Data loading

This section contains detailed API reference documentation. It is intended for people who are already familiar with the QuantNet platform. One may prefer to visit Getting started page for the first time.

Assets

Available financial instruments with brief information can be downloaded using the following function:

Function

qnt.data.load_assets(
        min_date: tp.Union[str, datetime.date] = '2007-01-01',
        max_date: tp.Union[str, datetime.date, None] = None,
        tail: tp.Union[datetime.timedelta, None] = None)

Parameters

Parameter	Explanation
min_date	first date in data
max_date	last date of data
tail	datetime.timedelta, tail size of data. min_date = max_date - tail

Output

The output is the list of dicts with info for all tickers. For instance, the dict for „IBIO INC“ looks as follows:

{'name': 'IBIO INC',
 'sector': 'Health Technology',
 'symbol': 'IBIO',
 'exchange': 'AMEX',
 'industry': 'Biotechnology',
 'id': 'AMEX:IBIO',
 'cik': '1420720',
 'FIGI': 'BBG000D5F2L9'}

Example

One can use it by setting the time interval:

import qnt.data as qndata          # data loading and manipulation
assets = qndata.load_assets(min_date = '2015-01-01', max_date = '2018-01-01') # two boundaries

# one boundary
# assets = qndata.load_assets(min_date = '2018-01-01')
# assets = qndata.load_assets(max_date = '2020-01-01')

Or load last several years information:

import qnt.data as qndata          # data loading and manipulation
assets = qndata.load_assets(tail = dt.timedelta(days=4*365))

Market data

Market data is mainly connected to stocks“ daily prices. This template shows how to download market data.

Function

qnt.data.load_data(
        assets: tp.List[tp.Union[dict,str]] = None,
        min_date: tp.Union[str, datetime.date] = '2007-01-01',
        max_date: tp.Union[str, datetime.date, None] = None,
        dims: tp.Tuple[str, str, str] = (ds.FIELD, ds.TIME, ds.ASSET),
        forward_order: bool = False,
        tail: tp.Union[datetime.timedelta, None] = None)

Parameters

Parameter	Explanation
assets	list of ticker names to load
min_date	first date in data
max_date	last date of data
dims	tuple with ds.FIELD, ds.TIME, ds.ASSET in the specified order
forward_order	boolean, set true if you need the forward order of dates, otherwise the order is backward
tail	datetime.timedelta, tail size of data. min_date = max_date - tail

Output

The output is xarray DataArray with historical data for selected assets.

asset time	NASDAQ:AAPL	NASDAQ:GOOGL
2016-09-09	2929.92	798.77
2016-09-12	2874.20	784.52
2016-09-13	3010.28	794.01
2016-09-14	3044.44	787.53
2016-09-15	3188.08	790.01

Example One can load market data for Apple Inc and Google Inc for the past 4 years:

import qnt.data as qndata          # data loading and manipulation
data = qnt.data.load_data(tail = dt.timedelta(days=4*365),
                        dims=("time", "field", "asset"),
                        assets=['NASDAQ:AAPL', 'NASDAQ:GOOGL'],
                        forward_order=True)

open_price = data.sel(field = 'open')
close_price = data.sel(field = 'close')
low_price = data.sel(field = 'low')
high_price = data.sel(field = 'high')

open_price.to_pandas().head()

asset time	NASDAQ:AAPL	NASDAQ:GOOGL
2016-09-09	2929.92	798.77
2016-09-12	2874.20	784.52
2016-09-13	3010.28	794.01
2016-09-14	3044.44	787.53
2016-09-15	3188.08	790.01

Fundamental data

This template shows how to download prepared fundamental data.

Function

qnt.data.secgov_load_indicators(assets, time_coord, standard_indicators=None, builders = None,
                           start_date_offset = datetime.timedelta(days=365*2),
                           fill_strategy=lambda xarr: xarr.ffill('time'))

Parameters

Parameter	Explanation
assets	the list of dicts with info for the desired tickers
time_coord	xarray DataArray with the time interval
standard_indicators	the list of standart fundamental indicators
builders	list of IndicatorBuilder or PeriodIndicatorBuilder classes. This parameter is designed for the custom uploading of the sec.gov facts
start_date_offset	datetime.timedelta, tail size of data. min_date = max_date - tail
fill_strategy	function, filling strategy

Output

The output is xarray DataArray with historical fundamental data.

Example

We have collected and processed a large amount of fundamental data for users. One can find the list of prepared data here. Below are two ways to download prepared data.

The first way is just to list the desired data labels.

data_lbls = ['assets', 'liabilities']
# One can load corresponding data
fun_data1 = qnt.data.secgov_load_indicators(assets,time_coord = data.time, standard_indicators = data_lbls)

The second way to load fundamental data is more complicated but gives user more options. Each report for the Securities and Exchange Commission contains facts that are listed here. One can make their builder that takes a name and a list of desired facts. Some indicators are instant and updated regularly within each report.

instant_data_list = [InstantIndicatorBuilder('assets' , ['us-gaap:Assets'], True),
                     InstantIndicatorBuilder('liabilities', ['us-gaap:Liabilities'], True),
                    InstantIndicatorBuilder('shares', ['us-gaap:CommonStockSharesOutstanding',
                                                       'us-gaap:CommonStockSharesIssued'], True)]

Others are periodical and correspond to a certain period. For example, operating expenses and sales. For periodical indicators, you can receive information with the quarter, annual frequency, or „last twelve month“ value. For these purposes put „qf“,“af“ or „ltm“ correspondingly:

period_data_list = [PeriodIndicatorBuilder('operating_expense', ['us-gaap:OperatingExpenses'], True, 'qf'),
                   PeriodIndicatorBuilder('sales_revenue', ['us-gaap:SalesRevenueGoodsNet',
                                                            'us-gaap:SalesRevenueNet',
                                                            'us-gaap:RevenueFromContractWithCustomerIncludingAssessedTax'
                                                           ], True, 'af'),
                    PeriodIndicatorBuilder('sga_expense', ['us-gaap:SellingGeneralAndAdministrativeExpense'], True, 'ltm')]