Financials Earnings and Revenues

Earnings and revenues are critical metrics closely scrutinized by financial markets. The endpoint includes a specialized section allowing clients to seamlessly track their release.

Default calendar

Using Requests:

import requests
your_api_key = 'your_api_key'
url = f'https://api.tradingeconomics.com/earnings-revenues?c={your_api_key}'
data = requests.get(url).json()
print(data)

Or using our package:

import tradingeconomics as te
te.login('your_api_key')
te.getEarnings()

Using Requests:

const axios = require('axios');
(async () => {
    const your_api_key = 'your_api_key'
    const response = await axios.get(`https://api.tradingeconomics.com/earnings-revenues?c=${your_api_key}`)
    console.log(response.data)
})()

Or using our package:

const te = require('tradingeconomics');
te.login('your_api_key');
data = te.getEarnings().then(function(data){
  console.log(data)     
});

Using Requests:

using (var httpClient = new HttpClient())
{
    using (var request = new HttpRequestMessage(new HttpMethod("GET"), "https://api.tradingeconomics.com/earnings-revenues?c=your_api_key"))
    {
        request.Headers.TryAddWithoutValidation("Upgrade-Insecure-Requests", "1");
        var response = await httpClient.SendAsync(request);
        if (response.IsSuccessStatusCode)
        {
            var content = await response.Content.ReadAsStringAsync(); 
            Console.WriteLine(content);
        }
    }
}

The response data format can be configured by appending the &f= parameter to the URL request.

https://api.tradingeconomics.com/earnings-revenues?c={your_api_key}&f=json

[
  {
    "Date": "2023-09-04",
    "Symbol": "TCOM:US",
    "Name": "Trip.com",
    "Actual": "5.11",
    "ActualValue": 5.11,
    "Forecast": "3.61",
    "ForecastValue": 3.61,
    "Previous": "-0.31",
    "PreviousValue": -0.31,
    "Revenue": "11.25B",
    "RevenueValue": 11250000000.0,
    "RevenueForecast": "10.77B",
    "RevenueForecastValue": 10770000000.0,
    "RevenuePrevious": "4.01B",
    "RevenuePreviousValue": 4010000000.0,
    "MarketCapUSD": 22641100000,
    "FiscalTag": "FY2023Q2",
    "FiscalReference": "Q2",
    "CalendarReference": "2023-06-30",
    "Country": "China",
    "Currency": "USD",
    "Importance": 2,
    "Session": 21,
    "MarketRelease": "after_close",
    "LastUpdate": "2023-09-27T13:36:00"
  },
  {
    "Date": "2023-09-04",
    "Symbol": "ARI:SJ",
    "Name": "African Rainbow Minerals",
    "Actual": "19.37",
    "ActualValue": 19.37,
    "Forecast": "0.00",
    "ForecastValue": 0.0,
    "Previous": "39.15",
    "PreviousValue": 39.15,
    "Revenue": "6.72B",
    "RevenueValue": 6720000000.0,
    "RevenueForecast": "",
    "RevenueForecastValue": null,
    "RevenuePrevious": "9.85B",
    "RevenuePreviousValue": 9850000000.0,
    "MarketCapUSD": 1726756424,
    "FiscalTag": "FY2023H2",
    "FiscalReference": "H2",
    "CalendarReference": "2023-06-30",
    "Country": "South Africa",
    "Currency": "ZAR",
    "Importance": 1,
    "Session": 24,
    "MarketRelease": "by_day_end",
    "LastUpdate": "2023-09-22T12:51:00"
  },
  {
    "Date": "2023-09-05",
    "Symbol": "AHT:LN",
    "Name": "Ashtead",
    "Actual": "1.07",
    "ActualValue": 1.07,
    "Forecast": "1.05",
    "ForecastValue": 1.05,
    "Previous": "0.94",
    "PreviousValue": 0.94,
    "Revenue": "2.70B",
    "RevenueValue": 2700000000.0,
    "RevenueForecast": "",
    "RevenueForecastValue": null,
    "RevenuePrevious": "2.26B",
    "RevenuePreviousValue": 2260000000.0,
    "MarketCapUSD": 26175459147,
    "FiscalTag": "FY2024Q1",
    "FiscalReference": "Q1",
    "CalendarReference": "2023-07-31",
    "Country": "United Kingdom",
    "Currency": "USD",
    "Importance": 2,
    "Session": 16,
    "MarketRelease": "after_close",
    "LastUpdate": "2023-09-22T12:42:00"
  }
]

https://api.tradingeconomics.com/earnings-revenues?c={your_api_key}&f=csv

Date,Symbol,Name,Actual,ActualValue,Forecast,ForecastValue,Previous,PreviousValue,Revenue,RevenueValue,RevenueForecast,RevenueForecastValue,RevenuePrevious,RevenuePreviousValue,MarketCapUSD,FiscalTag,FiscalReference,CalendarReference,Country,Currency,Importance,Session,MarketRelease,LastUpdate
2023-09-04,TCOM:US,Trip.com,5.11,5.11,3.61,3.61,-0.31,-0.31,11.25B,11250000000,10.77B,10770000000,4.01B,4010000000,22641100000,FY2023Q2,Q2,2023-06-30,China,USD,2,21,after_close,9/27/2023 1:36:00 PM
2023-09-04,ARI:SJ,African Rainbow Minerals,19.37,19.37,0.00,0,39.15,39.15,6.72B,6720000000,,,9.85B,9850000000,1726756424,FY2023H2,H2,2023-06-30,South Africa,ZAR,1,24,by_day_end,9/22/2023 12:51:00 PM
2023-09-05,AHT:LN,Ashtead,1.07,1.07,1.05,1.05,0.94,0.94,2.70B,2700000000,,,2.26B,2260000000,26175459147,FY2024Q1,Q1,2023-07-31,United Kingdom,USD,2,16,after_close,9/22/2023 12:42:00 PM

https://api.tradingeconomics.com/earnings-revenues?c={your_api_key}

Date	Symbol	Name	Actual	ActualValue	Forecast	ForecastValue	Previous	PreviousValue	Revenue	RevenueValue	RevenueForecast	RevenueForecastValue	RevenuePrevious	RevenuePreviousValue	MarketCapUSD	FiscalTag	FiscalReference	CalendarReference	Country	Currency	Importance	Session	MarketRelease	LastUpdate
2023-06-13	AHT:LN	Ashtead	0.84	0.84											30626778072	FY2023Q4	Q4	2023-03-31	United Kingdom	GBp	1	24	by_day_end	6/20/2023 7:24:00 PM
2023-06-13	MDI:CN	Major Drilling	0.25	0.25	0.26	0.26	0.27	0.27	184.97M	184970000	192.04M	192040000	189.98M	189980000	586712712	FY2023Q4	Q4	2023-03-31	Canada	CAD	1	21	after_close	6/17/2023 8:10:00 AM
2023-06-14	ADBE:US	Adobe Systems					3.35	3.35					4.39B	4390000000	237552100000	FY2023Q2	Q2	2023-03-31	United States	USD	3	21	after_close	4/21/2023 6:57:00 PM

By date

Using Requests:

import requests
your_api_key = 'your_api_key'
url = f'https://api.tradingeconomics.com/earnings-revenues?c={your_api_key}&d1=2017-01-01'
data = requests.get(url).json()
print(data)

Or using our package:

te.getEarnings(initDate='2017-01-01')

Using Requests:

const axios = require('axios');
(async () => {
    const your_api_key = 'your_api_key'
    const response = await axios.get(`https://api.tradingeconomics.com/earnings-revenues?c=${your_api_key}&d1=2017-01-01`)
    console.log(response.data)
})()

Or using our package:

data = te.getEarnings(start_date = '2017-01-01').then(function(data){
  console.log(data)     
});

Using Requests:

new HttpRequestMessage(new HttpMethod("GET"), "https://api.tradingeconomics.com/earnings-revenues?c=your_api_key&d1=2017-01-01");

With an end date:

Using Requests:

import requests
your_api_key = 'your_api_key'
url = f'https://api.tradingeconomics.com/earnings-revenues?c={your_api_key}&d1=2017-01-01&d2=2017-12-31'
data = requests.get(url).json()
print(data)

Or using our package:

te.getEarnings(initDate='2017-01-01', endDate='2017-12-31')

Using Requests:

const axios = require('axios');
(async () => {
    const your_api_key = 'your_api_key'
    const response = await axios.get(`https://api.tradingeconomics.com/earnings-revenues?c=${your_api_key}&d1=2017-01-01&d2=2017-12-31`)
    console.log(response.data)
})()

Or using our package:

data = te.getEarnings(start_date = '2017-01-01', end_date = '2017-12-31').then(function(data){
  console.log(data)     
});

Using Requests:

new HttpRequestMessage(new HttpMethod("GET"), "https://api.tradingeconomics.com/earnings-revenues?c=your_api_key&d1=2017-01-01&d2=2017-12-31");

https://api.tradingeconomics.com/earnings-revenues?c={your_api_key}&d1=yyyy-mm-dd&d2=yyy-mm-dd&f=json

[
  {
    "Date": "2017-01-04",
    "Symbol": "UNF:US",
    "Name": "UniFirst",
    "Actual": "1.38",
    "ActualValue": 1.38,
    "Forecast": "1.57",
    "ForecastValue": 1.57,
    "Previous": "1.78",
    "PreviousValue": 1.78,
    "Revenue": "386.11M",
    "RevenueValue": 386110000.0,
    "RevenueForecast": "",
    "RevenueForecastValue": null,
    "RevenuePrevious": "373.38M",
    "RevenuePreviousValue": 373380000.0,
    "MarketCapUSD": 3033500000,
    "FiscalTag": "FY2017Q1",
    "FiscalReference": "Q1",
    "CalendarReference": "2016-11-30",
    "Country": "United States",
    "Currency": "USD",
    "Importance": 1,
    "Session": 13,
    "MarketRelease": "before_open",
    "LastUpdate": "2023-08-14T18:14:00"
  },
  {
    "Date": "2017-01-05",
    "Symbol": "STZ:US",
    "Name": "Constellation Brands",
    "Actual": "1.96",
    "ActualValue": 1.96,
    "Forecast": "1.72",
    "ForecastValue": 1.72,
    "Previous": "1.42",
    "PreviousValue": 1.42,
    "Revenue": "1.81B",
    "RevenueValue": 1810000000.0,
    "RevenueForecast": "",
    "RevenueForecastValue": null,
    "RevenuePrevious": "1.64B",
    "RevenuePreviousValue": 1640000000.0,
    "MarketCapUSD": 45630900000,
    "FiscalTag": "FY2017Q3",
    "FiscalReference": "Q3",
    "CalendarReference": "2016-11-30",
    "Country": "United States",
    "Currency": "USD",
    "Importance": 3,
    "Session": 21,
    "MarketRelease": "after_close",
    "LastUpdate": "2023-08-14T12:12:00"
  },
  {
    "Date": "2017-01-05",
    "Symbol": "2884:TT",
    "Name": "E Sun Financial Hldg",
    "Actual": "0.18",
    "ActualValue": 0.18,
    "Forecast": "0.24",
    "ForecastValue": 0.24,
    "Previous": "0.18",
    "PreviousValue": 0.18,
    "Revenue": "12.22B",
    "RevenueValue": 12220000000.0,
    "RevenueForecast": "",
    "RevenueForecastValue": null,
    "RevenuePrevious": "9.68B",
    "RevenuePreviousValue": 9680000000.0,
    "MarketCapUSD": 11717835235,
    "FiscalTag": "FY2016Q4",
    "FiscalReference": "Q4",
    "CalendarReference": "2016-12-31",
    "Country": "Taiwan",
    "Currency": "TWD",
    "Importance": 2,
    "Session": 24,
    "MarketRelease": "by_day_end",
    "LastUpdate": "2023-08-14T12:36:00"
  }
]

https://api.tradingeconomics.com/earnings-revenues?c={your_api_key}&d1=yyyy-mm-dd&d2=yyy-mm-dd&f=csv

Date,Symbol,Name,Actual,ActualValue,Forecast,ForecastValue,Previous,PreviousValue,Revenue,RevenueValue,RevenueForecast,RevenueForecastValue,RevenuePrevious,RevenuePreviousValue,MarketCapUSD,FiscalTag,FiscalReference,CalendarReference,Country,Currency,Importance,Session,MarketRelease,LastUpdate
2017-01-04,UNF:US,UniFirst,1.38,1.38,1.57,1.57,1.78,1.78,386.11M,386110000,,,373.38M,373380000,3033500000,FY2017Q1,Q1,2016-11-30,United States,USD,1,13,before_open,8/14/2023 6:14:00 PM
2017-01-05,STZ:US,Constellation Brands,1.96,1.96,1.72,1.72,1.42,1.42,1.81B,1810000000,,,1.64B,1640000000,45630900000,FY2017Q3,Q3,2016-11-30,United States,USD,3,21,after_close,8/14/2023 12:12:00 PM
2017-01-05,2884:TT,E Sun Financial Hldg,0.18,0.18,0.24,0.24,0.18,0.18,12.22B,12220000000,,,9.68B,9680000000,11717835235,FY2016Q4,Q4,2016-12-31,Taiwan,TWD,2,24,by_day_end,8/14/2023 12:36:00 PM

https://api.tradingeconomics.com/earnings-revenues?c={your_api_key}&d1=yyyy-mm-dd&d2=yyy-mm-dd

Date	Symbol	Name	Actual	ActualValue	Forecast	ForecastValue	Previous	PreviousValue	MarketCapUSD	FiscalTag	FiscalReference	CalendarReference	Country	Currency	Importance	Session	MarketRelease	LastUpdate
2017-01-04	RECN:US	Resources Connection	0.18	0.18	0.19	0.19	0.16	0.16	560700000	FY2017Q3	Q3	2016-12-31	United States	USD	1	21	after_close	12/11/2022 7:05:00 PM
2017-01-05	WBA:US	Walgreens Boots Alliance	1.1	1.1			1.31	1.31	26498900000	FY2017Q2	Q2	2016-12-31	United States	USD	3	13	before_open	12/11/2022 1:03:00 PM
2017-01-05	STZ:US	Constellation Brands	1.96	1.96	1.7	1.7	1.19	1.19	48122900000	FY2017Q4	Q4	2016-12-31	United States	USD	2	13	before_open	12/12/2022 3:25:00 AM

By symbol and date

Using Requests:

import requests
your_api_key = 'your_api_key'
url = f'https://api.tradingeconomics.com/earnings-revenues/symbol/aapl:us?c={your_api_key}&d1=2017-01-01'
data = requests.get(url).json()
print(data)

Or using our package:

te.getEarnings(symbols = 'aapl:us', initDate='2017-01-01')

Using Requests:

const axios = require('axios');
(async () => {
    const your_api_key = 'your_api_key'
    const response = await axios.get(`https://api.tradingeconomics.com/earnings-revenues/symbol/aapl:us?c=${your_api_key}&d1=2017-01-01`)
    console.log(response.data)
})()

Or using our package:

data = te.getEarnings(symbol = 'aapl:us', start_date = '2017-01-01').then(function(data){
  console.log(data)     
});

Using Requests:

new HttpRequestMessage(new HttpMethod("GET"), "https://api.tradingeconomics.com/earnings-revenues/symbol/aapl:us?c=your_api_key&d1=2017-01-01");

With an end date:

Using Requests:

import requests
your_api_key = 'your_api_key'
url = f'https://api.tradingeconomics.com/earnings-revenues/symbol/aapl:us?c={your_api_key}&d1=2016-01-01&d2=2017-12-31'
data = requests.get(url).json()
print(data)

Or using our package:

te.getEarnings(symbols = 'aapl:us', initDate='2016-01-01', endDate='2017-12-31')

Using Requests:

const axios = require('axios');
(async () => {
    const your_api_key = 'your_api_key'
    const response = await axios.get(`https://api.tradingeconomics.com/earnings-revenues/symbol/aapl:us?c=${your_api_key}&d1=2016-01-01&d2=2017-12-31`)
    console.log(response.data)
})()

Or using our package:

data = te.getEarnings(symbol = 'aapl:us', start_date = '2016-01-01', end_date = '2017-12-31').then(function(data){
  console.log(data)     
});

Using Requests:

new HttpRequestMessage(new HttpMethod("GET"), "https://api.tradingeconomics.com/earnings-revenues/symbol/aapl:us?c=your_api_key&d1=2016-01-01&d2=2017-12-31");

https://api.tradingeconomics.com/earnings-revenues/symbol/{symbol}?c={your_api_key}&d1=yyyy-mm-dd&d2=yyyy-mm-dd&f=json

[
  {
    "Date": "2016-01-26",
    "Symbol": "AAPL:US",
    "Name": "Apple",
    "Actual": "0.82",
    "ActualValue": 0.82,
    "Forecast": "0.81",
    "ForecastValue": 0.81,
    "Previous": "0.77",
    "PreviousValue": 0.77,
    "Revenue": "75.87B",
    "RevenueValue": 75870000000.0,
    "RevenueForecast": "",
    "RevenueForecastValue": null,
    "RevenuePrevious": "74.60B",
    "RevenuePreviousValue": 74600000000.0,
    "MarketCapUSD": 2712851900000,
    "FiscalTag": "FY2016Q1",
    "FiscalReference": "Q1",
    "CalendarReference": "2015-12-31",
    "Country": "United States",
    "Currency": "USD",
    "Importance": 3,
    "Session": 21,
    "MarketRelease": "after_close",
    "LastUpdate": "2023-08-14T11:41:00"
  },
  {
    "Date": "2016-04-26",
    "Symbol": "AAPL:US",
    "Name": "Apple",
    "Actual": "0.48",
    "ActualValue": 0.48,
    "Forecast": "0.50",
    "ForecastValue": 0.5,
    "Previous": "0.58",
    "PreviousValue": 0.58,
    "Revenue": "50.56B",
    "RevenueValue": 50560000000.0,
    "RevenueForecast": "",
    "RevenueForecastValue": null,
    "RevenuePrevious": "58.01B",
    "RevenuePreviousValue": 58010000000.0,
    "MarketCapUSD": 2712851900000,
    "FiscalTag": "FY2016Q2",
    "FiscalReference": "Q2",
    "CalendarReference": "2016-03-31",
    "Country": "United States",
    "Currency": "USD",
    "Importance": 3,
    "Session": 21,
    "MarketRelease": "after_close",
    "LastUpdate": "2023-08-14T11:41:00"
  },
  {
    "Date": "2016-07-26",
    "Symbol": "AAPL:US",
    "Name": "Apple",
    "Actual": "0.36",
    "ActualValue": 0.36,
    "Forecast": "0.35",
    "ForecastValue": 0.35,
    "Previous": "0.46",
    "PreviousValue": 0.46,
    "Revenue": "42.36B",
    "RevenueValue": 42360000000.0,
    "RevenueForecast": "",
    "RevenueForecastValue": null,
    "RevenuePrevious": "49.61B",
    "RevenuePreviousValue": 49610000000.0,
    "MarketCapUSD": 2712851900000,
    "FiscalTag": "FY2016Q3",
    "FiscalReference": "Q3",
    "CalendarReference": "2016-06-30",
    "Country": "United States",
    "Currency": "USD",
    "Importance": 3,
    "Session": 21,
    "MarketRelease": "after_close",
    "LastUpdate": "2023-08-14T11:41:00"
  }
]

https://api.tradingeconomics.com/earnings-revenues/symbol/{symbol}?c={your_api_key}&d1=yyyy-mm-dd&d2=yyyy-mm-dd&f=csv

Date,Symbol,Name,Actual,ActualValue,Forecast,ForecastValue,Previous,PreviousValue,Revenue,RevenueValue,RevenueForecast,RevenueForecastValue,RevenuePrevious,RevenuePreviousValue,MarketCapUSD,FiscalTag,FiscalReference,CalendarReference,Country,Currency,Importance,Session,MarketRelease,LastUpdate
2016-01-26,AAPL:US,Apple,0.82,0.82,0.81,0.81,0.77,0.77,75.87B,75870000000,,,74.60B,74600000000,2712851900000,FY2016Q1,Q1,2015-12-31,United States,USD,3,21,after_close,8/14/2023 11:41:00 AM
2016-04-26,AAPL:US,Apple,0.48,0.48,0.50,0.5,0.58,0.58,50.56B,50560000000,,,58.01B,58010000000,2712851900000,FY2016Q2,Q2,2016-03-31,United States,USD,3,21,after_close,8/14/2023 11:41:00 AM
2016-07-26,AAPL:US,Apple,0.36,0.36,0.35,0.35,0.46,0.46,42.36B,42360000000,,,49.61B,49610000000,2712851900000,FY2016Q3,Q3,2016-06-30,United States,USD,3,21,after_close,8/14/2023 11:41:00 AM

https://api.tradingeconomics.com/earnings-revenues/symbol/{symbol}?c={your_api_key}&d1=yyyy-mm-dd&d2=yyyy-mm-dd

Date	Symbol	Name	Actual	ActualValue	Previous	PreviousValue	Revenue	RevenueValue	RevenuePrevious	RevenuePreviousValue	MarketCapUSD	FiscalTag	FiscalReference	CalendarReference	Country	Currency	Importance	Session	MarketRelease	LastUpdate
2016-01-26	AAPL:US	Apple	3.28	3.28	0.77	0.77	75.87B	75870000000	74.60B	74600000000	3093777000000	FY2016Q1	Q1	2015-12-31	United States	USD	3	21	after_close	12/12/2022 5:42:00 AM
2016-04-26	AAPL:US	Apple	1.9	1.9	0.58	0.58	50.56B	50560000000	58.01B	58010000000	3093777000000	FY2016Q2	Q2	2016-03-31	United States	USD	3	21	after_close	12/12/2022 5:42:00 AM
2016-07-26	AAPL:US	Apple	1.42	1.42	0.46	0.46	42.36B	42360000000	49.61B	49610000000	3093777000000	FY2016Q3	Q3	2016-06-30	United States	USD	3	21	after_close	12/12/2022 5:42:00 AM

By country

Using Requests:

import requests
your_api_key = 'your_api_key'
url = f'https://api.tradingeconomics.com/earnings-revenues/country/mexico?c={your_api_key}'
data = requests.get(url).json()
print(data)

Or using our package:

te.getEarnings(country = 'mexico')

Using Requests:

const axios = require('axios');
(async () => {
    const your_api_key = 'your_api_key'
    const response = await axios.get(`https://api.tradingeconomics.com/earnings-revenues/country/mexico?c=${your_api_key}`)
    console.log(response.data)
})()

Or using our package:

data = te.getEarnings(country = 'mexico').then(function(data){
  console.log(data)     
});

Using Requests:

new HttpRequestMessage(new HttpMethod("GET"), "https://api.tradingeconomics.com/earnings-revenues/country/mexico?c=your_api_key");

https://api.tradingeconomics.com/earnings-revenues/country/{country}?c={your_api_key}&f=json

[
  {
    "Date": "2023-10-18",
    "Symbol": "ALFAA:MM",
    "Name": "Alfa",
    "Actual": "",
    "ActualValue": null,
    "Forecast": "",
    "ForecastValue": null,
    "Previous": "0.99",
    "PreviousValue": 0.99,
    "Revenue": "",
    "RevenueValue": null,
    "RevenueForecast": "",
    "RevenueForecastValue": null,
    "RevenuePrevious": "98.29B",
    "RevenuePreviousValue": 98290000000.0,
    "MarketCapUSD": 3103881530,
    "FiscalTag": "FY2023Q3",
    "FiscalReference": "Q3",
    "CalendarReference": "2023-09-30",
    "Country": "Mexico",
    "Currency": "MXN",
    "Importance": 1,
    "Session": 24,
    "MarketRelease": "by_day_end",
    "LastUpdate": "2023-04-21T18:57:00"
  },
  {
    "Date": "2023-10-18",
    "Symbol": "GRUMAB:MM",
    "Name": "GRUMA",
    "Actual": "",
    "ActualValue": null,
    "Forecast": "4.36",
    "ForecastValue": 4.36,
    "Previous": "4.24",
    "PreviousValue": 4.24,
    "Revenue": "",
    "RevenueValue": null,
    "RevenueForecast": "29.14B",
    "RevenueForecastValue": 29140000000.0,
    "RevenuePrevious": "1.44B",
    "RevenuePreviousValue": 1440000000.0,
    "MarketCapUSD": 6238738122,
    "FiscalTag": "FY2023Q3",
    "FiscalReference": "Q3",
    "CalendarReference": "2023-09-30",
    "Country": "Mexico",
    "Currency": "MXN",
    "Importance": 1,
    "Session": 24,
    "MarketRelease": "by_day_end",
    "LastUpdate": "2023-09-27T18:02:00"
  },
  {
    "Date": "2023-10-19",
    "Symbol": "GFNORTEO:MM",
    "Name": "Banorte",
    "Actual": "",
    "ActualValue": null,
    "Forecast": "4.66",
    "ForecastValue": 4.66,
    "Previous": "4.00",
    "PreviousValue": 4.0,
    "Revenue": "",
    "RevenueValue": null,
    "RevenueForecast": "33.66B",
    "RevenueForecastValue": 33659999999.999996,
    "RevenuePrevious": "49.11B",
    "RevenuePreviousValue": 49110000000.0,
    "MarketCapUSD": 24273536951,
    "FiscalTag": "FY2023Q3",
    "FiscalReference": "Q3",
    "CalendarReference": "2023-09-30",
    "Country": "Mexico",
    "Currency": "MXN",
    "Importance": 2,
    "Session": 24,
    "MarketRelease": "by_day_end",
    "LastUpdate": "2023-09-26T13:41:00"
  },
  {
    "Date": "2023-10-19",
    "Symbol": "ALPEKA:MM",
    "Name": "Alpek",
    "Actual": "",
    "ActualValue": null,
    "Forecast": "0.46",
    "ForecastValue": 0.46,
    "Previous": "0.05",
    "PreviousValue": 0.05,
    "Revenue": "",
    "RevenueValue": null,
    "RevenueForecast": "35.08B",
    "RevenueForecastValue": 35080000000.0,
    "RevenuePrevious": "59.75B",
    "RevenuePreviousValue": 59750000000.0,
    "MarketCapUSD": 1764022385,
    "FiscalTag": "FY2023Q3",
    "FiscalReference": "Q3",
    "CalendarReference": "2023-09-30",
    "Country": "Mexico",
    "Currency": "MXN",
    "Importance": 1,
    "Session": 24,
    "MarketRelease": "by_day_end",
    "LastUpdate": "2023-09-26T13:41:00"
  },
  {
    "Date": "2023-10-19",
    "Symbol": "KIMBERA:MM",
    "Name": "Kimberly-Clark de M&eacute;xico",
    "Actual": "",
    "ActualValue": null,
    "Forecast": "0.58",
    "ForecastValue": 0.58,
    "Previous": "0.40",
    "PreviousValue": 0.4,
    "Revenue": "",
    "RevenueValue": null,
    "RevenueForecast": "13.61B",
    "RevenueForecastValue": 13610000000.0,
    "RevenuePrevious": "12.79B",
    "RevenuePreviousValue": 12790000000.0,
    "MarketCapUSD": 3161983285,
    "FiscalTag": "FY2023Q3",
    "FiscalReference": "Q3",
    "CalendarReference": "2023-09-30",
    "Country": "Mexico",
    "Currency": "MXN",
    "Importance": 1,
    "Session": 24,
    "MarketRelease": "by_day_end",
    "LastUpdate": "2023-09-26T13:41:00"
  }
]

https://api.tradingeconomics.com/earnings-revenues/country/{country}?c={your_api_key}&f=csv

Date,Symbol,Name,Actual,ActualValue,Forecast,ForecastValue,Previous,PreviousValue,Revenue,RevenueValue,RevenueForecast,RevenueForecastValue,RevenuePrevious,RevenuePreviousValue,MarketCapUSD,FiscalTag,FiscalReference,CalendarReference,Country,Currency,Importance,Session,MarketRelease,LastUpdate
2023-10-18,ALFAA:MM,Alfa,,,,,0.99,0.99,,,,,98.29B,98290000000,3103881530,FY2023Q3,Q3,2023-09-30,Mexico,MXN,1,24,by_day_end,4/21/2023 6:57:00 PM
2023-10-18,GRUMAB:MM,GRUMA,,,4.36,4.36,4.24,4.24,,,29.14B,29140000000,1.44B,1440000000,6238738122,FY2023Q3,Q3,2023-09-30,Mexico,MXN,1,24,by_day_end,9/27/2023 6:02:00 PM
2023-10-19,GFNORTEO:MM,Banorte,,,4.66,4.66,4.00,4,,,33.66B,33660000000,49.11B,49110000000,24273536951,FY2023Q3,Q3,2023-09-30,Mexico,MXN,2,24,by_day_end,9/26/2023 1:41:00 PM
2023-10-19,ALPEKA:MM,Alpek,,,0.46,0.46,0.05,0.05,,,35.08B,35080000000,59.75B,59750000000,1764022385,FY2023Q3,Q3,2023-09-30,Mexico,MXN,1,24,by_day_end,9/26/2023 1:41:00 PM
2023-10-19,KIMBERA:MM,Kimberly-Clark de M&eacute;xico,,,0.58,0.58,0.40,0.4,,,13.61B,13610000000,12.79B,12790000000,3161983285,FY2023Q3,Q3,2023-09-30,Mexico,MXN,1,24,by_day_end,9/26/2023 1:41:00 PM

https://api.tradingeconomics.com/earnings-revenues/country/{country}?c={your_api_key}

Date	Symbol	Name	Forecast	ForecastValue	Previous	PreviousValue	RevenueForecast	RevenueForecastValue	RevenuePrevious	RevenuePreviousValue	MarketCapUSD	FiscalTag	FiscalReference	CalendarReference	Country	Currency	Importance	Session	MarketRelease	LastUpdate
2023-07-10	TV:US	Grupo Televisa Sab	0.04	0.04	0.06	0.06	1.05B	1050000000	923.00M	923000000	2822900000	FY2023Q2	Q2	2023-06-30	Mexico	USD	1	21	after_close	7/7/2023 8:46:00 PM
2023-07-18	ALFAA:MM	Alfa			0.99	0.99			96.87B	96870000000	2938204886	FY2023Q2	Q2	2023-06-30	Mexico	MXN	1	24	by_day_end	5/26/2023 10:56:00 AM
2023-07-18	ALPEKA:MM	Alpek			0.14	0.14			56.40B	56400000000	2171026546	FY2023Q2	Q2	2023-06-30	Mexico	MXN	1	24	by_day_end	5/26/2023 10:55:00 AM

By country and date

Using Requests:

import requests
your_api_key = 'your_api_key'
url = f'https://api.tradingeconomics.com/earnings-revenues/country/mexico?d1=2016-01-01&d2=2023-12-31&c={your_api_key}'
data = requests.get(url).json()
print(data)

Or using our package:

te.getEarnings(country = 'mexico', initDate='2016-01-01', endDate='2023-12-31')

Using Requests:

const axios = require('axios');
(async () => {
    const your_api_key = 'your_api_key'
    const response = await axios.get(`https://api.tradingeconomics.com/earnings-revenues/country/mexico?d1=2016-01-01&d2=2023-12-31&c=${your_api_key}`)
    console.log(response.data)
})()

Or using our package:

data = te.getEarnings(country = 'mexico', start_date = '2016-01-01', end_date = '2023-12-31').then(function(data){
  console.log(data)     
});

Using Requests:

new HttpRequestMessage(new HttpMethod("GET"), "https://api.tradingeconomics.com/earnings-revenues/country/mexico?d1=2016-01-01&d2=2023-12-31&c=your_api_key");

https://api.tradingeconomics.com/earnings-revenues/country/{country}?c={your_api_key}&d1=yyyy-mm-dd&d2=yyyy-mm-dd&f=json

[
  {
    "Date": "2016-01-28",
    "Symbol": "GFNORTEO:MM",
    "Name": "Banorte",
    "Actual": "1.78",
    "ActualValue": 1.78,
    "Forecast": "1.74",
    "ForecastValue": 1.74,
    "Previous": "1.38",
    "PreviousValue": 1.38,
    "Revenue": "22.68B",
    "RevenueValue": 22680000000.0,
    "RevenueForecast": "",
    "RevenueForecastValue": null,
    "RevenuePrevious": "23.50B",
    "RevenuePreviousValue": 23500000000.0,
    "MarketCapUSD": 24273536951,
    "FiscalTag": "FY2015Q4",
    "FiscalReference": "Q4",
    "CalendarReference": "2015-12-31",
    "Country": "Mexico",
    "Currency": "MXN",
    "Importance": 2,
    "Session": 24,
    "MarketRelease": "by_day_end",
    "LastUpdate": "2023-08-14T15:50:00"
  },
  {
    "Date": "2016-02-02",
    "Symbol": "KIMBERA:MM",
    "Name": "Kimberly-Clark de M&eacute;xico",
    "Actual": "0.39",
    "ActualValue": 0.39,
    "Forecast": "0.36",
    "ForecastValue": 0.36,
    "Previous": "0.29",
    "PreviousValue": 0.29,
    "Revenue": "8.48B",
    "RevenueValue": 8480000000.0,
    "RevenueForecast": "",
    "RevenueForecastValue": null,
    "RevenuePrevious": "7.64B",
    "RevenuePreviousValue": 7640000000.0,
    "MarketCapUSD": 3161983285,
    "FiscalTag": "FY2015Q4",
    "FiscalReference": "Q4",
    "CalendarReference": "2015-12-31",
    "Country": "Mexico",
    "Currency": "MXN",
    "Importance": 1,
    "Session": 24,
    "MarketRelease": "by_day_end",
    "LastUpdate": "2023-08-14T16:22:00"
  },
  {
    "Date": "2016-02-04",
    "Symbol": "CX:US",
    "Name": "Cemex",
    "Actual": "0.06",
    "ActualValue": 0.06,
    "Forecast": "-0.01",
    "ForecastValue": -0.01,
    "Previous": "-0.12",
    "PreviousValue": -0.12,
    "Revenue": "3.08B",
    "RevenueValue": 3080000000.0,
    "RevenueForecast": "",
    "RevenueForecastValue": null,
    "RevenuePrevious": "3.46B",
    "RevenuePreviousValue": 3460000000.0,
    "MarketCapUSD": 9778800000,
    "FiscalTag": "FY2015Q4",
    "FiscalReference": "Q4",
    "CalendarReference": "2015-12-31",
    "Country": "Mexico",
    "Currency": "USD",
    "Importance": 2,
    "Session": 13,
    "MarketRelease": "before_open",
    "LastUpdate": "2023-08-14T15:18:00"
  },
  {
    "Date": "2016-02-10",
    "Symbol": "GMEXICOB:MM",
    "Name": "Grupo M&eacute;xico",
    "Actual": "0.02",
    "ActualValue": 0.02,
    "Forecast": "0.04",
    "ForecastValue": 0.04,
    "Previous": "0.05",
    "PreviousValue": 0.05,
    "Revenue": "1.99B",
    "RevenueValue": 1990000000.0,
    "RevenueForecast": "",
    "RevenueForecastValue": null,
    "RevenuePrevious": "2.29B",
    "RevenuePreviousValue": 2290000000.0,
    "MarketCapUSD": 36952525663,
    "FiscalTag": "FY2015Q4",
    "FiscalReference": "Q4",
    "CalendarReference": "2015-12-31",
    "Country": "Mexico",
    "Currency": "USD",
    "Importance": 3,
    "Session": 24,
    "MarketRelease": "by_day_end",
    "LastUpdate": "2023-08-14T15:51:00"
  },
  {
    "Date": "2016-02-10",
    "Symbol": "ALFAA:MM",
    "Name": "Alfa",
    "Actual": "0.03",
    "ActualValue": 0.03,
    "Forecast": "0.29",
    "ForecastValue": 0.29,
    "Previous": "1.88",
    "PreviousValue": 1.88,
    "Revenue": "65.23B",
    "RevenueValue": 65230000000.00001,
    "RevenueForecast": "",
    "RevenueForecastValue": null,
    "RevenuePrevious": "64.22B",
    "RevenuePreviousValue": 64220000000.0,
    "MarketCapUSD": 3103881530,
    "FiscalTag": "FY2015Q4",
    "FiscalReference": "Q4",
    "CalendarReference": "2015-12-31",
    "Country": "Mexico",
    "Currency": "MXN",
    "Importance": 1,
    "Session": 24,
    "MarketRelease": "by_day_end",
    "LastUpdate": "2023-08-14T12:59:00"
  }
]

https://api.tradingeconomics.com/earnings-revenues/country/{country}?c={your_api_key}&d1=yyyy-mm-dd&d2=yyyy-mm-dd&f=csv

Date,Symbol,Name,Actual,ActualValue,Forecast,ForecastValue,Previous,PreviousValue,Revenue,RevenueValue,RevenueForecast,RevenueForecastValue,RevenuePrevious,RevenuePreviousValue,MarketCapUSD,FiscalTag,FiscalReference,CalendarReference,Country,Currency,Importance,Session,MarketRelease,LastUpdate
2016-01-28,GFNORTEO:MM,Banorte,1.78,1.78,1.74,1.74,1.38,1.38,22.68B,22680000000,,,23.50B,23500000000,24273536951,FY2015Q4,Q4,2015-12-31,Mexico,MXN,2,24,by_day_end,8/14/2023 3:50:00 PM
2016-02-02,KIMBERA:MM,Kimberly-Clark de M&eacute;xico,0.39,0.39,0.36,0.36,0.29,0.29,8.48B,8480000000,,,7.64B,7640000000,3161983285,FY2015Q4,Q4,2015-12-31,Mexico,MXN,1,24,by_day_end,8/14/2023 4:22:00 PM
2016-02-04,CX:US,Cemex,0.06,0.06,-0.01,-0.01,-0.12,-0.12,3.08B,3080000000,,,3.46B,3460000000,9778800000,FY2015Q4,Q4,2015-12-31,Mexico,USD,2,13,before_open,8/14/2023 3:18:00 PM
2016-02-10,GMEXICOB:MM,Grupo M&eacute;xico,0.02,0.02,0.04,0.04,0.05,0.05,1.99B,1990000000,,,2.29B,2290000000,36952525663,FY2015Q4,Q4,2015-12-31,Mexico,USD,3,24,by_day_end,8/14/2023 3:51:00 PM
2016-02-10,ALFAA:MM,Alfa,0.03,0.03,0.29,0.29,1.88,1.88,65.23B,65230000000,,,64.22B,64220000000,3103881530,FY2015Q4,Q4,2015-12-31,Mexico,MXN,1,24,by_day_end,8/14/2023 12:59:00 PM

https://api.tradingeconomics.com/earnings-revenues/country/{country}?c={your_api_key}&d1=yyyy-mm-dd&d2=yyyy-mm-dd

Date	Symbol	Name	Actual	ActualValue	Previous	PreviousValue	Revenue	RevenueValue	RevenuePrevious	RevenuePreviousValue	MarketCapUSD	FiscalTag	FiscalReference	CalendarReference	Country	Currency	Importance	Session	MarketRelease	LastUpdate
2016-02-23	KOF:US	FEMSA	0.95	0.95	1.06	1.06					17280900000	FY2015Q4	Q4	2015-12-31	Mexico	MXN	1	21	after_close	12/11/2022 11:24:00 PM
2016-02-29	SIM:US	Grupo Simec Sab De Cv	2.04	2.04	0.04	0.04					17404900000	FY2015Q4	Q4	2015-12-31	Mexico	MXN	1	21	after_close	12/12/2022 3:26:00 AM
2016-03-31	AC:MM	Arca Continental	4.80	4.8	0.81	0.81	21.27B	21270000000	15.27B	15270000000	17871365894	FY2016Q1	Q1	2016-03-31	Mexico	MXN	2		by_day_end	1/21/2023 10:47:00 AM

By index

Using Requests:

import requests
your_api_key = 'your_api_key'
url = f'https://api.tradingeconomics.com/earnings-revenues/index/ndx:ind?c={your_api_key}'
data = requests.get(url).json()
print(data)

Or using our package:

te.getEarnings(index = 'ndx:ind')

Using Requests:

const axios = require('axios');
(async () => {
    const your_api_key = 'your_api_key'
    const response = await axios.get(`https://api.tradingeconomics.com/earnings-revenues/index/ndx:ind?c=${your_api_key}`)
    console.log(response.data)
})()

Or using our package:

data = te.getEarnings(index = 'ndx:ind').then(function(data){
  console.log(data)     
});

Using Requests:

new HttpRequestMessage(new HttpMethod("GET"), "https://api.tradingeconomics.com/earnings-revenues/index/ndx:ind?c=your_api_key");

https://api.tradingeconomics.com/earnings-revenues/index/{index}?c={your_api_key}&f=json

[
  {
    "Date": "2023-09-05",
    "Symbol": "ZS:US",
    "Name": "Zscaler",
    "Actual": "0.64",
    "ActualValue": 0.64,
    "Forecast": "0.49",
    "ForecastValue": 0.49,
    "Previous": "0.25",
    "PreviousValue": 0.25,
    "Revenue": "455M",
    "RevenueValue": 455000000.0,
    "RevenueForecast": "430.38M",
    "RevenueForecastValue": 430380000.0,
    "RevenuePrevious": "318.06M",
    "RevenuePreviousValue": 318060000.0,
    "MarketCapUSD": 23519115193,
    "FiscalTag": "FY2023Q4",
    "FiscalReference": "Q4",
    "CalendarReference": "2023-07-31",
    "Country": "United States",
    "Currency": "USD",
    "Importance": 2,
    "Session": 21,
    "MarketRelease": "after_close",
    "LastUpdate": "2023-09-27T13:36:00"
  },
  {
    "Date": "2023-09-14",
    "Symbol": "ADBE:US",
    "Name": "Adobe Systems",
    "Actual": "4.09",
    "ActualValue": 4.09,
    "Forecast": "3.97",
    "ForecastValue": 3.97,
    "Previous": "3.4",
    "PreviousValue": 3.4,
    "Revenue": "4.89B",
    "RevenueValue": 4890000000.0,
    "RevenueForecast": "4.87B",
    "RevenueForecastValue": 4870000000.0,
    "RevenuePrevious": "4.43B",
    "RevenuePreviousValue": 4430000000.0,
    "MarketCapUSD": 235821800000,
    "FiscalTag": "FY2023Q3",
    "FiscalReference": "Q3",
    "CalendarReference": "2023-08-31",
    "Country": "United States",
    "Currency": "USD",
    "Importance": 3,
    "Session": 21,
    "MarketRelease": "after_close",
    "LastUpdate": "2023-09-27T13:39:00"
  },
  {
    "Date": "2023-09-14",
    "Symbol": "CPRT:US",
    "Name": "Copart",
    "Actual": "0.34",
    "ActualValue": 0.34,
    "Forecast": "0.32",
    "ForecastValue": 0.32,
    "Previous": "0.56",
    "PreviousValue": 0.56,
    "Revenue": "997.6M",
    "RevenueValue": 997600000.0,
    "RevenueForecast": "962.78M",
    "RevenueForecastValue": 962780000.0,
    "RevenuePrevious": "883.39M",
    "RevenuePreviousValue": 883390000.0,
    "MarketCapUSD": 41585000000,
    "FiscalTag": "FY2023Q4",
    "FiscalReference": "Q4",
    "CalendarReference": "2023-07-31",
    "Country": "United States",
    "Currency": "USD",
    "Importance": 3,
    "Session": 21,
    "MarketRelease": "after_close",
    "LastUpdate": "2023-09-27T13:39:00"
  }
]

https://api.tradingeconomics.com/earnings-revenues/index/{index}?c={your_api_key}&f=csv

Date,Symbol,Name,Actual,ActualValue,Forecast,ForecastValue,Previous,PreviousValue,Revenue,RevenueValue,RevenueForecast,RevenueForecastValue,RevenuePrevious,RevenuePreviousValue,MarketCapUSD,FiscalTag,FiscalReference,CalendarReference,Country,Currency,Importance,Session,MarketRelease,LastUpdate
2023-09-05,ZS:US,Zscaler,0.64,0.64,0.49,0.49,0.25,0.25,455M,455000000,430.38M,430380000,318.06M,318060000,23519115193,FY2023Q4,Q4,2023-07-31,United States,USD,2,21,after_close,9/27/2023 1:36:00 PM
2023-09-14,ADBE:US,Adobe Systems,4.09,4.09,3.97,3.97,3.4,3.4,4.89B,4890000000,4.87B,4870000000,4.43B,4430000000,235821800000,FY2023Q3,Q3,2023-08-31,United States,USD,3,21,after_close,9/27/2023 1:39:00 PM
2023-09-14,CPRT:US,Copart,0.34,0.34,0.32,0.32,0.56,0.56,997.6M,997600000,962.78M,962780000,883.39M,883390000,41585000000,FY2023Q4,Q4,2023-07-31,United States,USD,3,21,after_close,9/27/2023 1:39:00 PM

https://api.tradingeconomics.com/earnings-revenues/index/{index}?c={your_api_key}

Date	Symbol	Name	Actual	ActualValue	Forecast	ForecastValue	Previous	PreviousValue	Revenue	RevenueValue	RevenueForecast	RevenueForecastValue	RevenuePrevious	RevenuePreviousValue	MarketCapUSD	FiscalTag	FiscalReference	CalendarReference	Country	Currency	Importance	Session	MarketRelease	LastUpdate
2023-06-14	ADBE:US	Adobe Systems					3.35	3.35					4.39B	4390000000	237552100000	FY2023Q2	Q2	2023-03-31	United States	USD	3	21	after_close	4/21/2023 6:57:00 PM
2023-06-15	ADBE:US	Adobe Systems	3.91	3.91	3.79	3.79	3.35	3.35	4.82B	4820000000	4.77B	4770000000	4.39B	4390000000	237552100000	FY2023Q2	Q2	2023-03-31	United States	USD	3	21	after_close	6/15/2023 8:30:00 PM
2023-06-27	WBA:US	Walgreens Boots Alliance	1.00	1	1.08	1.08			35.4B	35400000000	34.24B	34240000000			26498900000	FY2023Q3	Q3	2023-03-31	United States	USD	2	13	before_open	7/1/2023 12:10:00 AM

By index and date

Using Requests:

import requests
your_api_key = 'your_api_key'
url = f'https://api.tradingeconomics.com/earnings-revenues/index/ndx:ind?d1=2016-01-01&d2=2023-12-31&c={your_api_key}'
data = requests.get(url).json()
print(data)

Or using our package:

te.getEarnings(index = 'ndx:ind', initDate='2016-01-01', endDate='2023-12-31')

Using Requests:

const axios = require('axios');
(async () => {
    const your_api_key = 'your_api_key'
    const response = await axios.get(`https://api.tradingeconomics.com/earnings-revenues/index/ndx:ind?d1=2016-01-01&d2=2023-12-31&c=${your_api_key}`)
    console.log(response.data)
})()

Or using our package:

data = te.getEarnings(index = 'ndx:ind', start_date = '2016-01-01', end_date = '2023-12-31').then(function(data){
  console.log(data)     
});

Using Requests:

new HttpRequestMessage(new HttpMethod("GET"), "https://api.tradingeconomics.com/earnings-revenues/index/ndx:ind?d1=2016-01-01&d2=2023-12-31&c=your_api_key");

https://api.tradingeconomics.com/earnings-revenues/index/{index}?c={your_api_key}&d1=yyyy-mm-dd&d2=yyyy-mm-dd&f=json

[
  {
    "Date": "2016-01-07",
    "Symbol": "WBA:US",
    "Name": "Walgreens Boots Alliance",
    "Actual": "1.03",
    "ActualValue": 1.03,
    "Forecast": "0.96",
    "ForecastValue": 0.96,
    "Previous": "",
    "PreviousValue": null,
    "Revenue": "29.03B",
    "RevenueValue": 29030000000.0,
    "RevenueForecast": "",
    "RevenueForecastValue": null,
    "RevenuePrevious": "",
    "RevenuePreviousValue": null,
    "MarketCapUSD": 19017600000,
    "FiscalTag": "FY2016Q1",
    "FiscalReference": "Q1",
    "CalendarReference": "2015-11-30",
    "Country": "United States",
    "Currency": "USD",
    "Importance": 2,
    "Session": 13,
    "MarketRelease": "before_open",
    "LastUpdate": "2023-08-14T12:16:00"
  },
  {
    "Date": "2016-01-12",
    "Symbol": "CSX:US",
    "Name": "CSX",
    "Actual": "0.16",
    "ActualValue": 0.16,
    "Forecast": "0.15",
    "ForecastValue": 0.15,
    "Previous": "0.16",
    "PreviousValue": 0.16,
    "Revenue": "",
    "RevenueValue": null,
    "RevenueForecast": "",
    "RevenueForecastValue": null,
    "RevenuePrevious": "",
    "RevenuePreviousValue": null,
    "MarketCapUSD": 60972400000,
    "FiscalTag": "FY2015Q4",
    "FiscalReference": "Q4",
    "CalendarReference": "2015-12-31",
    "Country": "United States",
    "Currency": "USD",
    "Importance": 3,
    "Session": 21,
    "MarketRelease": "after_close",
    "LastUpdate": "2023-08-14T11:50:00"
  },
  {
    "Date": "2016-01-14",
    "Symbol": "INTC:US",
    "Name": "Intel",
    "Actual": "0.74",
    "ActualValue": 0.74,
    "Forecast": "0.63",
    "ForecastValue": 0.63,
    "Previous": "0.74",
    "PreviousValue": 0.74,
    "Revenue": "14.91B",
    "RevenueValue": 14910000000.0,
    "RevenueForecast": "",
    "RevenueForecastValue": null,
    "RevenuePrevious": "14.72B",
    "RevenuePreviousValue": 14720000000.0,
    "MarketCapUSD": 148799600000,
    "FiscalTag": "FY2015Q4",
    "FiscalReference": "Q4",
    "CalendarReference": "2015-12-31",
    "Country": "United States",
    "Currency": "USD",
    "Importance": 3,
    "Session": 13,
    "MarketRelease": "before_open",
    "LastUpdate": "2023-08-14T12:00:00"
  }
]

https://api.tradingeconomics.com/earnings-revenues/index/{index}?c={your_api_key}&d1=yyyy-mm-dd&d2=yyyy-mm-dd&f=csv

Date,Symbol,Name,Actual,ActualValue,Forecast,ForecastValue,Previous,PreviousValue,Revenue,RevenueValue,RevenueForecast,RevenueForecastValue,RevenuePrevious,RevenuePreviousValue,MarketCapUSD,FiscalTag,FiscalReference,CalendarReference,Country,Currency,Importance,Session,MarketRelease,LastUpdate
2016-01-07,WBA:US,Walgreens Boots Alliance,1.03,1.03,0.96,0.96,,,29.03B,29030000000,,,,,19017600000,FY2016Q1,Q1,2015-11-30,United States,USD,2,13,before_open,8/14/2023 12:16:00 PM
2016-01-12,CSX:US,CSX,0.16,0.16,0.15,0.15,0.16,0.16,,,,,,,60972400000,FY2015Q4,Q4,2015-12-31,United States,USD,3,21,after_close,8/14/2023 11:50:00 AM
2016-01-14,INTC:US,Intel,0.74,0.74,0.63,0.63,0.74,0.74,14.91B,14910000000,,,14.72B,14720000000,148799600000,FY2015Q4,Q4,2015-12-31,United States,USD,3,13,before_open,8/14/2023 12:00:00 PM

https://api.tradingeconomics.com/earnings-revenues/index/{index}?c={your_api_key}&d1=yyyy-mm-dd&d2=yyyy-mm-dd

Date	Symbol	Name	Actual	ActualValue	Previous	PreviousValue	MarketCapUSD	FiscalTag	FiscalReference	CalendarReference	Country	Currency	Importance	Session	MarketRelease	LastUpdate
2016-01-07	WBA:US	Walgreens Boots Alliance	1.03	1.03	1.18	1.18	26498900000	FY2016Q2	Q2	2015-12-31	United States	USD	3	21	after_close	12/11/2022 1:03:00 PM
2016-01-12	CSX:US	CSX	0.48	0.48	0.16	0.16	69049300000	FY2015Q4	Q4	2015-12-31	United States	USD	3	21	after_close	12/12/2022 1:16:00 AM
2016-01-14	INTC:US	Intel	0.74	0.74	0.74	0.74	139970900000	FY2015Q4	Q4	2015-12-31	United States	USD	3	21	after_close	12/11/2022 8:32:00 PM

Response fields

Field	Type	Description	Example
Date	string	Earnings release date in UTC	“2023-03-15”
Symbol	string	Unique Trading Economics symbol identifying the company	“ADBE:US”
Name	string	Company name	“Adobe Systems”
Actual	string	Reported earnings per share (EPS)	“3.80”
ActualValue	number	Reported earnings per share (EPS) as a numeric value	3.80
Forecast	string	Average analyst EPS forecast	“3.68”
ForecastValue	number	Average analyst EPS forecast as a numeric value	3.68
Previous	string	Previously reported EPS	“3.37”
PreviousValue	number	Previously reported EPS as a numeric value	3.37
Revenue	string	Reported company revenue	“4.66B”
RevenueValue	number	Reported Revenue as a numeric value	4660000000
RevenueForecast	string	Average analyst Revenue forecast	“4.62B”
RevenueForecastValue	number	Average analyst Revenue forecast as a numeric value	4620000000
RevenuePrevious	string	Previously reported Revenue	“4.26B”
RevenuePreviousValue	number	Previously reported Revenue as a numeric value	4260000000
MarketCapUSD	number	Company market capitalization in US dollars	173177600000
FiscalTag	string	Fiscal year and quarter tag	“FY2023Q1”
FiscalReference	string	Fiscal quarter in simplified format	“Q1”
CalendarReference	string	Calendar date for the fiscal quarter end	“2022-12-31”
Country	string	Country where the company is based	“United States”
Currency	string	Reporting currency	“USD”
Importance	number	Indicator importance: 1 = low, 2 = medium, 3 = high	3
Session	number	Expected release hour in UTC	21
MarketRelease	string	Type of release: `after_close`, `before_open`, or `by_day_end`	“after_close”
LastUpdate	string	Timestamp of the latest data update in UTC	“2023-03-18T16:15:00”