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.
You can install ib_fundamental using pip
pip install ib-fundamentalYou 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
map_item 2018-09-29 2019-09-28 2020-09-26 2021-09-25 2022-09-24 2023-09-30 statement_type
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
as_of_date
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 ib_fundamental.utils import to_json
# CompanyFinancials.data property contains all data in dataclass format
to_json(aapl.data.eps_ttm)
'[{"as_of_date": "2024-03-31T00:00:00", "report_type": "TTM", "period": "12M", "eps": 6.46}, {"as_of_date": "2023-12-31T00:00:00", "report_type": "TTM", "period": "12M", "eps": 6.46}, ...'
# and much moreib_fundamental is a wrapper around IBKR TWS API. It will connect to a running TWS or
ibgateway instance and request fundamental data through
reqFundamentalData 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
- 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
- dividends_ps_q
- dividends_ps_ttm
- 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',
'div_ps_q',
'div_ps_ttm',
'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(as_of_date=datetime.datetime(2024, 3, 31, 0, 0), report_type='R', period='3M', revenue=90753000000.0),
Revenue(as_of_date=datetime.datetime(2023, 12, 31, 0, 0), report_type='R', period='3M', revenue=119575000000.0),
Revenue(as_of_date=datetime.datetime(2023, 9, 30, 0, 0), report_type='R', period='3M', revenue=89498000000.0),
Revenue(as_of_date=datetime.datetime(2023, 6, 30, 0, 0), report_type='R', period='3M', revenue=81797000000.0),
Revenue(as_of_date=datetime.datetime(2023, 3, 31, 0, 0), report_type='R', period='3M', revenue=94836000000.0),
Revenue(as_of_date=datetime.datetime(2022, 12, 31, 0, 0), report_type='R', period='3M', revenue=117154000000.0),
Revenue(as_of_date=datetime.datetime(2022, 9, 30, 0, 0), report_type='R', period='3M', revenue=90146000000.0),
Revenue(as_of_date=datetime.datetime(2022, 6, 30, 0, 0), report_type='R', period='3M', revenue=82959000000.0),
Revenue(as_of_date=datetime.datetime(2022, 3, 31, 0, 0), report_type='R', period='3M', revenue=97278000000.0),
Revenue(as_of_date=datetime.datetime(2021, 12, 31, 0, 0), report_type='R', period='3M', revenue=123945000000.0),
Revenue(as_of_date=datetime.datetime(2021, 9, 30, 0, 0), report_type='R', period='3M', revenue=83360000000.0),
Revenue(as_of_date=datetime.datetime(2021, 6, 30, 0, 0), report_type='R', period='3M', revenue=81434000000.0),
Revenue(as_of_date=datetime.datetime(2021, 3, 31, 0, 0), report_type='R', period='3M', revenue=89584000000.0),
Revenue(as_of_date=datetime.datetime(2020, 12, 31, 0, 0), report_type='R', period='3M', revenue=111439000000.0),
Revenue(as_of_date=datetime.datetime(2020, 9, 30, 0, 0), report_type='R', period='3M', revenue=64698000000.0),
Revenue(as_of_date=datetime.datetime(2020, 6, 30, 0, 0), report_type='R', period='3M', revenue=59685000000.0),
Revenue(as_of_date=datetime.datetime(2020, 3, 31, 0, 0), report_type='R', period='3M', revenue=58313000000.0),
Revenue(as_of_date=datetime.datetime(2019, 12, 31, 0, 0), report_type='R', period='3M', revenue=91819000000.0),
Revenue(as_of_date=datetime.datetime(2019, 9, 30, 0, 0), report_type='R', period='3M', revenue=64040000000.0),
Revenue(as_of_date=datetime.datetime(2019, 6, 30, 0, 0), report_type='R', period='3M', revenue=53809000000.0),
Revenue(as_of_date=datetime.datetime(2019, 3, 31, 0, 0), report_type='R', period='3M', revenue=58015000000.0),
Revenue(as_of_date=datetime.datetime(2018, 12, 31, 0, 0), report_type='R', period='3M', revenue=84310000000.0),
Revenue(as_of_date=datetime.datetime(2018, 9, 30, 0, 0), report_type='R', period='3M', revenue=62900000000.0),
Revenue(as_of_date=datetime.datetime(2018, 6, 30, 0, 0), report_type='R', period='3M', revenue=53265000000.0),
Revenue(as_of_date=datetime.datetime(2018, 3, 31, 0, 0), report_type='R', period='3M', revenue=61137000000.0),
Revenue(as_of_date=datetime.datetime(2017, 12, 31, 0, 0), report_type='R', period='3M', revenue=88293000000.0),
Revenue(as_of_date=datetime.datetime(2017, 9, 30, 0, 0), report_type='R', period='3M', revenue=52579000000.0),
Revenue(as_of_date=datetime.datetime(2017, 6, 30, 0, 0), report_type='R', period='3M', revenue=45408000000.0)]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