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hoopsAI allows you to generate on-demand, personalized content automatically for financial news and events. This API is documented in OpenAPI format.
All HTTP requests made against the hoopsAI API must be validated with an API Key. If you don't have an API Key yet please contact support@hoopsai.com to register for one.
Using Your API Key
You may use any server side programming language that can make HTTP requests to target the API. All requests should target domain https://api.hoopsai.com/prod.
API Key should be supplied in REST API calls via a custom header named x-api-key.
API Key Usage Credits
API plans include a monthly limit or "hard cap" to the number of data calls that can be made. This usage is tracked as API "call credits" which are incremented 1:1 against successful (HTTP Status 200) data calls made with your key. Please contact the support if the limit has been reached and you require an additional cap.
Each HTTP request must contain the header Accept: application/json. You should also send an Accept-Encoding: deflate, gzip header to receive data fast and efficiently.
Endpoint Response Payload Format
All endpoints return data in JSON format with the results of your query under 'result' if the call is successful:
{
"success": true,
"result": {
...
},
"code": 0,
"message": "string"
}
Status is always included for both successful calls and failures when possible.
Asset Identifiers
All Assets should be identified in endpoints using the assets's symbol (eg. symbol=EURUSD for Euro). For a current list of supported assets use the /assets api.
Content requests take a long time compared to a typical API since the system doesn't just retrieve data, it runs a complete pipeline for each call.
Expect a few seconds for a typical request, and up to 30 seconds for complicated requests (i.e. multiple assets)
When calling multiple assets, the current suggested limit is 3 assets per call, although this is not enforced by the API. Exceeding this might lead to a Timeout Error.
Get near real-time content within the context of the last trading day for the selected asset symbol
asset required | string Choose target asset, multiple assets are supported by chaining asset symbols with a comma i.e: EURUSD,GBPUSD |
metadata_level | integer [ 0 .. 2 ] Indicates the level of metadata to include in the response |
technical_level | integer [ 0 .. 1 ] Default: 0 Indicates the level of Technical analysis to include in the content. Possible values - 0 - turn off all technical analysis content. 1 - include interesting technical analysis aspects throughout the article. |
separate_header | boolean Default: false Indicates whether the header will be separated from the body in the response |
preferred_data_source | string Specifies a preferred base data source (for available options please contact support). The supplied source supplements data and any missing data uses the default source as a fallback to ensure content can be generated. |
use_customer_assets | boolean Default: false If true, the generated content will ONLY include assets from the assets specified as customer_assets, this is a list of approved assets that can be controlled via the dashboard by any user with Editor permissions. This includes any related assets that might be mentioned throughout the article. |
length | integer [ 1 .. 10 ] Default: 5 Choose target length for the generated article |
import http.client import json asset = TARGET_ASSET api_key = YOUR_API_KEY headers = {'x-api-key': api_key} conn = http.client.HTTPSConnection("api.hoopsai.com") conn.request("GET", "/prod/api/v2/resources/finance/daily/{}".format(asset), None, headers) res = conn.getresponse() data = json.loads(res.read())
{- "success": true,
- "result": {
- "content": "string",
- "text": "string",
- "image_url": "string",
- "metadata": {
- "generation_utc_date": "string",
- "generation_run_time": 0,
- "doc_structure": "string",
- "doc_structure_score": 0,
- "asset_quote": {
- "timestamp": "string",
- "open": 0,
- "previous_close": 0,
- "close": 0,
- "high": 0,
- "low": 0
}, - "asset_ticks": [
- [
- null
]
]
}
}, - "code": 0,
- "message": "string"
}
Generate endless Market Research over multiple topics, powered by a wide range of asset screeners and parameters
Generate an article covering the top gaining assets of the latest trading session.
metadata_level | integer [ 0 .. 2 ] Indicates the level of metadata to include in the response |
technical_level | integer [ 0 .. 1 ] Default: 0 Indicates the level of Technical analysis to include in the content. Possible values - 0 - turn off all technical analysis content. 1 - include interesting technical analysis aspects throughout the article. |
separate_header | boolean Default: false Indicates whether the header will be separated from the body in the response |
preferred_data_source | string Specifies a preferred base data source (for available options please contact support). The supplied source supplements data and any missing data uses the default source as a fallback to ensure content can be generated. |
use_customer_assets | boolean Default: false If true, the generated content will ONLY include assets from the assets specified as customer_assets, this is a list of approved assets that can be controlled via the dashboard by any user with Editor permissions. This includes any related assets that might be mentioned throughout the article. |
filters | string The asset filter requires a special format. See the filter guide for full options and examples. |
import http.client import json api_key = YOUR_API_KEY headers = {'x-api-key': api_key} conn = http.client.HTTPSConnection("api.hoopsai.com") conn.request("GET", "/prod/api/v2/resources/finance/article/top_gainers", None, headers) res = conn.getresponse() data = json.loads(res.read())
{- "success": true,
- "result": {
- "content": "string",
- "text": "string",
- "image_url": "string",
- "metadata": {
- "generation_utc_date": "string",
- "generation_run_time": 0,
- "doc_structure": "string",
- "doc_structure_score": 0,
- "asset_quote": {
- "timestamp": "string",
- "open": 0,
- "previous_close": 0,
- "close": 0,
- "high": 0,
- "low": 0
}, - "asset_ticks": [
- [
- null
]
]
}
}, - "code": 0,
- "message": "string"
}
Generate an article covering the top losing assets of the latest trading session.
metadata_level | integer [ 0 .. 2 ] Indicates the level of metadata to include in the response |
technical_level | integer [ 0 .. 1 ] Default: 0 Indicates the level of Technical analysis to include in the content. Possible values - 0 - turn off all technical analysis content. 1 - include interesting technical analysis aspects throughout the article. |
separate_header | boolean Default: false Indicates whether the header will be separated from the body in the response |
preferred_data_source | string Specifies a preferred base data source (for available options please contact support). The supplied source supplements data and any missing data uses the default source as a fallback to ensure content can be generated. |
use_customer_assets | boolean Default: false If true, the generated content will ONLY include assets from the assets specified as customer_assets, this is a list of approved assets that can be controlled via the dashboard by any user with Editor permissions. This includes any related assets that might be mentioned throughout the article. |
filters | string The asset filter requires a special format. See the filter guide for full options and examples. |
import http.client import json api_key = YOUR_API_KEY headers = {'x-api-key': api_key} conn = http.client.HTTPSConnection("api.hoopsai.com") conn.request("GET", "/prod/api/v2/resources/finance/article/top_losers", None, headers) res = conn.getresponse() data = json.loads(res.read())
{- "success": true,
- "result": {
- "content": "string",
- "text": "string",
- "image_url": "string",
- "metadata": {
- "generation_utc_date": "string",
- "generation_run_time": 0,
- "doc_structure": "string",
- "doc_structure_score": 0,
- "asset_quote": {
- "timestamp": "string",
- "open": 0,
- "previous_close": 0,
- "close": 0,
- "high": 0,
- "low": 0
}, - "asset_ticks": [
- [
- null
]
]
}
}, - "code": 0,
- "message": "string"
}
Generate an article covering the top news of the major indices and markets for the latest trading session.
metadata_level | integer [ 0 .. 2 ] Indicates the level of metadata to include in the response |
technical_level | integer [ 0 .. 1 ] Default: 0 Indicates the level of Technical analysis to include in the content. Possible values - 0 - turn off all technical analysis content. 1 - include interesting technical analysis aspects throughout the article. |
separate_header | boolean Default: false Indicates whether the header will be separated from the body in the response |
preferred_data_source | string Specifies a preferred base data source (for available options please contact support). The supplied source supplements data and any missing data uses the default source as a fallback to ensure content can be generated. |
use_customer_assets | boolean Default: false If true, the generated content will ONLY include assets from the assets specified as customer_assets, this is a list of approved assets that can be controlled via the dashboard by any user with Editor permissions. This includes any related assets that might be mentioned throughout the article. |
filters | string The asset filter requires a special format. See the filter guide for full options and examples. |
import http.client import json api_key = YOUR_API_KEY headers = {'x-api-key': api_key} conn = http.client.HTTPSConnection("api.hoopsai.com") conn.request("GET", "/prod/api/v2/resources/finance/article/market_overview", None, headers) res = conn.getresponse() data = json.loads(res.read())
{- "success": true,
- "result": {
- "content": "string",
- "text": "string",
- "image_url": "string",
- "metadata": {
- "generation_utc_date": "string",
- "generation_run_time": 0,
- "doc_structure": "string",
- "doc_structure_score": 0,
- "asset_quote": {
- "timestamp": "string",
- "open": 0,
- "previous_close": 0,
- "close": 0,
- "high": 0,
- "low": 0
}, - "asset_ticks": [
- [
- null
]
]
}
}, - "code": 0,
- "message": "string"
}
Generate an article covering the assets that are scheduled to publish quarterly earning reports in the upcoming 30 days
metadata_level | integer [ 0 .. 2 ] Indicates the level of metadata to include in the response |
technical_level | integer [ 0 .. 1 ] Default: 0 Indicates the level of Technical analysis to include in the content. Possible values - 0 - turn off all technical analysis content. 1 - include interesting technical analysis aspects throughout the article. |
separate_header | boolean Default: false Indicates whether the header will be separated from the body in the response |
preferred_data_source | string Specifies a preferred base data source (for available options please contact support). The supplied source supplements data and any missing data uses the default source as a fallback to ensure content can be generated. |
use_customer_assets | boolean Default: false If true, the generated content will ONLY include assets from the assets specified as customer_assets, this is a list of approved assets that can be controlled via the dashboard by any user with Editor permissions. This includes any related assets that might be mentioned throughout the article. |
filters | string The asset filter requires a special format. See the filter guide for full options and examples. |
import http.client import json api_key = YOUR_API_KEY headers = {'x-api-key': api_key} conn = http.client.HTTPSConnection("api.hoopsai.com") conn.request("GET", "/prod/api/v2/resources/finance/article/recent_earnings", None, headers) res = conn.getresponse() data = json.loads(res.read())
{- "success": true,
- "result": {
- "content": "string",
- "text": "string",
- "image_url": "string",
- "metadata": {
- "generation_utc_date": "string",
- "generation_run_time": 0,
- "doc_structure": "string",
- "doc_structure_score": 0,
- "asset_quote": {
- "timestamp": "string",
- "open": 0,
- "previous_close": 0,
- "close": 0,
- "high": 0,
- "low": 0
}, - "asset_ticks": [
- [
- null
]
]
}
}, - "code": 0,
- "message": "string"
}
Generate an article covering the assets that recently published quarterly earning reports with real time market updates..
metadata_level | integer [ 0 .. 2 ] Indicates the level of metadata to include in the response |
technical_level | integer [ 0 .. 1 ] Default: 0 Indicates the level of Technical analysis to include in the content. Possible values - 0 - turn off all technical analysis content. 1 - include interesting technical analysis aspects throughout the article. |
separate_header | boolean Default: false Indicates whether the header will be separated from the body in the response |
preferred_data_source | string Specifies a preferred base data source (for available options please contact support). The supplied source supplements data and any missing data uses the default source as a fallback to ensure content can be generated. |
use_customer_assets | boolean Default: false If true, the generated content will ONLY include assets from the assets specified as customer_assets, this is a list of approved assets that can be controlled via the dashboard by any user with Editor permissions. This includes any related assets that might be mentioned throughout the article. |
filters | string The asset filter requires a special format. See the filter guide for full options and examples. |
import http.client import json api_key = YOUR_API_KEY headers = {'x-api-key': api_key} conn = http.client.HTTPSConnection("api.hoopsai.com") conn.request("GET", "/prod/api/v2/resources/finance/article/recent_earnings", None, headers) res = conn.getresponse() data = json.loads(res.read())
{- "success": true,
- "result": {
- "content": "string",
- "text": "string",
- "image_url": "string",
- "metadata": {
- "generation_utc_date": "string",
- "generation_run_time": 0,
- "doc_structure": "string",
- "doc_structure_score": 0,
- "asset_quote": {
- "timestamp": "string",
- "open": 0,
- "previous_close": 0,
- "close": 0,
- "high": 0,
- "low": 0
}, - "asset_ticks": [
- [
- null
]
]
}
}, - "code": 0,
- "message": "string"
}
Generate an article covering assets showing unusually high volume in the current (or last) trading session.
metadata_level | integer [ 0 .. 2 ] Indicates the level of metadata to include in the response |
technical_level | integer [ 0 .. 1 ] Default: 0 Indicates the level of Technical analysis to include in the content. Possible values - 0 - turn off all technical analysis content. 1 - include interesting technical analysis aspects throughout the article. |
separate_header | boolean Default: false Indicates whether the header will be separated from the body in the response |
preferred_data_source | string Specifies a preferred base data source (for available options please contact support). The supplied source supplements data and any missing data uses the default source as a fallback to ensure content can be generated. |
use_customer_assets | boolean Default: false If true, the generated content will ONLY include assets from the assets specified as customer_assets, this is a list of approved assets that can be controlled via the dashboard by any user with Editor permissions. This includes any related assets that might be mentioned throughout the article. |
filters | string The asset filter requires a special format. See the filter guide for full options and examples. |
import http.client import json api_key = YOUR_API_KEY headers = {'x-api-key': api_key} conn = http.client.HTTPSConnection("api.hoopsai.com") conn.request("GET", "/prod/api/v2/resources/finance/article/unusual_high_volume", None, headers) res = conn.getresponse() data = json.loads(res.read())
{- "success": true,
- "result": {
- "content": "string",
- "text": "string",
- "image_url": "string",
- "metadata": {
- "generation_utc_date": "string",
- "generation_run_time": 0,
- "doc_structure": "string",
- "doc_structure_score": 0,
- "asset_quote": {
- "timestamp": "string",
- "open": 0,
- "previous_close": 0,
- "close": 0,
- "high": 0,
- "low": 0
}, - "asset_ticks": [
- [
- null
]
]
}
}, - "code": 0,
- "message": "string"
}
Get list of all available financial assets and their properties
filters | string The asset filter requires a special format. See the filter guide for full options and examples. |
sort_key | string The sort_key parameter defines how the results are ordered, such as by price changes or volume. See the filter guide for full options and examples. |
filter_scope | string Enum: "overview_scope" "performance_scope" "valuation_scope" "income_statement_scope" "balance_sheet_scope" "cash_flow_scope" "technical_scope" "earnings_scope" The filter_scope parameter allows you to specify the scope of data returned for assets, focusing on specific aspects like valuation or performance. See the filter guide for full options and examples. |
page | integer >= 1 Page number for paginated results |
size | integer [ 1 .. 100 ] Page size - Number of results per page |
import http.client import json api_key = YOUR_API_KEY headers = {'x-api-key': api_key} conn = http.client.HTTPSConnection("api.hoopsai.com") conn.request("GET", "/prod/api/v2/resources/finance/customer_assets" res = conn.getresponse() data = json.loads(res.read())
{- "assets": [
- {
- "asset_id": "string",
- "currency": "string",
- "asset_class": "string",
- "symbol": "string",
- "stock_exchange": "string"
}
], - "total": 0,
- "page": 0,
- "size": 0,
- "pages": 0
}
Get list of all financial assets and their properties under the configuration of the selected customer.
filters | string The asset filter requires a special format. See the filter guide for full options and examples. |
sort_key | string The sort_key parameter defines how the results are ordered, such as by price changes or volume. See the filter guide for full options and examples. |
filter_scope | string Enum: "overview_scope" "performance_scope" "valuation_scope" "income_statement_scope" "balance_sheet_scope" "cash_flow_scope" "technical_scope" "earnings_scope" The filter_scope parameter allows you to specify the scope of data returned for assets, focusing on specific aspects like valuation or performance. See the filter guide for full options and examples. |
page | integer >= 1 Page number for paginated results |
size | integer [ 1 .. 100 ] Page size - Number of results per page |
import http.client import json api_key = YOUR_API_KEY headers = {'x-api-key': api_key} conn = http.client.HTTPSConnection("api.hoopsai.com") conn.request("GET", "/prod/api/v2/resources/finance/assets" res = conn.getresponse() data = json.loads(res.read())
{- "assets": [
- {
- "asset_id": "string",
- "currency": "string",
- "asset_class": "string",
- "symbol": "string",
- "stock_exchange": "string"
}
], - "total": 0,
- "page": 0,
- "size": 0,
- "pages": 0
}
hoopsAI provides asset filtering. You can use the ?filters
param on any of the 'Data' endpoints to define which assets should be included in the response content.
The following section provides a detailed explanation of how to construct filter queries with comparison logic. This will allow developers to apply precise criteria to search queries.
_
i.e.: ?filters=assetclass_currencies
,
(url-encode value %2C) i.e.: ?filters=assetclass_currencies,targetassetnumber_7
They will be joined with AND logic. In this example: assetclass:currencies AND targetassetnumber:7.
?filters=fieldname_operator#value&fieldname_operator#value
Here, fieldname
is the name of the field you wish to filter on, operator
is the comparison operator (e.g., ge, le, eq), and value
is the number you want to compare the field value against. Use &
to chain multiple filter rules together, which will be applied with AND logic.
ge
- Greater than or equal tole
- Less than or equal toeq
- Equal to#
- Used to separate the operator from the value to compare to. (URL Encoded %23)&
- Used to chain multiple filter rules together. (URL Encoded %26)To filter assets where a particular field is greater than or equal to 2000 AND less than or equal to 3000, the filter query would look like this:
?filters=marketcap_ge#2000&marketcap_le#3000
This applies two filters to the marketcap
: one ensuring the value is >= 2000
and another that it is <= 3000
.
|
(url-encode value %7C) as values separator i.e.: ?filters=assetclass_currencies|stocks
. Multiple values specified for a field will be joined with OR logic. In this example: assetclass:currencies OR assetclass:stocks.Below is a complete list of current supported filters detailed by keys and possible values:
Asset Class (Default: "stocks") -
assetclass: {
"currencies",
"indices",
"stocks",
"commodities",
"cryptocurrency",
"etfs"
}
Stock Exchange -
stockexchange: {
"XNYS", // New York stock exchange
"XNAS" // Nasdaq
}
Stock Index -
stockindex: {
"^GSPC", // S&P 500
"^DJI" // Dow Jones
}
Stock Index -
stocksector: {
"basic_materials",
"communication_services",
"conglomerates",
"consumer_cyclical",
"consumer_defensive",
"energy",
"financial_services",
"healthcare",
"industrials",
"real_estate",
"technology",
"utilities"
}
Stock Country -
stockcountry: {
"AR", // Argentina
"AU", // Australia
"BE", // Belgium
"BM", // Bermuda
"BR", // Brazil
"CA", // Canada
"KY", // Cayman Islands
"CL", // Chile
"CN", // China
"CO", // Colombia
"CY", // Cyprus
"DK", // Denmark
"FR", // France
"DE", // Germany
"GB", // Great Britain (UK)
"HK", // Hong Kong
"IN", // India
"ID", // Indonesia
"IE", // Ireland
"IL", // Israel
"IT", // Italy
"JP", // Japan
"LU", // Luxembourg
"MX", // Mexico
"NL", // Netherlands
"PA", // Panama
"PR", // Puerto Rico
"SG", // Singapore
"ZA", // South Africa
"KR", // South Korea
"ES", // Spain
"SE", // Sweden
"CH", // Switzerland
"TW", // Taiwan
"US" // United States
},
Stock Industry -
'stockindustry' : {
"advertising_agencies" // Advertising Agencies
"aerospace_defense" // Aerospace & Defense
"agricultural_inputs" // Agricultural Inputs
"agriculture" // Agriculture
"airlines" // Airlines
"aluminum" // Aluminum
"apparel_manufacturing" // Apparel Manufacturing
"apparel_retail" // Apparel Retail
"application_software" // Application Software
"asset_management" // Asset Management
"auto_manufacturers" // Auto Manufacturers
"auto_parts" // Auto Parts
"auto_truck_dealerships" // Auto & Truck Dealerships
"banks" // Banks
"banks_diversified" // Banks Diversified
"banks_regional" // Banks Regional
"beverages_alcoholic" // Beverages - Alcoholic
"beverages_brewers" // Beverages Brewers
"beverages_non_alcoholic" // Beverages Non-Alcoholic
"beverages_wineries_distilleries" // Beverages Wineries & Distilleries
"biotechnology" // Biotechnology
"broadcasting" // Broadcasting
"building_materials" // Building Materials
"building_products_equipment" // Building Products & Equipment
"business_equipment_supplies" // Business Equipment & Supplies
"business_services" // Business Services
"capital_markets" // Capital Markets
"chemicals" // Chemicals
"communication_equipment" // Communication Equipment
"communication_services" // Communication Services
"computer_hardware" // Computer Hardware
"confectioners" // Confectioners
"conglomerates" // Conglomerates
"consulting_services" // Consulting Services
"consumer_electronics" // Consumer Electronics
"consumer_packaged_goods" // Consumer Packaged Goods
"copper" // Copper
"credit_services" // Credit Services
"department_stores" // Department Stores
"diagnostics_research" // Diagnostics & Research
"discount_stores" // Discount Stores
"drug_manufacturers" // Drug Manufacturers
"drug_manufacturers_general" // Drug Manufacturers General
"drug_manufacturers_general_specialty_generic" // Drug Manufacturers General Specialty & Generic
"drug_manufacturers_specialty_generic" // Drug Manufacturers Specialty & Generic
"education_training_services" // Education & Training Services
"electrical_equipment_parts" // Electrical Equipment & Parts
"electronic_components" // Electronic Components
"electronic_gaming_multimedia" // Electronic Gaming & Multimedia
"electronics_computer_distribution" // Electronics & Computer Distribution
"engineering_construction" // Engineering & Construction
"entertainment" // Entertainment
"farm_heavy_construction_machinery" // Farm & Heavy Construction Machinery
"farm_products" // Farm Products
"financial_conglomerates" // Financial Conglomerates
"financial_data_stock_exchanges" // Financial Data & Stock Exchanges
"food_distribution" // Food Distribution
"footwear_accessories" // Footwear & Accessories
"furnishings_fixtures_appliances" // Furnishings, Fixtures & Appliances
"gambling" // Gambling
"gold" // Gold
"grocery_stores" // Grocery Stores
"health_care_plans" // Health Care Plans
"health_information_services" // Health Information Services
"healthcare_plans" // Healthcare Plans
"home_improvement_retail" // Home Improvement Retail
"household_personal_products" // Household & Personal Products
"industrial_distribution" // Industrial Distribution
"information_technology_services" // Information Technology Services
"insurance" // Insurance
"insurance_brokers" // Insurance Brokers
"insurance_diversified" // Insurance Diversified
"insurance_life" // Insurance - Life
"insurance_property_casualty" // Insurance Property & Casualty
"insurance_reinsurance" // Insurance Reinsurance
"insurance_specialty" // Insurance Specialty
"integrated_freight_logistics" // Integrated Freight & Logistics
"internet_content_information" // Internet Content & Information
"internet_retail" // Internet Retail
"leisure" // Leisure
"lodging" // Lodging
"luxury_goods" // Luxury Goods
"marine_shipping" // Marine Shipping
"medical_care_facilities" // Medical Care Facilities
"medical_devices" // Medical Devices
"medical_distribution" // Medical Distribution
"medical_instruments_equipment" // Medical Instruments & Equipment
"medical_instruments_supplies" // Medical Instruments & Supplies
"metal_fabrication" // Metal Fabrication
"metals_mining" // Metals & Mining
"mortgage_finance" // Mortgage Finance
"oil_gas_e_p" // Oil & Gas - E&P
"oil_gas_equipment_services" // Oil & Gas Equipment & Services
"oil_gas_integrated" // Oil & Gas - Integrated
"oil_gas_midstream" // Oil & Gas Midstream
"oil_gas_refining_marketing" // Oil & Gas Refining & Marketing
"other_industrial_metals_mining" // Other Industrial Metals & Mining
"packaged_foods" // Packaged Foods
"packaging_containers" // Packaging & Containers
"paper_paper_products" // Paper & Paper Products
"personal_services" // Personal Services
"pharmaceutical_retailers" // Pharmaceutical Retailers
"publishing" // Publishing
"railroads" // Railroads
"real_estate_diversified" // Real Estate Diversified
"real_estate_services" // Real Estate Services
"recreational_vehicles" // Recreational Vehicles
"reit_diversified" // REIT Diversified
"reit_healthcare_facilities" // REIT Healthcare Facilities
"reit_hotel_motel" // REIT Hotel & Motel
"reit_industrial" // REIT Industrial
"reit_mortgage" // REIT Mortgage
"reit_office" // REIT Office
"reit_residential" // REIT Residential
"reit_retail" // REIT Retail
"reit_specialty" // REIT Specialty
"rental_leasing_services" // Rental & Leasing Services
"residential_construction" // Residential Construction
"resorts_casinos" // Resorts & Casinos
"restaurants" // Restaurants
"retail_apparel_specialty" // Retail Apparel & Specialty
"scientific_technical_instruments" // Scientific & Technical Instruments
"security_protection_services" // Security & Protection Services
"semiconductor_equipment_materials" // Semiconductor Equipment & Materials
"semiconductors" // Semiconductors
"software_application" // Software Application
"software_infrastructure" // Software Infrastructure
"solar" // Solar
"specialty_business_services" // Specialty Business Services
"specialty_chemicals" // Specialty Chemicals
"specialty_industrial_machinery" // Specialty Industrial Machinery
"specialty_retail" // Specialty Retail
"staffing_employment_services" // Staffing & Employment Services
"steel" // Steel
"telecom_services" // Telecom Services
"tobacco" // Tobacco
"tools_accessories" // Tools & Accessories
"travel_services" // Travel Services
"trucking" // Trucking
"utilities_diversified" // Utilities Diversified
"utilities_independent_power_producers" // Utilities Independent Power Producers
"utilities_regulated_electric" // Utilities Regulated Electric
"utilities_regulated_gas" // Utilities Regulated Gas
"utilities_regulated_water" // Utilities Regulated Water
"utilities_renewable" // Utilities Renewable
"waste_management" // Waste Management
}
Recent Earnings Date (in days) -
'recentearningsdate'
'eq#0' // Today
'eq#-1' // Yesterday
'ge#-1&le#0' // Since Yesterday
'ge#-2&le#-1' // Previous 2 days
'ge#-5&le#-1' // Previous 5 days
'ge#-7&le#-1' // Previous 7 days
Upcoming Earnings Date (in days) -
'upcomingearningsdate'
'eq#0' // Today
'eq#1' // Tomorrow
'ge#0&le#1' // Until Tomorrow
'ge#0&le#2' // Next 2 days
'ge#0&le#5' // Next 5 days
'ge#0&le#7' // Next 7 days
Market Cap (in Millions)-
'marketcap'
'ge#200000' // Mega (over $200B)
'le#200000&ge#10000' // Large ($10B to $200B)
'le#10000&ge#2000' // Mid ($2B to $10B)
'le#2000&ge#300' // Small ($300M to $2B)
'le#300&ge#50' // Micro ($50M to $300M)
'ge#10000' // Large and over (over $10B)
'ge#2000' // Mid and over (over $2B)
'ge#300' // Small and over (over $300M)
'ge#50' // Micro and over (over $50M)
'le#200000' // Large and under (under $200B)
'le#10000' // Mid and under (under $10B)
'le#2000' // Small and under (under $2B)
'le#300' // Micro and under (under $300M)
Last Session Volume Suggested range: 0 - 10,000 (In thousands) -
'lastsessionvolume'
'le#50' // Under 50k
'le#100' // Under 100k
'le#500' // Under 500k
'le#1000' // Under 1m
'ge#50' // Over 50k
'ge#100' // Over 100k
'ge#200' // Over 200k
'ge#500' // Over 500k
'ge#1000' // Over 1m
'ge#2000' // Over 2m
'ge#100&le#500' // 100k to 500k
'ge#500&le#1000' // 500k to 1m
Latest session Volume relative to average. Suggested range: 0 - 50 -
'lastsessionrelativevolume'
'ge#0.25' // Over 0.25
'ge#0.5' // Over 0.5
'ge#1' // Over 1
'ge#1.5' // Over 1.5
'ge#2' // Over 2
'ge#3' // Over 3
'ge#5' // Over 5
'ge#10' // Over 10
'le#0.25' // Under 0.25
'le#0.5' // Under 0.5
'le#1' // Under 1
'le#2' // Under 2
'le#3' // Under 3
Price % Change from open. Suggested range: -100 - 10,000 -
'lastchangepercentage'
'ge#0' // Up
'ge#1' // Up 1%
'ge#2' // Up 2%
'ge#3' // Up 3%
'ge#5' // Up 5%
'ge#10' // Up 10%
'ge#15' // Up 15%
'ge#20' // Up 20%
'le#0' // Down
'le#-1' // Down 1%
'le#-2' // Down 2%
'le#-3' // Down 3%
'le#-5' // Down 5%
'le#-10' // Down 10%
'le#-15' // Down 15%
'le#-20' // Down 20%
Beta -
'beta'
'le#0' // Under 0
'le#0.5' // Under 0.5
'le#1' // Under 1
'le#1.5' // Under 1.5
'le#2' // Under 2
'ge#0' // Over 0
'ge#0.5' // Over 0.5
'ge#1' // Over 1
'ge#1.5' // Over 1.5
'ge#2' // Over 2
'ge#2.5' // Over 2.5
'ge#3' // Over 3
'ge#4' // Over 4
'ge#0&le#0.5' // 0 to 0.5
'ge#0&le#1' // 0 to 1
'ge#0.5&le#1' // 0.5 to 1
'ge#0.5&le#1.5' // 0.5 to 1.5
'ge#1&le#1.5' // 1 to 1.5
'ge#1&le#2' // 1 to 2
Price Earnings Ratio -
'priceearningsratio'
'le#15': // Low (<15)
'ge#0': // Profitable (>0)
'ge#50': // High (>50)
'le#5': // Under 5
'le#10': // Under 10
'le#15': // Under 15
'le#20': // Under 20
'le#25': // Under 25
'le#30': // Under 30
'le#35': // Under 35
'le#40': // Under 40
'le#45': // Under 45
'le#50': // Under 50
'ge#5': // Over 5
'ge#10': // Over 10
'ge#15': // Over 15
'ge#20': // Over 20
'ge#25': // Over 25
'ge#30': // Over 30
'ge#35': // Over 35
'ge#40': // Over 40
'ge#45': // Over 45
'ge#50': // Over 5
Target Symbols - filter by one or more of our supported symbols (full list is available under /api/v2/resources/finance/assets
) -
targetsymbols: {
// full list is available under <code>/api/v2/resources/finance/assets</code>
}
When retrieving asset information through our API, you can tailor your queries to sort the results based on specific metrics of interest. The sort_key
query parameter allows you to define how the returned assets are ordered. The sort_key
parameter can be any of the filtering fields, as long as that field is returned in the current results. The sorting order can be specified as ascending or descending using the sort_asc
parameter (Default - false).
By leveraging these sorting options, developers can create more targeted and relevant asset queries, enhancing the utility and efficiency of financial applications and analyses.
Our API provides a versatile way to tailor your asset data retrieval by using the filter_scope
query parameter. This parameter allows you to specify the scope of data you wish to receive about assets, making your queries more efficient by focusing on the information that matters most to your analysis. Below are the available options for the filter_scope
parameter and what each option includes in the API response:
overview_scope
: Returns a general overview of the asset, including key financial metrics and identifiers such as price
, changesPercentage
, change
, previousClose
, pe
(price to earnings ratio), eps
(earnings per share), volume
, marketCap
(market capitalization), sector
, industry
, and country
. This scope is ideal for getting a quick snapshot of an asset's current standing in the market.
performance_scope
: Focuses on the performance metrics of the asset over various time frames, including price
, changesPercentage
, days_5_change
, month_1_change
, month_3_change
, month_6_change
, month_12_change
, ytd_change
(year-to-date change), years_3_change
, years_5_change
, and years_10_change
. This scope is particularly useful for trend analysis and understanding an asset's historical performance.
valuation_scope
: Provides valuation metrics of the asset, such as price_to_sales_ratio
, price_to_book_ratio
, price_to_free_cash_flows_ratio
, price_earnings_ratio
, price_cash_flow_ratio
, enterprise_value_multiple
, and dividend_yield
. This set of data helps in assessing the asset's valuation and its attractiveness as an investment.
income_statement_scope
: Includes key figures from the asset's income statement like accepted_date
, revenue
, gross_profit
, operating_income
, net_income
, ebitda
, and eps_diluted
. This scope offers a detailed look into the financial performance and profitability of the asset.
balance_sheet_scope
: Covers essential balance sheet items such as accepted_date
, total_assets
, total_current_assets
, cash_and_cash_equivalents
, total_liabilities
, total_debt
, net_debt
, and total_investments
. This scope is essential for evaluating the financial health and stability of the asset.
cash_flow_scope
: Delivers critical cash flow statement metrics including accepted_date
, capital_expenditure
, operating_cash_flow
, dividends_paid
, free_cash_flow
, stock_based_compensation
, common_stock_issued
, and common_stock_repurchased
. This set of data is crucial for understanding the cash generation and spending patterns of the asset.
technical_scope
: Focuses on technical analysis indicators such as trend_macd
, trend_macd_signal
, trend_cci
, momentum_rsi
, momentum_stoch_rsi_k
, momentum_stoch_rsi_d
, and momentum_ao
. This scope is particularly useful for traders and analysts looking for technical entry and exit signals.
earnings_scope
: Focuses on earnings metrics such as eps_forecast
, revenue_forecast
, earnings_time
, earnings_quarter
, eps_actual
, revenue_actual
, earnings_year
, earnings_date
. This scope is particularly useful for gaining a quick overview of the assets earnings.
By utilizing these filter_scope
options, you can streamline your queries to receive only the data you need, enhancing the efficiency and effectiveness of your financial analysis and decision-making processes.