publish package folder

This commit is contained in:
Diogo Sousa
2023-07-21 13:56:47 +01:00
parent 87a3fb2aad
commit b9044b3fc7
18 changed files with 6536 additions and 1 deletions

1
.gitignore vendored
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@@ -72,7 +72,6 @@ custom_imports/*/*.csv
# cache
cache/
openbb_sdk/sdk/core/openbb_core/app/static/package/
# lightning logs
lightning_logs/

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### THIS FILE IS AUTO-GENERATED. DO NOT EDIT. ###

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### THIS FILE IS AUTO-GENERATED. DO NOT EDIT. ###
import datetime
import typing
from typing import Literal, Optional, Union
from pydantic import validate_arguments
from openbb_core.app.model.command_output import CommandOutput
from openbb_core.app.static.container import Container
from openbb_core.app.static.filters import filter_call, filter_inputs, filter_output
class CLASS_crypto(Container):
@filter_call
@validate_arguments
def load(
self,
symbol: str,
start_date: Union[datetime.date, None, str] = None,
end_date: Union[datetime.date, None, str] = None,
chart: bool = False,
provider: Optional[Literal["fmp", "polygon"]] = None,
**kwargs
) -> CommandOutput[typing.List]:
"""Crypto Intraday Price.
Available providers: fmp, polygon
Standard
========
Parameter
---------
symbol : str
The symbol of the company.
start_date : Optional[date]
The start date of the stock data from which to retrieve the data.
end_date : Optional[date]
The end date of the stock data up to which to retrieve the data.
Returns
-------
date : datetime
The date of the stock.
open : PositiveFloat
The open price of the stock.
high : PositiveFloat
The high price of the stock.
low : PositiveFloat
The low price of the stock.
close : PositiveFloat
The close price of the stock.
adj_close : Optional[PositiveFloat]
The adjusted close price of the stock.
volume : PositiveFloat
The volume of the stock.
fmp
===
Source: https://site.financialmodelingprep.com/developer/docs/#Cryptocurrencies
Parameter
---------
timeseries : Optional[int]
The number of days to look back.
serietype : Optional[Literal["line"]]
The type of the series. Only "line" is supported.
Returns
-------
Documentation not available.
polygon
=======
Source: https://polygon.io/docs/crypto/get_v2_aggs_ticker__cryptoticker__range__multiplier___timespan___from___to
Parameters
----------
timespan : Timespan, optional
The timespan of the query, by default Timespan.day
sort : Literal["asc", "desc"], optional
The sort order of the query, by default "desc"
limit : PositiveInt, optional
The limit of the query, by default 49999
adjusted : bool, optional
Whether the query is adjusted, by default True
multiplier : PositiveInt, optional
The multiplier of the query, by default 1
Returns
-------
Documentation not available.
"""
inputs = filter_inputs(
provider_choices={
"provider": provider,
},
standard_params={
"symbol": symbol,
"start_date": start_date,
"end_date": end_date,
},
extra_params=kwargs,
chart=chart,
)
o = self._command_runner_session.run(
"/crypto/load",
**inputs,
).output
return filter_output(o)

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### THIS FILE IS AUTO-GENERATED. DO NOT EDIT. ###
import typing
from typing import Literal, Optional
from pydantic import validate_arguments
import openbb_core.app.model.command_context
import openbb_core.app.model.results.empty
from openbb_core.app.model.command_output import CommandOutput
from openbb_core.app.static.container import Container
from openbb_core.app.static.filters import filter_call, filter_inputs, filter_output
class CLASS_fixedincome(Container):
@filter_call
@validate_arguments
def treasury(
self,
start_date: Optional[str] = None,
end_date: Optional[str] = "2023-07-21",
chart: bool = False,
provider: Optional[Literal["fmp"]] = None,
**kwargs
) -> CommandOutput[typing.List]:
"""Get treasury rates.
Available providers: fmp,
Standard
========
Parameter
---------
start_date : Optional[str]
Start date of the data, default is None.
end_date : Optional[str]
End date of the data, default is today.
Returns
-------
date : dateType
The date of the treasury rates.
month_1 : float
The 1 month treasury rate.
month_2 : float
The 2 month treasury rate.
month_3 : float
The 3 month treasury rate.
month_6 : float
The 6 month treasury rate.
year_1 : float
The 1 year treasury rate.
year_2 : float
The 2 year treasury rate.
year_3 : float
The 3 year treasury rate.
year_5 : float
The 5 year treasury rate.
year_7 : float
The 7 year treasury rate.
year_10 : float
The 10 year treasury rate.
year_20 : float
The 20 year treasury rate.
year_30 : float
The 30 year treasury rate.
fmp
===
Source: https://site.financialmodelingprep.com/developer/docs/treasury-rates-api/
Parameter
---------
All fields are standardized.
Returns
-------
Documentation not available.
"""
inputs = filter_inputs(
provider_choices={
"provider": provider,
},
standard_params={
"start_date": start_date,
"end_date": end_date,
},
extra_params=kwargs,
chart=chart,
)
o = self._command_runner_session.run(
"/fixedincome/treasury",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def ycrv(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Yield curve."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/fixedincome/ycrv",
**inputs,
).output
return filter_output(o)

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### THIS FILE IS AUTO-GENERATED. DO NOT EDIT. ###
import typing
from typing import Literal, Optional
from pydantic import validate_arguments
from openbb_core.app.model.command_output import CommandOutput
from openbb_core.app.static.container import Container
from openbb_core.app.static.filters import filter_call, filter_inputs, filter_output
class CLASS_forex(Container):
@filter_call
@validate_arguments
def pairs(
self, chart: bool = False, provider: Optional[Literal["fmp"]] = None, **kwargs
) -> CommandOutput[typing.List]:
"""Forex Available Pairs.
Available providers: fmp,
Standard
========
Returns
-------
symbol : str
The symbol of the currency pair.
name : str
The name of the currency pair separated by '/'.
currency : str
The base currency of the currency pair.
stock_exchange : Optional[str]
The stock exchange of the currency pair.
exchange_short_name : Optional[str]
The short name of the stock exchange of the currency pair.
fmp
===
Source: https://site.financialmodelingprep.com/developer/docs/#Historical-Forex-Price
"""
inputs = filter_inputs(
provider_choices={
"provider": provider,
},
standard_params={},
extra_params=kwargs,
chart=chart,
)
o = self._command_runner_session.run(
"/forex/pairs",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def load(
self,
symbol: str,
chart: bool = False,
provider: Optional[Literal["fmp", "polygon"]] = None,
**kwargs
) -> CommandOutput[typing.List]:
"""Forex Intraday Price.
Available providers: fmp, polygon
Standard
========
Parameter
---------
symbol : str
The symbol of the forex currency pair.
Returns
-------
date : datetime
The date of the forex currency pair.
open : PositiveFloat
The open price of the forex currency pair.
high : PositiveFloat
The high price of the forex currency pair.
low : PositiveFloat
The low price of the forex currency pair.
close : PositiveFloat
The close price of the forex currency pair.
adj_close : Optional[PositiveFloat]
The adjusted close price of the forex currency pair.
fmp
===
Source: https://site.financialmodelingprep.com/developer/docs/#Historical-Forex-Price
Parameter
---------
All fields are standardized.
Returns
-------
Documentation not available.
polygon
=======
Source: https://polygon.io/docs/forex/get_v2_aggs_ticker__forexticker__range__multiplier___timespan___from___to
Parameters
----------
stocksTicker : str
The ticker symbol of the stocks to fetch.
start_date : Union[date, datetime], optional
The start date of the query.
end_date : Union[date, datetime], optional
The end date of the query.
timespan : Timespan, optional
The timespan of the query, by default Timespan.day
sort : Literal["asc", "desc"], optional
The sort order of the query, by default "desc"
limit : PositiveInt, optional
The limit of the query, by default 49999
adjusted : bool, optional
Whether the query is adjusted, by default True
multiplier : PositiveInt, optional
The multiplier of the query, by default 1
Returns
-------
Documentation not available.
"""
inputs = filter_inputs(
provider_choices={
"provider": provider,
},
standard_params={
"symbol": symbol,
},
extra_params=kwargs,
chart=chart,
)
o = self._command_runner_session.run(
"/forex/load",
**inputs,
).output
return filter_output(o)

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{
"crypto_load": "/crypto/load",
"root": "",
"crypto": "/crypto",
"economy_corecpi": "/economy/corecpi",
"economy": "/economy",
"economy_const": "/economy/const",
"economy_cpi": "/economy/cpi",
"economy_cpi_options": "/economy/cpi_options",
"economy_index": "/economy/index",
"economy_available_indices": "/economy/available_indices",
"economy_risk": "/economy/risk",
"economy_macro": "/economy/macro",
"economy_macro_countries": "/economy/macro_countries",
"economy_macro_parameters": "/economy/macro_parameters",
"economy_balance": "/economy/balance",
"economy_bigmac": "/economy/bigmac",
"economy_country_codes": "/economy/country_codes",
"economy_currencies": "/economy/currencies",
"economy_debt": "/economy/debt",
"economy_events": "/economy/events",
"economy_fgdp": "/economy/fgdp",
"economy_fred": "/economy/fred",
"economy_fred_search": "/economy/fred_search",
"economy_futures": "/economy/futures",
"economy_gdp": "/economy/gdp",
"economy_glbonds": "/economy/glbonds",
"economy_indices": "/economy/indices",
"economy_overview": "/economy/overview",
"economy_perfmap": "/economy/perfmap",
"economy_performance": "/economy/performance",
"economy_revenue": "/economy/revenue",
"economy_rgdp": "/economy/rgdp",
"economy_rtps": "/economy/rtps",
"economy_search_index": "/economy/search_index",
"economy_spending": "/economy/spending",
"economy_trust": "/economy/trust",
"economy_usbonds": "/economy/usbonds",
"economy_valuation": "/economy/valuation",
"fixedincome_treasury": "/fixedincome/treasury",
"fixedincome": "/fixedincome",
"fixedincome_ycrv": "/fixedincome/ycrv",
"forex_pairs": "/forex/pairs",
"forex": "/forex",
"forex_load": "/forex/load",
"news_globalnews": "/news/globalnews",
"news": "/news",
"news_sectornews": "/news/sectornews",
"stocks_fa_analysis": "/stocks/fa/analysis",
"stocks": "/stocks",
"stocks_fa": "/stocks/fa",
"stocks_fa_balance": "/stocks/fa/balance",
"stocks_fa_cal": "/stocks/fa/cal",
"stocks_fa_cash": "/stocks/fa/cash",
"stocks_fa_comp": "/stocks/fa/comp",
"stocks_fa_comsplit": "/stocks/fa/comsplit",
"stocks_fa_customer": "/stocks/fa/customer",
"stocks_fa_dcfc": "/stocks/fa/dcfc",
"stocks_fa_divs": "/stocks/fa/divs",
"stocks_fa_dupont": "/stocks/fa/dupont",
"stocks_fa_earning": "/stocks/fa/earning",
"stocks_fa_emp": "/stocks/fa/emp",
"stocks_fa_enterprise": "/stocks/fa/enterprise",
"stocks_fa_epsfc": "/stocks/fa/epsfc",
"stocks_fa_est": "/stocks/fa/est",
"stocks_fa_fama_coe": "/stocks/fa/fama_coe",
"stocks_fa_fama_raw": "/stocks/fa/fama_raw",
"stocks_fa_fraud": "/stocks/fa/fraud",
"stocks_fa_growth": "/stocks/fa/growth",
"stocks_fa_historical_5": "/stocks/fa/historical_5",
"stocks_fa_income": "/stocks/fa/income",
"stocks_fa_ins": "/stocks/fa/ins",
"stocks_fa_key": "/stocks/fa/key",
"stocks_fa_metrics": "/stocks/fa/metrics",
"stocks_fa_mgmt": "/stocks/fa/mgmt",
"stocks_fa_mktcap": "/stocks/fa/mktcap",
"stocks_fa_news": "/stocks/fa/news",
"stocks_fa_overview": "/stocks/fa/overview",
"stocks_fa_own": "/stocks/fa/own",
"stocks_fa_pt": "/stocks/fa/pt",
"stocks_fa_pta": "/stocks/fa/pta",
"stocks_fa_rating": "/stocks/fa/rating",
"stocks_fa_ratios": "/stocks/fa/ratios",
"stocks_fa_revfc": "/stocks/fa/revfc",
"stocks_fa_revgeo": "/stocks/fa/revgeo",
"stocks_fa_revseg": "/stocks/fa/revseg",
"stocks_fa_rot": "/stocks/fa/rot",
"stocks_fa_score": "/stocks/fa/score",
"stocks_fa_sec": "/stocks/fa/sec",
"stocks_fa_shares": "/stocks/fa/shares",
"stocks_fa_shrs": "/stocks/fa/shrs",
"stocks_fa_split": "/stocks/fa/split",
"stocks_fa_supplier": "/stocks/fa/supplier",
"stocks_fa_transcript": "/stocks/fa/transcript",
"stocks_ca_get": "/stocks/ca/get",
"stocks_ca": "/stocks/ca",
"stocks_ca_balance": "/stocks/ca/balance",
"stocks_ca_cashflow": "/stocks/ca/cashflow",
"stocks_ca_hcorr": "/stocks/ca/hcorr",
"stocks_ca_hist": "/stocks/ca/hist",
"stocks_ca_income": "/stocks/ca/income",
"stocks_ca_scorr": "/stocks/ca/scorr",
"stocks_ca_screener": "/stocks/ca/screener",
"stocks_ca_sentiment": "/stocks/ca/sentiment",
"stocks_ca_similar": "/stocks/ca/similar",
"stocks_ca_volume": "/stocks/ca/volume",
"stocks_dd_sec": "/stocks/dd/sec",
"stocks_dd": "/stocks/dd",
"stocks_dps_psi": "/stocks/dps/psi",
"stocks_dps": "/stocks/dps",
"stocks_dps_ctb": "/stocks/dps/ctb",
"stocks_dps_dpotc": "/stocks/dps/dpotc",
"stocks_dps_ftd": "/stocks/dps/ftd",
"stocks_dps_hsi": "/stocks/dps/hsi",
"stocks_dps_pos": "/stocks/dps/pos",
"stocks_dps_prom": "/stocks/dps/prom",
"stocks_dps_psi_q": "/stocks/dps/psi_q",
"stocks_dps_psi_sg": "/stocks/dps/psi_sg",
"stocks_dps_shorted": "/stocks/dps/shorted",
"stocks_dps_sidtc": "/stocks/dps/sidtc",
"stocks_dps_spos": "/stocks/dps/spos",
"stocks_disc_active": "/stocks/disc/active",
"stocks_disc": "/stocks/disc",
"stocks_disc_arkord": "/stocks/disc/arkord",
"stocks_disc_asc": "/stocks/disc/asc",
"stocks_disc_dividends": "/stocks/disc/dividends",
"stocks_disc_filings": "/stocks/disc/filings",
"stocks_disc_fipo": "/stocks/disc/fipo",
"stocks_disc_gainers": "/stocks/disc/gainers",
"stocks_disc_gtech": "/stocks/disc/gtech",
"stocks_disc_hotpenny": "/stocks/disc/hotpenny",
"stocks_disc_ipo": "/stocks/disc/ipo",
"stocks_disc_losers": "/stocks/disc/losers",
"stocks_disc_lowfloat": "/stocks/disc/lowfloat",
"stocks_disc_pipo": "/stocks/disc/pipo",
"stocks_disc_rtat": "/stocks/disc/rtat",
"stocks_disc_trending": "/stocks/disc/trending",
"stocks_disc_ugs": "/stocks/disc/ugs",
"stocks_disc_ulc": "/stocks/disc/ulc",
"stocks_disc_upcoming": "/stocks/disc/upcoming",
"stocks_gov_contracts": "/stocks/gov/contracts",
"stocks_gov": "/stocks/gov",
"stocks_gov_government_trading": "/stocks/gov/government_trading",
"stocks_gov_gtrades": "/stocks/gov/gtrades",
"stocks_gov_histcont": "/stocks/gov/histcont",
"stocks_gov_lastcontracts": "/stocks/gov/lastcontracts",
"stocks_gov_lasttrades": "/stocks/gov/lasttrades",
"stocks_gov_lobbying": "/stocks/gov/lobbying",
"stocks_gov_qtrcontracts": "/stocks/gov/qtrcontracts",
"stocks_gov_topbuys": "/stocks/gov/topbuys",
"stocks_gov_toplobbying": "/stocks/gov/toplobbying",
"stocks_gov_topsells": "/stocks/gov/topsells",
"stocks_ins_act": "/stocks/ins/act",
"stocks_ins": "/stocks/ins",
"stocks_ins_blcp": "/stocks/ins/blcp",
"stocks_ins_blcs": "/stocks/ins/blcs",
"stocks_ins_blip": "/stocks/ins/blip",
"stocks_ins_blis": "/stocks/ins/blis",
"stocks_ins_blop": "/stocks/ins/blop",
"stocks_ins_blos": "/stocks/ins/blos",
"stocks_ins_filt": "/stocks/ins/filt",
"stocks_ins_lcb": "/stocks/ins/lcb",
"stocks_ins_lins": "/stocks/ins/lins",
"stocks_ins_lip": "/stocks/ins/lip",
"stocks_ins_lis": "/stocks/ins/lis",
"stocks_ins_lit": "/stocks/ins/lit",
"stocks_ins_lpsb": "/stocks/ins/lpsb",
"stocks_ins_print_insider_data": "/stocks/ins/print_insider_data",
"stocks_ins_stats": "/stocks/ins/stats",
"stocks_options_chains": "/stocks/options/chains",
"stocks_options": "/stocks/options",
"stocks_options_dte": "/stocks/options/dte",
"stocks_options_eodchain": "/stocks/options/eodchain",
"stocks_options_expirations": "/stocks/options/expirations",
"stocks_options_grhist": "/stocks/options/grhist",
"stocks_options_hist": "/stocks/options/hist",
"stocks_options_info": "/stocks/options/info",
"stocks_options_last_price": "/stocks/options/last_price",
"stocks_options_oi": "/stocks/options/oi",
"stocks_options_pcr": "/stocks/options/pcr",
"stocks_options_price": "/stocks/options/price",
"stocks_options_unu": "/stocks/options/unu",
"stocks_options_voi": "/stocks/options/voi",
"stocks_options_vol": "/stocks/options/vol",
"stocks_options_vsurf": "/stocks/options/vsurf",
"stocks_load": "/stocks/load",
"stocks_news": "/stocks/news",
"stocks_multiples": "/stocks/multiples",
"stocks_tob": "/stocks/tob",
"stocks_quote": "/stocks/quote",
"stocks_search": "/stocks/search"
}

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### THIS FILE IS AUTO-GENERATED. DO NOT EDIT. ###
import typing
from typing import Literal, Optional
import pydantic
from pydantic import validate_arguments
import openbb_core.app.model.command_context
import openbb_core.app.model.results.empty
from openbb_core.app.model.command_output import CommandOutput
from openbb_core.app.static.container import Container
from openbb_core.app.static.filters import filter_call, filter_inputs, filter_output
class CLASS_news(Container):
@filter_call
@validate_arguments
def globalnews(
self,
page: pydantic.types.NonNegativeInt = 0,
chart: bool = False,
provider: Optional[Literal["benzinga", "fmp"]] = None,
**kwargs
) -> CommandOutput[typing.List]:
"""Global News.
Available providers: benzinga, fmp
Standard
========
Parameter
---------
page : NonNegativeInt
The page of the global news to be retrieved.
Returns
-------
date : datetime
The published date of the news.
title : str
The title of the news.
image : Optional[str]
The image URL of the news.
text : str
The text/body of the news.
url : str
The URL of the news.
benzinga
========
Source: https://docs.benzinga.io/benzinga/newsfeed-v2.html
Parameter
---------
pageSize : int (default: 15)
The number of results to return per page.
displayOutput : str (default: "headline")
The type of data to return. Options are "headline", "summary", "full", "all".
date : Optional[datetime]
The date of the news to retrieve.
dateFrom : Optional[datetime]
The start date of the news to retrieve.
dateTo : Optional[datetime]
The end date of the news to retrieve.
updatedSince : Optional[int]
The number of seconds since the news was updated.
publishedSince : Optional[int]
The number of seconds since the news was published.
sort : Optional[str]
The order in which to sort the news. Options are:
"published_at", "updated_at", "title", "author", "channel", "ticker", "topic", "content_type".
isin : Optional[str]
The ISIN of the news to retrieve.
cusip : Optional[str]
The CUSIP of the news to retrieve.
tickers : Optional[str]
The tickers of the news to retrieve.
channels : Optional[str]
The channels of the news to retrieve.
topics : Optional[str]
The topics of the news to retrieve.
authors : Optional[str]
The authors of the news to retrieve.
content_types : Optional[str]
The content types of the news to retrieve.
Returns
-------
Documentation not available.
fmp
===
Source: https://site.financialmodelingprep.com/developer/docs/general-news-api/
Parameter
---------
All fields are standardized.
Returns
-------
Documentation not available.
"""
inputs = filter_inputs(
provider_choices={
"provider": provider,
},
standard_params={
"page": page,
},
extra_params=kwargs,
chart=chart,
)
o = self._command_runner_session.run(
"/news/globalnews",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def sectornews(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Sector news."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/news/sectornews",
**inputs,
).output
return filter_output(o)

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### THIS FILE IS AUTO-GENERATED. DO NOT EDIT. ###
from openbb_core.app.static.container import Container
class Root(Container):
@property
def crypto(self): # route = "/crypto"
from openbb_core.app.static.package import crypto
return crypto.CLASS_crypto(command_runner_session=self._command_runner_session)
@property
def economy(self): # route = "/economy"
from openbb_core.app.static.package import economy
return economy.CLASS_economy(
command_runner_session=self._command_runner_session
)
@property
def fixedincome(self): # route = "/fixedincome"
from openbb_core.app.static.package import fixedincome
return fixedincome.CLASS_fixedincome(
command_runner_session=self._command_runner_session
)
@property
def forex(self): # route = "/forex"
from openbb_core.app.static.package import forex
return forex.CLASS_forex(command_runner_session=self._command_runner_session)
@property
def news(self): # route = "/news"
from openbb_core.app.static.package import news
return news.CLASS_news(command_runner_session=self._command_runner_session)
@property
def stocks(self): # route = "/stocks"
from openbb_core.app.static.package import stocks
return stocks.CLASS_stocks(command_runner_session=self._command_runner_session)

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### THIS FILE IS AUTO-GENERATED. DO NOT EDIT. ###
import datetime
import typing
from typing import Literal, Optional, Union
from pydantic import validate_arguments
import openbb_core.app.model.command_context
import openbb_core.app.model.results.empty
from openbb_core.app.model.command_output import CommandOutput
from openbb_core.app.static.container import Container
from openbb_core.app.static.filters import filter_call, filter_inputs, filter_output
class CLASS_stocks(Container):
@property
def fa(self): # route = "/stocks/fa"
from openbb_core.app.static.package import stocks_fa
return stocks_fa.CLASS_stocks_fa(
command_runner_session=self._command_runner_session
)
@property
def ca(self): # route = "/stocks/ca"
from openbb_core.app.static.package import stocks_ca
return stocks_ca.CLASS_stocks_ca(
command_runner_session=self._command_runner_session
)
@property
def dd(self): # route = "/stocks/dd"
from openbb_core.app.static.package import stocks_dd
return stocks_dd.CLASS_stocks_dd(
command_runner_session=self._command_runner_session
)
@property
def dps(self): # route = "/stocks/dps"
from openbb_core.app.static.package import stocks_dps
return stocks_dps.CLASS_stocks_dps(
command_runner_session=self._command_runner_session
)
@property
def disc(self): # route = "/stocks/disc"
from openbb_core.app.static.package import stocks_disc
return stocks_disc.CLASS_stocks_disc(
command_runner_session=self._command_runner_session
)
@property
def gov(self): # route = "/stocks/gov"
from openbb_core.app.static.package import stocks_gov
return stocks_gov.CLASS_stocks_gov(
command_runner_session=self._command_runner_session
)
@property
def ins(self): # route = "/stocks/ins"
from openbb_core.app.static.package import stocks_ins
return stocks_ins.CLASS_stocks_ins(
command_runner_session=self._command_runner_session
)
@property
def options(self): # route = "/stocks/options"
from openbb_core.app.static.package import stocks_options
return stocks_options.CLASS_stocks_options(
command_runner_session=self._command_runner_session
)
@filter_call
@validate_arguments
def load(
self,
symbol: str,
start_date: Union[datetime.date, None, str] = None,
end_date: Union[datetime.date, None, str] = None,
chart: bool = False,
provider: Optional[Literal["fmp", "polygon"]] = None,
**kwargs
) -> CommandOutput[typing.List]:
"""Load stock data for a specific ticker.
Available providers: fmp, polygon
Standard
========
Parameter
---------
symbol : str
The symbol of the company.
start_date : Optional[date]
The start date of the stock data from which to retrieve the data.
end_date : Optional[date]
The end date of the stock data up to which to retrieve the data.
Returns
-------
date : datetime
The date of the stock.
open : PositiveFloat
The open price of the stock.
high : PositiveFloat
The high price of the stock.
low : PositiveFloat
The low price of the stock.
close : PositiveFloat
The close price of the stock.
adj_close : Optional[PositiveFloat]
The adjusted close price of the stock.
volume : PositiveFloat
The volume of the stock.
fmp
===
Source: https://financialmodelingprep.com/developer/docs/#Stock-Historical-Price
Parameter
---------
timeseries : Optional[int]
The number of days to look back.
series_type : Optional[Literal["line"]]
The type of the series. Only "line" is supported.
Returns
-------
Documentation not available.
polygon
=======
Source: https://polygon.io/docs/stocks/getting-started
Parameters
----------
timespan : Timespan, optional
The timespan of the query, by default Timespan.day
sort : Literal["asc", "desc"], optional
The sort order of the query, by default "desc"
limit : PositiveInt, optional
The limit of the query, by default 49999
adjusted : bool, optional
Whether the query is adjusted, by default True
multiplier : PositiveInt, optional
The multiplier of the query, by default 1
Returns
-------
Documentation not available.
"""
inputs = filter_inputs(
provider_choices={
"provider": provider,
},
standard_params={
"symbol": symbol,
"start_date": start_date,
"end_date": end_date,
},
extra_params=kwargs,
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/load",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def news(
self,
symbols: str,
page: int = 0,
chart: bool = False,
provider: Optional[Literal["benzinga", "fmp", "polygon"]] = None,
**kwargs
) -> CommandOutput[typing.List]:
"""Get news for one or more stock tickers.
Available providers: benzinga, fmp, polygon
Standard
========
Parameter
---------
symbols : str
The symbols of the company.
page : int
The page of the stock news to be retrieved.
Returns
-------
date : date
The published date of the news.
title : str
The title of the news.
image : Optional[str]
The image URL of the news.
text : str
The text/body of the news.
url : str
The URL of the news.
benzinga
========
Source: https://docs.benzinga.io/benzinga/newsfeed-v2.html
Parameter
---------
symbols: str
The symbols of the companies.
pageSize : int (default: 15)
The number of results to return per page.
headline : str
The type of data to return. Options are "headline", "summary", "full", "all".
date : Optional[datetime]
The date of the news to retrieve.
dateFrom : Optional[datetime]
The start date of the news to retrieve.
dateTo : Optional[datetime]
The end date of the news to retrieve.
updatedSince : Optional[int]
The number of seconds since the news was updated.
publishedSince : Optional[int]
The number of seconds since the news was published.
sort : Optional[str]
The order in which to sort the news. Options are:
"published_at", "updated_at", "title", "author", "channel", "ticker", "topic", "content_type".
isin : Optional[str]
The ISIN of the news to retrieve.
cusip : Optional[str]
The CUSIP of the news to retrieve.
channels : Optional[str]
The channels of the news to retrieve.
topics : Optional[str]
The topics of the news to retrieve.
authors : Optional[str]
The authors of the news to retrieve.
content_types : Optional[str]
The content types of the news to retrieve.
fmp
===
Source: https://site.financialmodelingprep.com/developer/docs/stock-news-api/
Parameter
---------
limit : Optional[int]
The limit of the data to retrieve.
Returns
-------
Documentation not available.
polygon
=======
Source: https://polygon.io/docs/stocks/get_v2_reference_news
Parameters
----------
symbol : str
The symbol of the stocks to fetch.
ticker_lt : str, optional
Less than, by default None
ticker_lte : str, optional
Less than or equal, by default None
ticker_gt : str, optional
Greater than, by default None
ticker_gte : str, optional
Greater than or equal, by default None
published_utc : str, optional
The published date of the query, by default None
published_utc_lt : str, optional
Less than, by default None
published_utc_lte : str, optional
Less than or equal, by default None
published_utc_gt : str, optional
Greater than, by default None
published_utc_gte : str, optional
Greater than or equal, by default None
order : Literal["asc", "desc"], optional
The sort order of the query, by default None
limit : int, optional
The limit of the query, by default 100
sort : str, optional
The sort of the query, by default None
"""
inputs = filter_inputs(
provider_choices={
"provider": provider,
},
standard_params={
"symbols": symbols,
"page": page,
},
extra_params=kwargs,
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/news",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def multiples(
self,
symbol: str,
limit: Optional[int] = None,
chart: bool = False,
provider: Optional[Literal["fmp"]] = None,
**kwargs
) -> CommandOutput[typing.List]:
"""Get valuation multiples for a stock ticker.
Available providers: fmp,
Standard
========
Parameter
---------
symbol : str
The symbol of the company.
limit : Optional[int]
The limit of the key metrics ttm to be returned.
Returns
-------
revenue_per_share_ttm: Optional[float]
The revenue per share of the stock calculated as trailing twelve months.
net_income_per_share_ttm: Optional[float]
The net income per share of the stock calculated as trailing twelve months.
operating_cash_flow_per_share_ttm: Optional[float]
The operating cash flow per share of the stock calculated as trailing twelve months.
free_cash_flow_per_share_ttm: Optional[float]
The free cash flow per share of the stock calculated as trailing twelve months.
cash_per_share_ttm: Optional[float]
The cash per share of the stock calculated as trailing twelve months.
book_value_per_share_ttm: Optional[float]
The book value per share of the stock calculated as trailing twelve months.
tangible_book_value_per_share_ttm: Optional[float]
The tangible book value per share of the stock calculated as trailing twelve months.
shareholders_equity_per_share_ttm: Optional[float]
The shareholders equity per share of the stock calculated as trailing twelve months.
interest_debt_per_share_ttm: Optional[float]
The interest debt per share of the stock calculated as trailing twelve months.
market_cap_ttm: Optional[float]
The market cap of the stock calculated as trailing twelve months.
enterprise_value_ttm: Optional[float]
The enterprise value of the stock calculated as trailing twelve months.
pe_ratio_ttm: Optional[float]
The PE ratio of the stock calculated as trailing twelve months.
price_to_sales_ratio_ttm: Optional[float]
The price to sales ratio of the stock calculated as trailing twelve months.
pocf_ratio_ttm: Optional[float]
The POCF ratio of the stock calculated as trailing twelve months.
pfcf_ratio_ttm: Optional[float]
The PFCF ratio of the stock calculated as trailing twelve months.
pb_ratio_ttm: Optional[float]
The PB ratio of the stock calculated as trailing twelve months.
ptb_ratio_ttm: Optional[float]
The PTB ratio of the stock calculated as trailing twelve months.
ev_to_sales_ttm: Optional[float]
The EV to sales of the stock calculated as trailing twelve months.
enterprise_value_over_ebitda_ttm: Optional[float]
The enterprise value over EBITDA of the stock calculated as trailing twelve months.
ev_to_operating_cash_flow_ttm: Optional[float]
The EV to operating cash flow of the stock calculated as trailing twelve months.
ev_to_free_cash_flow_ttm: Optional[float]
The EV to free cash flow of the stock calculated as trailing twelve months.
earnings_yield_ttm: Optional[float]
The earnings yield of the stock calculated as trailing twelve months.
free_cash_flow_yield_ttm: Optional[float]
The free cash flow yield of the stock calculated as trailing twelve months.
debt_to_equity_ttm: Optional[float]
The debt to equity of the stock calculated as trailing twelve months.
debt_to_assets_ttm: Optional[float]
The debt to assets of the stock calculated as trailing twelve months.
net_debt_to_ebitda_ttm: Optional[float]
The net debt to EBITDA of the stock calculated as trailing twelve months.
current_ratio_ttm: Optional[float]
The current ratio of the stock calculated as trailing twelve months.
interest_coverage_ttm: Optional[float]
The interest coverage of the stock calculated as trailing twelve months.
income_quality_ttm: Optional[float]
The income quality of the stock calculated as trailing twelve months.
dividend_yield_ttm: Optional[float]
The dividend yield of the stock calculated as trailing twelve months.
payout_ratio_ttm: Optional[float]
The payout ratio of the stock calculated as trailing twelve months.
sales_general_and_administrative_to_revenue_ttm: Optional[float]
The sales general and administrative to revenue of the stock calculated as trailing twelve months.
research_and_development_to_revenue_ttm: Optional[float]
The research and development to revenue of the stock calculated as trailing twelve months.
intangibles_to_total_assets_ttm: Optional[float]
The intangibles to total assets of the stock calculated as trailing twelve months.
capex_to_operating_cash_flow_ttm: Optional[float]
The capex to operating cash flow of the stock calculated as trailing twelve months.
capex_to_revenue_ttm: Optional[float]
The capex to revenue of the stock calculated as trailing twelve months.
capex_to_depreciation_ttm: Optional[float]
The capex to depreciation of the stock calculated as trailing twelve months.
stock_based_compensation_to_revenue_ttm: Optional[float]
The stock based compensation to revenue of the stock calculated as trailing twelve months.
graham_number_ttm: Optional[float]
The graham number of the stock calculated as trailing twelve months.
roic_ttm: Optional[float]
The ROIC of the stock calculated as trailing twelve months.
return_on_tangible_assets_ttm: Optional[float]
The return on tangible assets of the stock calculated as trailing twelve months.
graham_net_net_ttm: Optional[float]
The graham net net of the stock calculated as trailing twelve months.
working_capital_ttm: Optional[float]
The working capital of the stock calculated as trailing twelve months.
tangible_asset_value_ttm: Optional[float]
The tangible asset value of the stock calculated as trailing twelve months.
net_current_asset_value_ttm: Optional[float]
The net current asset value of the stock calculated as trailing twelve months.
invested_capital_ttm: Optional[float]
The invested capital of the stock calculated as trailing twelve months.
average_receivables_ttm: Optional[float]
The average receivables of the stock calculated as trailing twelve months.
average_payables_ttm: Optional[float]
The average payables of the stock calculated as trailing twelve months.
average_inventory_ttm: Optional[float]
The average inventory of the stock calculated as trailing twelve months.
days_sales_outstanding_ttm: Optional[float]
The days sales outstanding of the stock calculated as trailing twelve months.
days_payables_outstanding_ttm: Optional[float]
The days payables outstanding of the stock calculated as trailing twelve months.
days_of_inventory_on_hand_ttm: Optional[float]
The days of inventory on hand of the stock calculated as trailing twelve months.
receivables_turnover_ttm: Optional[float]
The receivables turnover of the stock calculated as trailing twelve months.
payables_turnover_ttm: Optional[float]
The payables turnover of the stock calculated as trailing twelve months.
inventory_turnover_ttm: Optional[float]
The inventory turnover of the stock calculated as trailing twelve months.
roe_ttm: Optional[float]
The ROE of the stock calculated as trailing twelve months.
capex_per_share_ttm: Optional[float]
The capex per share of the stock calculated as trailing twelve months.
fmp
===
Source: https://site.financialmodelingprep.com/developer/docs/#Company-Key-Metrics
Parameter
---------
All fields are standardized.
"""
inputs = filter_inputs(
provider_choices={
"provider": provider,
},
standard_params={
"symbol": symbol,
"limit": limit,
},
extra_params=kwargs,
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/multiples",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def tob(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""View top of book for loaded ticker (US exchanges only)."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/tob",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def quote(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""View the current price for a specific stock ticker."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/quote",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def search(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Search a specific stock ticker for analysis."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/search",
**inputs,
).output
return filter_output(o)

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@@ -0,0 +1,240 @@
### THIS FILE IS AUTO-GENERATED. DO NOT EDIT. ###
import typing
from typing import Literal, Optional
from pydantic import validate_arguments
import openbb_core.app.model.command_context
import openbb_core.app.model.results.empty
from openbb_core.app.model.command_output import CommandOutput
from openbb_core.app.static.container import Container
from openbb_core.app.static.filters import filter_call, filter_inputs, filter_output
class CLASS_stocks_ca(Container):
@filter_call
@validate_arguments
def get(
self,
symbol: str,
chart: bool = False,
provider: Optional[Literal["fmp"]] = None,
**kwargs
) -> CommandOutput[typing.List]:
"""Company peers.
Available providers: fmp,
Standard
========
Parameter
---------
symbol: str
The symbol of the company.
Returns
-------
symbol : str
The symbol of the company to retrieve the stock peers of.
peers_list : List[str]
A list of stock peers based on sector, exchange and market cap
fmp
===
Source: https://site.financialmodelingprep.com/developer/docs/#Stock-Peers
Parameter
---------
All fields are standardized.
"""
inputs = filter_inputs(
provider_choices={
"provider": provider,
},
standard_params={
"symbol": symbol,
},
extra_params=kwargs,
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ca/get",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def balance(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Company balance sheet."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ca/balance",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def cashflow(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Company cashflow."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ca/cashflow",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def hcorr(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Company historical correlation."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ca/hcorr",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def hist(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Company historical prices."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ca/hist",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def income(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Company income statement."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ca/income",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def scorr(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Company sector correlation."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ca/scorr",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def screener(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Company screener."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ca/screener",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def sentiment(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Company sentiment."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ca/sentiment",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def similar(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Company similar."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ca/similar",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def volume(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Company volume."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ca/volume",
**inputs,
).output
return filter_output(o)

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@@ -0,0 +1,93 @@
### THIS FILE IS AUTO-GENERATED. DO NOT EDIT. ###
import typing
from typing import Literal, Optional
from pydantic import validate_arguments
from openbb_core.app.model.command_output import CommandOutput
from openbb_core.app.static.container import Container
from openbb_core.app.static.filters import filter_call, filter_inputs, filter_output
class CLASS_stocks_dd(Container):
@filter_call
@validate_arguments
def sec(
self,
symbol: str,
type: Optional[str] = None,
page: Optional[int] = 0,
limit: Optional[int] = None,
chart: bool = False,
provider: Optional[Literal["fmp"]] = None,
**kwargs
) -> CommandOutput[typing.List]:
"""SEC Filings.
Available providers: fmp,
Standard
========
Parameter
---------
symbol : str
The symbol of the company.
type : str
The type of the SEC filing form. (full list: https://www.sec.gov/forms)
page : int
The page of the results.
limit : int
The limit of the results.
Returns
-------
symbol : str
The symbol of the stock.
filling_date : date
The filling date of the SEC filing.
accepted_date : date
The accepted date of the SEC filing.
cik : str
The CIK of the SEC filing.
type : str
The type of the SEC filing.
link : str
The link of the SEC filing.
final_link : str
The final link of the SEC filing.
fmp
===
Source: https://site.financialmodelingprep.com/developer/docs/sec-filings-api/
Parameter
---------
The type of the SEC filing form. (full list: https://www.sec.gov/forms)
Returns
-------
Documentation not available.
"""
inputs = filter_inputs(
provider_choices={
"provider": provider,
},
standard_params={
"symbol": symbol,
"type": type,
"page": page,
"limit": limit,
},
extra_params=kwargs,
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/dd/sec",
**inputs,
).output
return filter_output(o)

View File

@@ -0,0 +1,315 @@
### THIS FILE IS AUTO-GENERATED. DO NOT EDIT. ###
from pydantic import validate_arguments
import openbb_core.app.model.results.empty
from openbb_core.app.model.command_output import CommandOutput
from openbb_core.app.static.container import Container
from openbb_core.app.static.filters import filter_call, filter_inputs, filter_output
class CLASS_stocks_disc(Container):
@filter_call
@validate_arguments
def active(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Most active stocks by intraday trade volumes."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/disc/active",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def arkord(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Order by ARK INvestment Management LLC."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/disc/arkord",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def asc(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Small cap stocks with revenue and earnings growth more than 25%."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/disc/asc",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def dividends(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/disc/dividends",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def filings(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""The most-recent form submissions to the SEC."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/disc/filings",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def fipo(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Future IPOs dates."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/disc/fipo",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def gainers(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Show latest top gainers."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/disc/gainers",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def gtech(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Tech stocks with revenue and earnings growth more than 25%."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/disc/gtech",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def hotpenny(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Today's hot penny stocks."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/disc/hotpenny",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def ipo(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/disc/ipo",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def losers(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Show latest top losers."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/disc/losers",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def lowfloat(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Low float stocks under 10M shares float."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/disc/lowfloat",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def pipo(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Past IPOs dates."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/disc/pipo",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def rtat(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Top 10 retail traded stocks per day."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/disc/rtat",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def trending(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Trending news."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/disc/trending",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def ugs(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Undervalued stocks with revenue and earnings growth above 25%."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/disc/ugs",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def ulc(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Potentially undervalued large cap stocks."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/disc/ulc",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def upcoming(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Upcoming earnings release dates."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/disc/upcoming",
**inputs,
).output
return filter_output(o)

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### THIS FILE IS AUTO-GENERATED. DO NOT EDIT. ###
from pydantic import validate_arguments
import openbb_core.app.model.results.empty
from openbb_core.app.model.command_output import CommandOutput
from openbb_core.app.static.container import Container
from openbb_core.app.static.filters import filter_call, filter_inputs, filter_output
class CLASS_stocks_dps(Container):
@filter_call
@validate_arguments
def psi(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Price vs short interest volume"""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/dps/psi",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def ctb(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Cost to borrow of stocks."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/dps/ctb",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def dpotc(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Dark pools (ATS) vs OTC data."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/dps/dpotc",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def ftd(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Fails-to-deliver data."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/dps/ftd",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def hsi(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Show top high short interest stocks of over 20% ratio."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/dps/hsi",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def pos(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Dark pool short position."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/dps/pos",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def prom(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Promising tickers based on dark pool shares regression."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/dps/prom",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def psi_q(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/dps/psi_q",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def psi_sg(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/dps/psi_sg",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def shorted(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Most shorted stocks."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/dps/shorted",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def sidtc(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Short interest and days to cover."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/dps/sidtc",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def spos(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Net short vs position."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/dps/spos",
**inputs,
).output
return filter_output(o)

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### THIS FILE IS AUTO-GENERATED. DO NOT EDIT. ###
from pydantic import validate_arguments
import openbb_core.app.model.results.empty
from openbb_core.app.model.command_output import CommandOutput
from openbb_core.app.static.container import Container
from openbb_core.app.static.filters import filter_call, filter_inputs, filter_output
class CLASS_stocks_gov(Container):
@filter_call
@validate_arguments
def contracts(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Return government contracts."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/gov/contracts",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def government_trading(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Return government trading."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/gov/government_trading",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def gtrades(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Return government trades."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/gov/gtrades",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def histcont(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Historical quarterly government contracts."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/gov/histcont",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def lastcontracts(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Return last government contracts given out."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/gov/lastcontracts",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def lasttrades(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Last trades."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/gov/lasttrades",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def lobbying(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Corporate lobbying details."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/gov/lobbying",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def qtrcontracts(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Quarterly government contracts analysis."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/gov/qtrcontracts",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def topbuys(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Show most purchased stocks."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/gov/topbuys",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def toplobbying(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Top corporate lobbying tickers."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/gov/toplobbying",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def topsells(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Show most sold stocks."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/gov/topsells",
**inputs,
).output
return filter_output(o)

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### THIS FILE IS AUTO-GENERATED. DO NOT EDIT. ###
from pydantic import validate_arguments
import openbb_core.app.model.results.empty
from openbb_core.app.model.command_output import CommandOutput
from openbb_core.app.static.container import Container
from openbb_core.app.static.filters import filter_call, filter_inputs, filter_output
class CLASS_stocks_ins(Container):
@filter_call
@validate_arguments
def act(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Insider activity over time."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ins/act",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def blcp(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Big latest CEO/CFO purchaces ($25k+)."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ins/blcp",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def blcs(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Big latest CEO/CFO sales ($100k+)."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ins/blcs",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def blip(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Big latest insider purchaces ($25+)."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ins/blip",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def blis(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Big latest insider sales ($100k+)."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ins/blis",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def blop(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Big latest officer purchaces ($25k+)."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ins/blop",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def blos(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Big latest officer sales ($100k+)."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ins/blos",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def filt(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Filter insiders based on preset."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ins/filt",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def lcb(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Latest cluster buys."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ins/lcb",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def lins(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Last insider trading of the company."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ins/lins",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def lip(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Latest insider purchaces."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ins/lip",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def lis(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Latest insider sales."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ins/lis",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def lit(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Latest insider trading (all filings)."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ins/lit",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def lpsb(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Latest penny stock buys."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ins/lpsb",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def print_insider_data(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Print insider data."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ins/print_insider_data",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def stats(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Insider stats of the company."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/ins/stats",
**inputs,
).output
return filter_output(o)

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@@ -0,0 +1,264 @@
### THIS FILE IS AUTO-GENERATED. DO NOT EDIT. ###
from pydantic import validate_arguments
import openbb_core.app.model.results.empty
from openbb_core.app.model.command_output import CommandOutput
from openbb_core.app.static.container import Container
from openbb_core.app.static.filters import filter_call, filter_inputs, filter_output
class CLASS_stocks_options(Container):
@filter_call
@validate_arguments
def chains(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Return options chains with greeks."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/options/chains",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def dte(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/options/dte",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def eodchain(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Gets option chain at a specific date."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/options/eodchain",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def expirations(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Return options expirations."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/options/expirations",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def grhist(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Plot option greek history."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/options/grhist",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def hist(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Plot option history."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/options/hist",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def info(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Display option information (volatility, IV rank, etc.)."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/options/info",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def last_price(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Return last price of an option."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/options/last_price",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def oi(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Plot option open interest."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/options/oi",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def pcr(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Display put/call ratio for ticker."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/options/pcr",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def price(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/options/price",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def unu(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Show unusual options activity."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/options/unu",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def voi(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Plot volume and open interest."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/options/voi",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def vol(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Plot volume."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/options/vol",
**inputs,
).output
return filter_output(o)
@filter_call
@validate_arguments
def vsurf(
self, chart: bool = False
) -> CommandOutput[openbb_core.app.model.results.empty.Empty]:
"""Show 3D volatility surface."""
inputs = filter_inputs(
chart=chart,
)
o = self._command_runner_session.run(
"/stocks/options/vsurf",
**inputs,
).output
return filter_output(o)