def get_industries(country: str = "", sector: str = ""):
"""Get all industries in Yahoo Finance data based on country or sector. [Source: Finance Database]
Parameters
----------
country : str
Filter retrieved industries by country
sector : str
Filter retrieved industries by sector
Returns
-------
list
List of possible industries
"""
if country and sector:
return fd.show_options("equities", country=country, sector=sector)
if country:
return fd.show_options("equities", country=country)["Industries"]
if sector:
return fd.show_options("equities", sector=sector)["Industries"]
return fd.show_options("equities", "industries")
def industry_autocomp(inter, industry: str):
data = fd.show_options("equities", "industries")
if not industry:
industry = "a"
ilow = industry.lower()
return [
industry for industry in data if industry.lower().startswith(ilow)
][:24]
def get_etfs_categories() -> List[str]:
"""Return a selection of ETF categories. [Source: Finance Database]
Returns
----------
List[str]
ETF categories
"""
return fd.show_options("etfs")
def get_countries(industry: str = "", sector: str = ""):
"""Get all countries in Yahoo Finance data based on sector or industry. [Source: Finance Database]
Parameters
----------
industry : str
Filter retrieved countries by industry
sector : str
Filter retrieved countries by sector
Returns
-------
list
List of possible countries
"""
# industry takes priority since there's 1 sector per industry, but multiple industries per sector
if industry:
return fd.show_options("equities", industry=True)[industry]["Countries"]
if sector:
return fd.show_options("equities", sector=sector)["Countries"]
return fd.show_options("equities", "countries")
def get_sectors(industry: str = "", country: str = ""):
"""Get all sectors in Yahoo Finance data based on country or industry. [Source: Finance Database]
Parameters
----------
industry : str
Filter retrieved sectors by industry
country : str
Filter retrieved sectors by country
Returns
-------
list
List of possible sectors
"""
# industry takes priority since there's 1 sector per industry, but multiple industries per country
if industry:
return [fd.show_options("equities", industry=True)[industry]["Sector"]]
if country:
return fd.show_options("equities", country=country)["Sectors"]
return fd.show_options("equities", "sectors")
def country_autocomp(inter, country: str):
data = fd.show_options("equities", "countries")
clow = country.lower()
return [country for country in data
if country.lower().startswith(clow)][:24]
def show_equities(
country: str,
sector: str,
industry: str,
name: str,
description: str,
marketcap: str,
amount: int,
include_exchanges: bool,
options: str,
):
"""
Display a selection of Equities based on country, sector, industry, name and/or description filtered
by market cap. If no arguments are given, return equities categorized as Large Cap.
[Source: Finance Database]
Parameters
----------
country: str
Search by country to find stocks matching the criteria.
sector : str
Search by sector to find stocks matching the criteria.
industry : str
Search by industry to find stocks matching the criteria.
name : str
Search by name to find stocks matching the criteria.
description : str
Search by description to find stocks matching the criteria.
marketcap : str
Select stocks based on the market cap.
amount : int
Number of stocks to display, default is 10.
include_exchanges: bool
When you wish to include different exchanges use this boolean.
options : str
Show the country, sector or industry options.
"""
if options is not None:
for option in fd.show_options("equities", options):
print(option)
return
if country is not None:
country = " ".join(country).title()
if sector is not None:
sector = " ".join(sector).title()
if industry is not None:
industry = " ".join(industry).title()
data = fd.select_equities(
country=country,
sector=sector,
industry=industry,
exclude_exchanges=include_exchanges,
)
if name is not None:
data = fd.search_products(data, query=" ".join(name), search="long_name")
if description is not None:
data = fd.search_products(data, query=" ".join(description), search="summary")
if marketcap is not None:
data = fd.search_products(
data, query=f"{''.join(marketcap)} Cap", search="market_cap"
)
tabulate_data = pd.DataFrame(data).T[
[
"long_name",
"sector",
"industry",
"country",
"city",
"website",
"market_cap",
]
]
if gtff.USE_TABULATE_DF:
print(
tabulate(
tabulate_data.iloc[:amount],
showindex=True,
headers=[
"Name",
"Sector",
"Industry",
"Country",
"City",
"Website",
"Market Cap",
],
floatfmt=".2f",
tablefmt="fancy_grid",
),
"\n",
)
else:
print(tabulate_data.iloc[:amount].to_string(), "\n")