Fundamental Data: Periodic indicators & Instant indicators

johback

Hallo,

i wonder how to get Fundamental Data for a Stock.
at:
https://quantiacs.com/documentation/en/user_guide/data_stocks.html?highlight=instant indicators#fundamental-data
there are two tables with keys to retrieve Fundamental-Data but i dont understand how to use them. I can not find examples.

Best Regards.

support

@johback Hi, these features are experimental. You can adapt the following test code:

https://github.com/quantiacs/toolbox/blob/dev/qnt/tests/test_fundamental_data.py

or use:

import qnt.ta as qnta
import qnt.data as qndata
import qnt.output as qnout
import qnt.stats as qns

assets = qndata.stocks.load_ndx_list(tail=dt.timedelta(days=5*365))

data = qndata.stocks.load_ndx_data(tail=dt.timedelta(days=5*365),
                        dims=("time", "field", "asset"),
                        assets=assets_names,
                        forward_order=True)

johback

@support Thank you!

Vyacheslav_B

@johback

Hello

More examples are here https://github.com/quantiacs/toolbox/blob/main/qnt/tests/test_fundamental_data.py

This is a simple example.

import qnt.data as qndata
import datetime as dt
import qnt.data.secgov_indicators
import qnt.data as qndata
import qnt.stats as qns

assets = qndata.stocks.load_ndx_list(tail=dt.timedelta(days=5 * 365))
assets_names = [i["id"] for i in assets]
data = qndata.stocks.load_ndx_data(tail=dt.timedelta(days=5 * 365),
                                   dims=("time", "field", "asset"),
                                   assets=assets_names,
                                   forward_order=True)

facts_names = ['operating_expense']  # 'assets', 'liabilities', 'ivestment_short_term' and other

fundamental_data = qnt.data.secgov_load_indicators(assets,
                                                   time_coord=data.time,
                                                   standard_indicators=facts_names)

# Operating expenses include marketing, noncapitalized R&D,
# travel and entertainment, office supply, rent, salary, cogs...
weights = fundamental_data.sel(field='operating_expense')
is_liquid = data.sel(field="is_liquid")
weights = weights * is_liquid

# calc stats
stats = qns.calc_stat(data, weights.sel(time=slice("2006-01-01", None)))
display(stats.to_pandas().tail())

# graph
performance = stats.to_pandas()["equity"]
import qnt.graph as qngraph

qngraph.make_plot_filled(performance.index, performance, name="PnL (Equity)", type="log")