue=82959000000.0),  Revenue(asofdate=datetime.datetime(2022, 3, 31, 0, 0), report_type='R', period='3M', revenue=97278000000.0),  Revenue(asofdate=datetime.datetime(2021, 12, 31, 0, 0), report_type='R', period='3M', revenue=123945000000.0),  Revenue(asofdate=datetime.datetime(2021, 9, 30, 0, 0), report_type='R', period='3M', revenue=83360000000.0),  Revenue(asofdate=datetime.datetime(2021, 6, 30, 0, 0), report_type='R', period='3M', revenue=81434000000.0),  Revenue(asofdate=datetime.datetime(2021, 3, 31, 0, 0), report_type='R', period='3M', revenue=89584000000.0),  Revenue(asofdate=datetime.datetime(2020, 12, 31, 0, 0), report_type='R', period='3M', revenue=111439000000.0),  Revenue(asofdate=datetime.datetime(2020, 9, 30, 0, 0), report_type='R', period='3M', revenue=64698000000.0),  Revenue(asofdate=datetime.datetime(2020, 6, 30, 0, 0), report_type='R', period='3M', revenue=59685000000.0),  Revenue(asofdate=datetime.datetime(2020, 3, 31, 0, 0), report_type='R', period='3M', revenue=58313000000.0),  Revenue(asofdate=datetime.datetime(2019, 12, 31, 0, 0), report_type='R', period='3M', revenue=91819000000.0),  Revenue(asofdate=datetime.datetime(2019, 9, 30, 0, 0), report_type='R', period='3M', revenue=64040000000.0),  Revenue(asofdate=datetime.datetime(2019, 6, 30, 0, 0), report_type='R', period='3M', revenue=53809000000.0),  Revenue(asofdate=datetime.datetime(2019, 3, 31, 0, 0), report_type='R', period='3M', revenue=58015000000.0),  Revenue(asofdate=datetime.datetime(2018, 12, 31, 0, 0), report_type='R', period='3M', revenue=84310000000.0),  Revenue(asofdate=datetime.datetime(2018, 9, 30, 0, 0), report_type='R', period='3M', revenue=62900000000.0),  Revenue(asofdate=datetime.datetime(2018, 6, 30, 0, 0), report_type='R', period='3M', revenue=53265000000.0),  Revenue(asofdate=datetime.datetime(2018, 3, 31, 0, 0), report_type='R', period='3M', revenue=61137000000.0),  Revenue(asofdate=datetime.datetime(2017, 12, 31, 0, 0), report_type='R', period='3M', revenue=88293000000.0),  Revenue(asofdate=datetime.datetime(2017, 9, 30, 0, 0), report_type='R', period='3M', revenue=52579000000.0),  Revenue(asofdate=datetime.datetime(2017, 6, 30, 0, 0), report_type='R', period='3M', revenue=45408000000.0)]


Contributing
If you find a bug please open an issue, pull requests are always welcome.

To develop with ib_fundamental` please follow these steps

git clone https://github.com/quantbelt/ib_fundamental.git
cd ib_fundamental
install development dependencies
pip install .[dev]
do your things
run linters and code quality checks
pre-commit
run tests with tox, requires pypy310,py{310,311,312}
tox

[reqFundamental]: https://ib-api-reloaded.github.io/ibasync/api.html#ibasync.ib.IB.reqFundamentalData [finratios]: http://web.archive.org/web/20200725010343/https://interactivebrokers.github.io/tws-api/fundamentalratios_tags.html

🔗 More in this category