screener
Screen for companies meeting various criteria.
These criteria include market cap, price, beta, volume, and dividend yield.
Examples
from openbb import obb
obb.equity.screener(provider='fmp')
Parameters
- standard
- finviz
- fmp
- nasdaq
- yfinance
metric: Literal['overview', 'valuation', 'financial', 'ownership', 'performance', 'technical']
Default: overview
The data group to return, default is 'overview'.
exchange: Literal['all', 'amex', 'nasdaq', 'nyse']
Default: all
Filter by exchange.
index: Literal['all', 'dow', 'nasdaq', 'sp500', 'russell']
Default: all
Filter by index.
sector: Literal['all', 'energy', 'materials', 'industrials', 'consumer_cyclical', 'consumer_defensive', 'financial', 'healthcare', 'technology', 'communication_services', 'utilities', 'real_estate']
Default: all
Filter by sector.
industry: str
Default: all
Filter by industry.
Choices
- all
- stocks_only
- etf
- advertising_agencies
- aerospace_defense
- agricultural_inputs
- airlines
- airports_airservices
- aluminum
- apparel_manufacturing
- apparel_retail
- asset_management
- auto_manufacturers
- auto_parts
- auto_dealerships
- banks_diversified
- banks_regional
- beverages_brewers
- beverages_nonalcoholic
- beverages_wineries_distilleries
- biotechnology
- broadcasting
- building_materials
- building_products_equipment
- business_equipment_supplies
- capital_markets
- chemicals
- closed_end_fund_debt
- closed_end_fund_equity
- closed_end_fund_foreign
- coking_coal
- communication_equipment
- computer_hardware
- confectioners
- conglomerates
- consulting_services
- consumer_electronics
- copper
- credit_services
- department_stores
- diagnostics_research
- discount_stores
- drug_manufacturers_general
- drug_manufacturers_specialty_generic
- education_training_services
- electrical_equipment_parts
- electronic_components
- electronic_gaming_multimedia
- electronics_computer_distribution
- engineering_construction
- entertainment
- farm_heavy_construction_machinery
- farm_products
- financial_conglomerates
- financial_data_stock_exchanges
- food_distribution
- footwear_accessories
- furnishings_fixtures_appliances
- gambling
- gold
- grocery_stores
- health_care_plans
- health_information_services
- home_improvement_retail
- household_personal_products
- industrial_distribution
- information_technology_services
- infrastructure_operations
- insurance_brokers
- insurance_diversified
- insurance_life
- insurance_property_casualty
- insurance_reinsurance
- insurance_specialty
- integrated_freight_logistics
- internet_content_information
- internet_retail
- leisure
- lodging
- lumber_wood_production
- luxury_goods
- marine_shipping
- medical_care_facilities
- medical_devices
- medical_distribution
- medical_instruments_supplies
- metal_fabrication
- mortgage_finance
- oil_gas_drilling
- oil_gas_ep
- oil_gas_equipment_services
- oil_gas_integrated
- oil_gas_midstream
- oil_gas_refining_marketing
- other_industrial_metals_mining
- other_precious_metals_mining
- packaged_foods
- packaging_containers
- paper_paper_products
- personal_services
- pharmaceutical_retailers
- pollution_treatment_controls
- publishing
- railroads
- real_estate_development
- real_estate_diversified
- real_estate_services
- recreational_vehicles
- reit_diversified
- reit_health_care_facilities
- reit_hotel_motel
- reit_industrial
- reit_mortgage
- reit_office
- reit_residential
- reit_retail
- reit_specialty
- rental_leasing_services
- residential_construction
- resorts_casinos
- restaurants
- scientific_technical_instruments
- security_protection_services
- semiconductor_equipment_materials
- semiconductors
- shell_companies
- silver
- software_application
- software_infrastructure
- solar
- specialty_business_services
- specialty_chemicals
- specialty_industrial_machinery
- specialty_retail
- staffing_employment_services
- steel
- telecom_services
- textile_manufacturing
- thermal_coal
- tobacco
- tools_accessories
- travel_services
- trucking
- uranium
- utilities_diversified
- utilities_independent_power_producers
- utilities_regulated_electric
- utilities_regulated_gas
- utilities_regulated_water
- utilities_renewable
- waste_management
mktcap: Literal['all', 'mega', 'large', 'large_over', 'large_under', 'mid', 'mid_over', 'mid_under', 'small', 'small_over', 'small_under', 'micro', 'micro_over', 'micro_under', 'nano']
Default: all
Description
Filter by market cap.
Mega - > 200B
Large - 10B - 200B
Mid - 2B - 10B
Small - 300M - 2B
Micro - 50M - 300M
Nano - < 50M
recommendation: Literal['all', 'strong_buy', 'buy+', 'buy', 'hold+', 'hold', 'hold-', 'sell', 'sell-', 'strong_sell']
Default: all
Filter by analyst recommendation.
signal: str
Description
The Finviz screener signal to use. When no parameters are provided, the screener defaults to 'top_gainers'. Available signals are:
channel: both support and resistance trendlines are horizontal
channel_down: both support and resistance trendlines slope downward
channel_up: both support and resistance trendlines slope upward
double_bottom: stock with 'W' shape that indicates a bullish reversal in trend
double_top: stock with 'M' shape that indicates a bearish reversal in trend
downgrades: stocks downgraded by analysts today
earnings_after: companies reporting earnings today, after market close
earnings_before: companies reporting earnings today, before market open
head_shoulders: chart formation that predicts a bullish-to-bearish trend reversal
head_shoulders_inverse: chart formation that predicts a bearish-to-bullish trend reversal
horizontal_sr: horizontal channel of price range between support and resistance trendlines
major_news: stocks with the highest news coverage today
most_active: stocks with the highest trading volume today
most_volatile: stocks with the highest widest high/low trading range today
multiple_bottom: same as double_bottom hitting more lows
multiple_top: same as double_top hitting more highs
new_high: stocks making 52-week high today
new_low: stocks making 52-week low today
overbought: stock is becoming overvalued and may experience a pullback.
oversold: oversold stocks may represent a buying opportunity for investors
recent_insider_buying: stocks with recent insider buying activity
recent_insider_selling: stocks with recent insider selling activity
tl_resistance: once a rising trendline is broken
tl_support: once a falling trendline is broken
top_gainers: stocks with the highest price gain percent today
top_losers: stocks with the highest price percent loss today
triangle_ascending: upward trendline support and horizontal trendline resistance
triangle_descending: horizontal trendline support and downward trendline resistance
unusual_volume: stocks with unusually high volume today - the highest relative volume ratio
upgrades: stocks upgraded by analysts today
wedge: upward trendline support, downward trendline resistance (contiunation)
wedge_down: downward trendline support and downward trendline resistance (reversal)
wedge_up: upward trendline support and upward trendline resistance (reversal)
Choices
- channel
- channel_down
- channel_up
- double_bottom
- double_top
- downgrades
- earnings_after
- earnings_before
- head_shoulders
- head_shoulders_inverse
- horizontal_sr
- major_news
- most_active
- most_volatile
- multiple_bottom
- multiple_top
- new_high
- new_low
- overbought
- oversold
- recent_insider_buying
- recent_insider_selling
- tl_resistance
- tl_support
- top_gainers
- top_losers
- triangle_ascending
- triangle_descending
- unusual_volume
- upgrades
- wedge
- wedge_down
- wedge_up
preset: str
Description
A configured preset file to use for the query. This overrides all other query parameters except 'metric', and 'limit'. Presets (.ini text files) can be created and modified in the '~/OpenBBUserData/finviz/presets' directory. If the path does not exist, it will be created and populated with the default presets on the first run. Refer to the file, 'screener_template.ini', for the format and options.
Note: Syntax of parameters in preset files must follow the template file exactly - i.e, Analyst Recom. = Strong Buy (1)
filters_dict: Union[Dict, str]
A formatted dictionary, or serialized JSON string, of additional filters to apply to the query. This parameter can be used as an alternative to preset files, and is ignored when a preset is supplied. Invalid entries will raise an error. Syntax should follow the 'screener_template.ini' file.
limit: int
The number of data entries to return.
mktcap_min: int
Filter by market cap greater than this value.
mktcap_max: int
Filter by market cap less than this value.
price_min: float
Filter by price greater than this value.
price_max: float
Filter by price less than this value.
beta_min: float
Filter by a beta greater than this value.
beta_max: float
Filter by a beta less than this value.
volume_min: int
Filter by volume greater than this value.
volume_max: int
Filter by volume less than this value.
dividend_min: float
Filter by dividend amount greater than this value.
dividend_max: float
Filter by dividend amount less than this value.
sector: Literal['consumer_cyclical', 'energy', 'technology', 'industrials', 'financial_services', 'basic_materials', 'communication_services', 'consumer_defensive', 'healthcare', 'real_estate', 'utilities', 'industrial_goods', 'financial', 'services']
Filter by sector.
industry: str
Filter by industry.
country: Literal['ae', 'ai', 'ar', 'at', 'au', 'ax', 'az', 'bb', 'bd', 'be', 'bg', 'bh', 'bm', 'br', 'bs', 'bw', 'ca', 'ch', 'ci', 'ck', 'cl', 'cn', 'co', 'cr', 'cw', 'cy', 'cz', 'de', 'dk', 'do', 'ee', 'eg', 'es', 'fi', 'fk', 'fr', 'ga', 'gb', 'ge', 'gf', 'gg', 'gi', 'gl', 'gr', 'hk', 'hu', 'id', 'ie', 'il', 'im', 'in', 'is', 'it', 'je', 'jo', 'jp', 'ke', 'kg', 'kh', 'kr', 'kw', 'ky', 'kz', 'li', 'lt', 'lu', 'lv', 'ma', 'mc', 'me', 'mk', 'mm', 'mn', 'mo', 'mq', 'mt', 'mu', 'mx', 'my', 'mz', 'na', 'ng', 'nl', 'no', 'nz', 'pa', 'pe', 'pg', 'ph', 'pk', 'pl', 'pr', 'pt', 'qa', 're', 'ro', 'ru', 'sa', 'se', 'sg', 'si', 'sk', 'sn', 'sr', 'tc', 'th', 'tr', 'tw', 'tz', 'ua', 'uk', 'us', 'uy', 'vg', 'vn', 'za', 'zm']
Filter by country, as a two-letter country code.
exchange: Literal['amex', 'ams', 'ase', 'asx', 'ath', 'bme', 'bru', 'bud', 'bue', 'cai', 'cnq', 'cph', 'dfm', 'doh', 'etf', 'euronext', 'hel', 'hkse', 'ice', 'iob', 'ist', 'jkt', 'jnb', 'jpx', 'kls', 'koe', 'ksc', 'kuw', 'lse', 'mex', 'nasdaq', 'neo', 'nse', 'nyse', 'nze', 'osl', 'otc', 'pnk', 'pra', 'ris', 'sao', 'sau', 'set', 'sgo', 'shh', 'shz', 'six', 'sto', 'tai', 'tlv', 'tsx', 'two', 'vie', 'wse', 'xetra']
Filter by exchange.
is_etf: bool
If true, includes ETFs.
is_active: bool
If false, returns only inactive tickers.
is_fund: bool
If true, includes funds.
all_share_classes: bool
If true, includes all share classes of a equity.
limit: int
Default: 50000
Limit the number of results to return.
exchange: Union[Union[Literal['all', 'nasdaq', 'nyse', 'amex'], str], list[Union[Literal['all', 'nasdaq', 'nyse', 'amex'], str]]]
Default: all
Filter by exchange. Multiple items allowed for provider(s): nasdaq.
exsubcategory: Union[Union[Literal['all', 'ngs', 'ngm', 'ncm', 'adr'], str], list[Union[Literal['all', 'ngs', 'ngm', 'ncm', 'adr'], str]]]
Default: all
Description
Filter by exchange subcategory.
NGS - Nasdaq Global Select Market
NGM - Nasdaq Global Market
NCM - Nasdaq Capital Market
ADR - American Depository Receipt Multiple items allowed for provider(s): nasdaq.
mktcap: Union[Union[Literal['all', 'mega', 'large', 'mid', 'small', 'micro'], str], list[Union[Literal['all', 'mega', 'large', 'mid', 'small', 'micro'], str]]]
Default: all
Description
Filter by market cap.
Mega - > 200B
Large - 10B - 200B
Mid - 2B - 10B
Small - 300M - 2B
Micro - 50M - 300M Multiple items allowed for provider(s): nasdaq.
recommendation: Union[Union[Literal['all', 'strong_buy', 'buy', 'hold', 'sell', 'strong_sell'], str], list[Union[Literal['all', 'strong_buy', 'buy', 'hold', 'sell', 'strong_sell'], str]]]
Default: all
Filter by consensus analyst action. Multiple items allowed for provider(s): nasdaq.
sector: Union[Union[Literal['all', 'energy', 'basic_materials', 'industrials', 'consumer_staples', 'consumer_discretionary', 'health_care', 'financial_services', 'technology', 'communication_services', 'utilities', 'real_estate'], str], list[Union[Literal['all', 'energy', 'basic_materials', 'industrials', 'consumer_staples', 'consumer_discretionary', 'health_care', 'financial_services', 'technology', 'communication_services', 'utilities', 'real_estate'], str]]]
Default: all
Filter by sector. Multiple items allowed for provider(s): nasdaq.
region: Union[Union[Literal['all', 'africa', 'asia', 'australia_and_south_pacific', 'caribbean', 'europe', 'middle_east', 'north_america', 'south_america'], str], list[Union[Literal['all', 'africa', 'asia', 'australia_and_south_pacific', 'caribbean', 'europe', 'middle_east', 'north_america', 'south_america'], str]]]
Default: all
Filter by region. Multiple items allowed for provider(s): nasdaq.
country: Union[Union[Literal['all', 'argentina', 'armenia', 'australia', 'austria', 'belgium', 'bermuda', 'brazil', 'canada', 'cayman_islands', 'chile', 'colombia', 'costa_rica', 'curacao', 'cyprus', 'denmark', 'finland', 'france', 'germany', 'greece', 'guernsey', 'hong_kong', 'india', 'indonesia', 'ireland', 'isle_of_man', 'israel', 'italy', 'japan', 'jersey', 'luxembourg', 'macau', 'mexico', 'monaco', 'netherlands', 'norway', 'panama', 'peru', 'philippines', 'puerto_rico', 'russia', 'singapore', 'south_africa', 'south_korea', 'spain', 'sweden', 'switzerland', 'taiwan', 'turkey', 'united_kingdom', 'united_states', 'usa'], str], list[Union[Literal['all', 'argentina', 'armenia', 'australia', 'austria', 'belgium', 'bermuda', 'brazil', 'canada', 'cayman_islands', 'chile', 'colombia', 'costa_rica', 'curacao', 'cyprus', 'denmark', 'finland', 'france', 'germany', 'greece', 'guernsey', 'hong_kong', 'india', 'indonesia', 'ireland', 'isle_of_man', 'israel', 'italy', 'japan', 'jersey', 'luxembourg', 'macau', 'mexico', 'monaco', 'netherlands', 'norway', 'panama', 'peru', 'philippines', 'puerto_rico', 'russia', 'singapore', 'south_africa', 'south_korea', 'spain', 'sweden', 'switzerland', 'taiwan', 'turkey', 'united_kingdom', 'united_states', 'usa'], str]]]
Default: all
Filter by country. Multiple items allowed for provider(s): nasdaq.
limit: int
Limit the number of results to return.
country: str
Default: us
Filter by country, as a two-letter country code. Default is, 'us'. Use, 'all', for all countries.
Choices
- all
- ar
- at
- au
- be
- br
- ca
- ch
- cl
- cn
- cz
- de
- dk
- ee
- eg
- es
- fi
- fr
- gb
- gr
- hk
- hu
- id
- ie
- il
- in
- is
- it
- jp
- kr
- kw
- lk
- lt
- lv
- mx
- my
- nl
- no
- nz
- pe
- ph
- pk
- pl
- pt
- qa
- ro
- ru
- sa
- se
- sg
- sr
- th
- tr
- tw
- us
- ve
- vn
- za
exchange: Literal['ams', 'aqs', 'ase', 'asx', 'ath', 'ber', 'bru', 'bse', 'bts', 'bud', 'bue', 'bvb', 'bvc', 'ccs', 'cnq', 'cph', 'cxe', 'dfm', 'doh', 'dus', 'ebs', 'fka', 'fra', 'ger', 'ham', 'han', 'hel', 'hkg', 'ice', 'iob', 'ise', 'ist', 'jkt', 'jnb', 'jpx', 'kls', 'kuw', 'lis', 'lit', 'lse', 'mce', 'mex', 'mil', 'mun', 'ncm', 'neo', 'ngm', 'nms', 'nsi', 'nyq', 'nze', 'oem', 'oqb', 'oqx', 'osl', 'par', 'pnk', 'pra', 'ris', 'sau', 'ses', 'set', 'sgo', 'shh', 'shz', 'sto', 'stu', 'tai', 'tal', 'tlv', 'tor', 'two', 'van', 'vie', 'vse', 'wse']
Filter by exchange.
sector: Literal['basic_materials', 'communication_services', 'consumer_cyclical', 'consumer_defensive', 'energy', 'financial_services', 'healthcare', 'industrials', 'real_estate', 'technology', 'utilities']
Filter by sector.
industry: str
Filter by industry.
Choices
- advertising_agencies
- aerospace_defense
- agricultural_inputs
- airlines
- airports_air_services
- aluminum
- apparel_manufacturing
- apparel_retail
- asset_management
- auto_components
- auto_manufacturers
- auto_parts
- auto_truck_dealerships
- automobiles
- banks
- biotechnology
- broadcasting
- building_materials
- building_products
- building_products_equipment
- business_equipment_supplies
- capital_markets
- chemicals
- coking_coal
- commercial_services
- communication_equipment
- computer_hardware
- confectioners
- construction_engineering
- construction_materials
- consulting_services
- consumer_durables
- consumer_electronics
- consumer_services
- copper
- credit_services
- department_stores
- diagnostics_research
- discount_stores
- diversified_financials
- education_training_services
- electrical_equipment
- electrical_equipment_parts
- electronic_components
- electronic_gaming_multimedia
- electronics_computer_distribution
- energy_services
- engineering_construction
- entertainment
- farm_heavy_construction_machinery
- farm_products
- financial_conglomerates
- financial_data_stock_exchanges
- food_distribution
- footwear_accessories
- furnishings_fixtures_appliances
- gambling
- gold
- grocery_stores
- health_information_services
- healthcare_plans
- home_builders
- home_improvement_retail
- household_products
- household_personal_products
- industrial_conglomerates
- industrial_distribution
- information_technology_services
- infrastructure_operations
- insurance
- integrated_freight_logistics
- internet_content_information
- internet_retail
- leisure
- lodging
- lumber_wood_production
- luxury_goods
- machinery
- marine_shipping
- media
- medical_care_facilities
- medical_devices
- medical_distribution
- medical_instruments_supplies
- metal_fabrication
- mortgage_finance
- oil_gas_drilling
- oil_gas_e_p
- oil_gas_equipment_services
- oil_gas_integrated
- oil_gas_midstream
- oil_gas_producers
- oil_gas_refining_marketing
- other_industrial_metals_mining
- other_precious_metals_mining
- packaged_foods
- packaging_containers
- paper_forestry
- paper_paper_products
- personal_services
- pharmaceuticals
- pharmaceutical_retailers
- pollution_treatment_controls
- precious_metals
- publishing
- railroads
- real_estate
- recreational_vehicles
- refiners_pipelines
- rental_leasing_services
- residential_construction
- resorts_casinos
- restaurants
- retailing
- scientific_technical_instruments
- security_protection_services
- semiconductor_equipment_materials
- semiconductors
- shell_companies
- silver
- software_and_services
- solar
- specialty_business_services
- specialty_chemicals
- specialty_industrial_machinery
- specialty_retail
- staffing_employment_services
- steel
- technology_hardware
- telecom_services
- textiles_apparel
- textile_manufacturing
- thermal_coal
- tobacco
- tools_accessories
- traders_distributors
- transportation
- transportation_infrastructure
- travel_services
- trucking
- uranium
- utilities
- waste_management
mktcap_min: int
Default: 500000000
Filter by market cap greater than this value. Default is 500M.
mktcap_max: int
Filter by market cap less than this value.
price_min: float
Default: 5
Filter by price greater than this value. Default is, 5
price_max: float
Filter by price less than this value.
volume_min: int
Default: 10000
Filter by volume greater than this value. Default is, 10K
volume_max: int
Filter by volume less than this value.
beta_min: float
Filter by a beta greater than this value.
beta_max: float
Filter by a beta less than this value.
limit: int
Default: 200
Limit the number of results returned. Default is, 200. Set to, 0, for all results.
Returns
results: list[EquityScreener]
Serializable results.
provider: Optional[Literal['finviz', 'fmp', 'nasdaq', 'yfinance']]
Provider name.
warnings: Optional[list[Warning_]]
list of warnings.
chart: Optional[Chart]
Chart object.
extra: dict[str, Any]
Extra info.
Data
- standard
- finviz
- fmp
- nasdaq
- yfinance
symbol: str
Symbol representing the entity requested in the data.
name: str
Name of the company.
symbol: str
Symbol representing the entity requested in the data.
name: str
Name of the company.
earnings_date: str
Earnings date, where 'a' and 'b' mean after and before market close, respectively.
country: str
Country of the company.
sector: str
Sector of the company.
industry: str
Industry of the company.
beta: float
Beta of the stock.
analyst_recommendation: float
Analyst's mean recommendation. (1=Buy 5=Sell).
market_cap: float
Market capitalization of the company.
price: float
Price of a share.
change_percent: float
Price change percentage.
change_from_open: float
Price change percentage, from the opening price.
gap: float
Price gap percentage, from the previous close.
volume: Union[float, int]
The trading volume.
volume_avg: Union[float, int]
3-month average daily volume.
volume_relative: float
Current volume relative to the average.
average_true_range: float
Average true range (14).
price_change_1w: float
One-week price return.
price_change_1m: float
One-month price return.
price_change_3m: float
Three-month price return.
price_change_6m: float
Six-month price return.
price_change_1y: float
One-year price return.
price_change_ytd: float
Year-to-date price return.
volatility_1w: float
One-week volatility.
volatility_1m: float
One-month volatility.
year_high_percent: float
Percent difference from current price to the 52-week high.
year_low_percent: float
Percent difference from current price to the 52-week low.
sma20_percent: float
Percent difference from current price to the 20-day simple moving average.
sma50_percent: float
Percent difference from current price to the 50-day simple moving average.
sma200_percent: float
Percent difference from current price to the 200-day simple moving average.
rsi: float
Relative strength index (14).
shares_outstanding: Union[float, int]
Number of shares outstanding.
shares_float: Union[float, int]
Number of shares available to trade.
short_interest: float
Percent of float reported as short.
short_ratio: float
Short interest ratio
insider_ownership: float
Insider ownership as a percentage.
insider_ownership_change: float
6-month change in insider ownership percentage.
institutional_ownership: float
Institutional ownership as a percentage.
institutional_ownership_change: float
3-month change in institutional ownership percentage.
price_to_earnings: float
Price to earnings ratio.
forward_pe: float
Forward price to earnings ratio.
peg_ratio: float
Price/Earnings-To-Growth (PEG) ratio.
price_to_sales: float
Price to sales ratio.
price_to_book: float
Price to book ratio.
price_to_cash: float
Price to cash ratio.
price_to_fcf: float
Price to free cash flow ratio.
eps_growth_past_1y: float
EPS growth for this year.
eps_growth_next_1y: float
EPS growth next year.
eps_growth_past_5y: float
EPS growth for the previous 5 years.
eps_growth_next_5y: float
EPS growth for the next 5 years.
sales_growth_past_5y: float
Sales growth for the previous 5 years.
dividend_yield: float
Annualized dividend yield.
return_on_assets: float
Return on assets.
return_on_equity: float
Return on equity.
return_on_investment: float
Return on investment.
current_ratio: float
Current ratio.
quick_ratio: float
Quick ratio.
long_term_debt_to_equity: float
Long term debt to equity ratio.
debt_to_equity: float
Total debt to equity ratio.
gross_margin: float
Gross margin.
operating_margin: float
Operating margin.
profit_margin: float
Profit margin.
symbol: str
Symbol representing the entity requested in the data.
name: str
Name of the company.
market_cap: int
The market cap of ticker.
sector: str
The sector the ticker belongs to.
industry: str
The industry ticker belongs to.
beta: float
The beta of the ETF.
price: float
The current price.
last_annual_dividend: float
The last annual amount dividend paid.
volume: int
The current trading volume.
exchange: str
The exchange code the asset trades on.
exchange_name: str
The full name of the primary exchange.
country: str
The two-letter country abbreviation where the head office is located.
is_etf: bool
Whether the ticker is an ETF.
is_fund: bool
Whether the ticker is a fund.
actively_trading: bool
Whether the ETF is actively trading.
symbol: str
Symbol representing the entity requested in the data.
name: str
Name of the company.
last_price: float
Last sale price.
change: float
1-day change in price.
change_percent: float
1-day percent change in price.
market_cap: int
Market cap.
symbol: str
Symbol representing the entity requested in the data.
name: str
Name of the company.
open: float
Open price for the day.
high: float
High price for the day.
low: float
Low price for the day.
previous_close: float
Previous close price.
ma50: float
50-day moving average.
ma200: float
200-day moving average.
year_high: float
52-week high.
year_low: float
52-week low.
market_cap: float
Market Cap.
shares_outstanding: float
Shares outstanding.
book_value: float
Book value per share.
price_to_book: float
Price to book ratio.
eps_ttm: float
Earnings per share over the trailing twelve months.
eps_forward: float
Forward earnings per share.
pe_forward: float
Forward price-to-earnings ratio.
dividend_yield: float
Trailing twelve month dividend yield.
exchange: str
Exchange where the stock is listed.
exchange_timezone: str
Timezone of the exchange.
earnings_date: datetime
Most recent earnings date.
currency: str
Currency of the price data.