Earnings 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
api_key = 'YOUR_API_KEY'
url = f'https://api.tradingeconomics.com/earnings-revenues?c={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 api_key = 'YOUR_API_KEY'
const response = await axios.get(`https://api.tradingeconomics.com/earnings-revenues?c=${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.
| 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 |
[{"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"}]
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
<ArrayOfMarkets.EarningsRevenuesItem xmlns:i="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://schemas.datacontract.org/2004/07/APILib.DB">
<Markets.EarningsRevenuesItem>
<Actual>5.11</Actual>
<ActualValue>5.11</ActualValue>
<CalendarReference>2023-06-30</CalendarReference>
<Country>China</Country>
<Currency>USD</Currency>
<Date>2023-09-04</Date>
<FiscalReference>Q2</FiscalReference>
<FiscalTag>FY2023Q2</FiscalTag>
<Forecast>3.61</Forecast>
<ForecastValue>3.61</ForecastValue>
<Importance>2</Importance>
<LastUpdate>2023-09-27T13:36:00</LastUpdate>
<MarketCapUSD>22641100000</MarketCapUSD>
<MarketRelease>after_close</MarketRelease>
<Name>Trip.com</Name>
<Previous>-0.31</Previous>
<PreviousValue>-0.31</PreviousValue>
<Revenue>11.25B</Revenue>
<RevenueForecast>10.77B</RevenueForecast>
<RevenueForecastValue>10770000000</RevenueForecastValue>
<RevenuePrevious>4.01B</RevenuePrevious>
<RevenuePreviousValue>4010000000</RevenuePreviousValue>
<RevenueValue>11250000000</RevenueValue>
<Session>21</Session>
<Symbol>TCOM:US</Symbol>
</Markets.EarningsRevenuesItem>
<Markets.EarningsRevenuesItem>
<Actual>19.37</Actual>
<ActualValue>19.37</ActualValue>
<CalendarReference>2023-06-30</CalendarReference>
<Country>South Africa</Country>
<Currency>ZAR</Currency>
<Date>2023-09-04</Date>
<FiscalReference>H2</FiscalReference>
<FiscalTag>FY2023H2</FiscalTag>
<Forecast>0.00</Forecast>
<ForecastValue>0</ForecastValue>
<Importance>1</Importance>
<LastUpdate>2023-09-22T12:51:00</LastUpdate>
<MarketCapUSD>1726756424</MarketCapUSD>
<MarketRelease>by_day_end</MarketRelease>
<Name>African Rainbow Minerals</Name>
<Previous>39.15</Previous>
<PreviousValue>39.15</PreviousValue>
<Revenue>6.72B</Revenue>
<RevenueForecast/>
<RevenueForecastValue i:nil="true"/>
<RevenuePrevious>9.85B</RevenuePrevious>
<RevenuePreviousValue>9850000000</RevenuePreviousValue>
<RevenueValue>6720000000</RevenueValue>
<Session>24</Session>
<Symbol>ARI:SJ</Symbol>
</Markets.EarningsRevenuesItem>
<Markets.EarningsRevenuesItem>
<Actual>1.07</Actual>
<ActualValue>1.07</ActualValue>
<CalendarReference>2023-07-31</CalendarReference>
<Country>United Kingdom</Country>
<Currency>USD</Currency>
<Date>2023-09-05</Date>
<FiscalReference>Q1</FiscalReference>
<FiscalTag>FY2024Q1</FiscalTag>
<Forecast>1.05</Forecast>
<ForecastValue>1.05</ForecastValue>
<Importance>2</Importance>
<LastUpdate>2023-09-22T12:42:00</LastUpdate>
<MarketCapUSD>26175459147</MarketCapUSD>
<MarketRelease>after_close</MarketRelease>
<Name>Ashtead</Name>
<Previous>0.94</Previous>
<PreviousValue>0.94</PreviousValue>
<Revenue>2.70B</Revenue>
<RevenueForecast/>
<RevenueForecastValue i:nil="true"/>
<RevenuePrevious>2.26B</RevenuePrevious>
<RevenuePreviousValue>2260000000</RevenuePreviousValue>
<RevenueValue>2700000000</RevenueValue>
<Session>16</Session>
<Symbol>AHT:LN</Symbol>
</Markets.EarningsRevenuesItem>
</ArrayOfMarkets.EarningsRevenuesItem>
By date
Using Requests:
import requests
api_key = 'YOUR_API_KEY'
url = f'https://api.tradingeconomics.com/earnings-revenues?c={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 api_key = 'YOUR_API_KEY'
const response = await axios.get(`https://api.tradingeconomics.com/earnings-revenues?c=${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
api_key = 'YOUR_API_KEY'
url = f'https://api.tradingeconomics.com/earnings-revenues?c={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 api_key = 'YOUR_API_KEY'
const response = await axios.get(`https://api.tradingeconomics.com/earnings-revenues?c=${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");
/earnings-revenues?d1=yyyy-mm-dd&d2=yyy-mm-dd
| 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 | 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 |
/earnings-revenues?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"}]
/earnings-revenues?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
/earnings-revenues?d1=yyyy-mm-dd&d2=yyy-mm-dd?f=xml
<ArrayOfMarkets.EarningsRevenuesItem xmlns:i="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://schemas.datacontract.org/2004/07/APILib.DB">
<Markets.EarningsRevenuesItem>
<Actual>1.38</Actual>
<ActualValue>1.38</ActualValue>
<CalendarReference>2016-11-30</CalendarReference>
<Country>United States</Country>
<Currency>USD</Currency>
<Date>2017-01-04</Date>
<FiscalReference>Q1</FiscalReference>
<FiscalTag>FY2017Q1</FiscalTag>
<Forecast>1.57</Forecast>
<ForecastValue>1.57</ForecastValue>
<Importance>1</Importance>
<LastUpdate>2023-08-14T18:14:00</LastUpdate>
<MarketCapUSD>3033500000</MarketCapUSD>
<MarketRelease>before_open</MarketRelease>
<Name>UniFirst</Name>
<Previous>1.78</Previous>
<PreviousValue>1.78</PreviousValue>
<Revenue>386.11M</Revenue>
<RevenueForecast/>
<RevenueForecastValue i:nil="true"/>
<RevenuePrevious>373.38M</RevenuePrevious>
<RevenuePreviousValue>373380000</RevenuePreviousValue>
<RevenueValue>386110000</RevenueValue>
<Session>13</Session>
<Symbol>UNF:US</Symbol>
</Markets.EarningsRevenuesItem>
<Markets.EarningsRevenuesItem>
<Actual>1.96</Actual>
<ActualValue>1.96</ActualValue>
<CalendarReference>2016-11-30</CalendarReference>
<Country>United States</Country>
<Currency>USD</Currency>
<Date>2017-01-05</Date>
<FiscalReference>Q3</FiscalReference>
<FiscalTag>FY2017Q3</FiscalTag>
<Forecast>1.72</Forecast>
<ForecastValue>1.72</ForecastValue>
<Importance>3</Importance>
<LastUpdate>2023-08-14T12:12:00</LastUpdate>
<MarketCapUSD>45630900000</MarketCapUSD>
<MarketRelease>after_close</MarketRelease>
<Name>Constellation Brands</Name>
<Previous>1.42</Previous>
<PreviousValue>1.42</PreviousValue>
<Revenue>1.81B</Revenue>
<RevenueForecast/>
<RevenueForecastValue i:nil="true"/>
<RevenuePrevious>1.64B</RevenuePrevious>
<RevenuePreviousValue>1640000000</RevenuePreviousValue>
<RevenueValue>1810000000</RevenueValue>
<Session>21</Session>
<Symbol>STZ:US</Symbol>
</Markets.EarningsRevenuesItem>
<Markets.EarningsRevenuesItem>
<Actual>0.18</Actual>
<ActualValue>0.18</ActualValue>
<CalendarReference>2016-12-31</CalendarReference>
<Country>Taiwan</Country>
<Currency>TWD</Currency>
<Date>2017-01-05</Date>
<FiscalReference>Q4</FiscalReference>
<FiscalTag>FY2016Q4</FiscalTag>
<Forecast>0.24</Forecast>
<ForecastValue>0.24</ForecastValue>
<Importance>2</Importance>
<LastUpdate>2023-08-14T12:36:00</LastUpdate>
<MarketCapUSD>11717835235</MarketCapUSD>
<MarketRelease>by_day_end</MarketRelease>
<Name>E Sun Financial Hldg</Name>
<Previous>0.18</Previous>
<PreviousValue>0.18</PreviousValue>
<Revenue>12.22B</Revenue>
<RevenueForecast/>
<RevenueForecastValue i:nil="true"/>
<RevenuePrevious>9.68B</RevenuePrevious>
<RevenuePreviousValue>9680000000</RevenuePreviousValue>
<RevenueValue>12220000000</RevenueValue>
<Session>24</Session>
<Symbol>2884:TT</Symbol>
</Markets.EarningsRevenuesItem>
</ArrayOfMarkets.EarningsRevenuesItem>
By symbol and date
Using Requests:
import requests
api_key = 'YOUR_API_KEY'
url = f'https://api.tradingeconomics.com/earnings-revenues/symbol/aapl:us?c={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 api_key = 'YOUR_API_KEY'
const response = await axios.get(`https://api.tradingeconomics.com/earnings-revenues/symbol/aapl:us?c=${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
api_key = 'YOUR_API_KEY'
url = f'https://api.tradingeconomics.com/earnings-revenues/symbol/aapl:us?c={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 api_key = 'YOUR_API_KEY'
const response = await axios.get(`https://api.tradingeconomics.com/earnings-revenues/symbol/aapl:us?c=${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");
/earnings-revenues/symbol/{symbol}?d1=yyyy-mm-dd&d2=yyyy-mm-dd
| 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 | 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 |
/earnings-revenues/symbol/{symbol}?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"}]
/earnings-revenues/symbol/{symbol}?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
/earnings-revenues/symbol/{symbol}?d1=yyyy-mm-dd&d2=yyyy-mm-dd?f=xml
<ArrayOfMarkets.EarningsRevenuesItem xmlns:i="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://schemas.datacontract.org/2004/07/APILib.DB">
<Markets.EarningsRevenuesItem>
<Actual>0.82</Actual>
<ActualValue>0.82</ActualValue>
<CalendarReference>2015-12-31</CalendarReference>
<Country>United States</Country>
<Currency>USD</Currency>
<Date>2016-01-26</Date>
<FiscalReference>Q1</FiscalReference>
<FiscalTag>FY2016Q1</FiscalTag>
<Forecast>0.81</Forecast>
<ForecastValue>0.81</ForecastValue>
<Importance>3</Importance>
<LastUpdate>2023-08-14T11:41:00</LastUpdate>
<MarketCapUSD>2712851900000</MarketCapUSD>
<MarketRelease>after_close</MarketRelease>
<Name>Apple</Name>
<Previous>0.77</Previous>
<PreviousValue>0.77</PreviousValue>
<Revenue>75.87B</Revenue>
<RevenueForecast/>
<RevenueForecastValue i:nil="true"/>
<RevenuePrevious>74.60B</RevenuePrevious>
<RevenuePreviousValue>74600000000</RevenuePreviousValue>
<RevenueValue>75870000000</RevenueValue>
<Session>21</Session>
<Symbol>AAPL:US</Symbol>
</Markets.EarningsRevenuesItem>
<Markets.EarningsRevenuesItem>
<Actual>0.48</Actual>
<ActualValue>0.48</ActualValue>
<CalendarReference>2016-03-31</CalendarReference>
<Country>United States</Country>
<Currency>USD</Currency>
<Date>2016-04-26</Date>
<FiscalReference>Q2</FiscalReference>
<FiscalTag>FY2016Q2</FiscalTag>
<Forecast>0.50</Forecast>
<ForecastValue>0.5</ForecastValue>
<Importance>3</Importance>
<LastUpdate>2023-08-14T11:41:00</LastUpdate>
<MarketCapUSD>2712851900000</MarketCapUSD>
<MarketRelease>after_close</MarketRelease>
<Name>Apple</Name>
<Previous>0.58</Previous>
<PreviousValue>0.58</PreviousValue>
<Revenue>50.56B</Revenue>
<RevenueForecast/>
<RevenueForecastValue i:nil="true"/>
<RevenuePrevious>58.01B</RevenuePrevious>
<RevenuePreviousValue>58010000000</RevenuePreviousValue>
<RevenueValue>50560000000</RevenueValue>
<Session>21</Session>
<Symbol>AAPL:US</Symbol>
</Markets.EarningsRevenuesItem>
<Markets.EarningsRevenuesItem>
<Actual>0.36</Actual>
<ActualValue>0.36</ActualValue>
<CalendarReference>2016-06-30</CalendarReference>
<Country>United States</Country>
<Currency>USD</Currency>
<Date>2016-07-26</Date>
<FiscalReference>Q3</FiscalReference>
<FiscalTag>FY2016Q3</FiscalTag>
<Forecast>0.35</Forecast>
<ForecastValue>0.35</ForecastValue>
<Importance>3</Importance>
<LastUpdate>2023-08-14T11:41:00</LastUpdate>
<MarketCapUSD>2712851900000</MarketCapUSD>
<MarketRelease>after_close</MarketRelease>
<Name>Apple</Name>
<Previous>0.46</Previous>
<PreviousValue>0.46</PreviousValue>
<Revenue>42.36B</Revenue>
<RevenueForecast/>
<RevenueForecastValue i:nil="true"/>
<RevenuePrevious>49.61B</RevenuePrevious>
<RevenuePreviousValue>49610000000</RevenuePreviousValue>
<RevenueValue>42360000000</RevenueValue>
<Session>21</Session>
<Symbol>AAPL:US</Symbol>
</Markets.EarningsRevenuesItem>
</ArrayOfMarkets.EarningsRevenuesItem>
By country
Using Requests:
import requests
api_key = 'YOUR_API_KEY'
url = f'https://api.tradingeconomics.com/earnings-revenues/country/mexico?c={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 api_key = 'YOUR_API_KEY'
const response = await axios.get(`https://api.tradingeconomics.com/earnings-revenues/country/mexico?c=${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");
/earnings-revenues/country/{country}
| 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2023-07-10 | 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 |
/earnings-revenues/country/{country}?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 México","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"}]
/earnings-revenues/country/{country}?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 México,,,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
/earnings-revenues/country/{country}?f=xml
<ArrayOfMarkets.EarningsRevenuesItem xmlns:i="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://schemas.datacontract.org/2004/07/APILib.DB">
<Markets.EarningsRevenuesItem>
<Actual/>
<ActualValue i:nil="true"/>
<CalendarReference>2023-09-30</CalendarReference>
<Country>Mexico</Country>
<Currency>MXN</Currency>
<Date>2023-10-18</Date>
<FiscalReference>Q3</FiscalReference>
<FiscalTag>FY2023Q3</FiscalTag>
<Forecast/>
<ForecastValue i:nil="true"/>
<Importance>1</Importance>
<LastUpdate>2023-04-21T18:57:00</LastUpdate>
<MarketCapUSD>3103881530</MarketCapUSD>
<MarketRelease>by_day_end</MarketRelease>
<Name>Alfa</Name>
<Previous>0.99</Previous>
<PreviousValue>0.99</PreviousValue>
<Revenue/>
<RevenueForecast/>
<RevenueForecastValue i:nil="true"/>
<RevenuePrevious>98.29B</RevenuePrevious>
<RevenuePreviousValue>98290000000</RevenuePreviousValue>
<RevenueValue i:nil="true"/>
<Session>24</Session>
<Symbol>ALFAA:MM</Symbol>
</Markets.EarningsRevenuesItem>
<Markets.EarningsRevenuesItem>
<Actual/>
<ActualValue i:nil="true"/>
<CalendarReference>2023-09-30</CalendarReference>
<Country>Mexico</Country>
<Currency>MXN</Currency>
<Date>2023-10-18</Date>
<FiscalReference>Q3</FiscalReference>
<FiscalTag>FY2023Q3</FiscalTag>
<Forecast>4.36</Forecast>
<ForecastValue>4.36</ForecastValue>
<Importance>1</Importance>
<LastUpdate>2023-09-27T18:02:00</LastUpdate>
<MarketCapUSD>6238738122</MarketCapUSD>
<MarketRelease>by_day_end</MarketRelease>
<Name>GRUMA</Name>
<Previous>4.24</Previous>
<PreviousValue>4.24</PreviousValue>
<Revenue/>
<RevenueForecast>29.14B</RevenueForecast>
<RevenueForecastValue>29140000000</RevenueForecastValue>
<RevenuePrevious>1.44B</RevenuePrevious>
<RevenuePreviousValue>1440000000</RevenuePreviousValue>
<RevenueValue i:nil="true"/>
<Session>24</Session>
<Symbol>GRUMAB:MM</Symbol>
</Markets.EarningsRevenuesItem>
<Markets.EarningsRevenuesItem>
<Actual/>
<ActualValue i:nil="true"/>
<CalendarReference>2023-09-30</CalendarReference>
<Country>Mexico</Country>
<Currency>MXN</Currency>
<Date>2023-10-19</Date>
<FiscalReference>Q3</FiscalReference>
<FiscalTag>FY2023Q3</FiscalTag>
<Forecast>4.66</Forecast>
<ForecastValue>4.66</ForecastValue>
<Importance>2</Importance>
<LastUpdate>2023-09-26T13:41:00</LastUpdate>
<MarketCapUSD>24273536951</MarketCapUSD>
<MarketRelease>by_day_end</MarketRelease>
<Name>Banorte</Name>
<Previous>4.00</Previous>
<PreviousValue>4</PreviousValue>
<Revenue/>
<RevenueForecast>33.66B</RevenueForecast>
<RevenueForecastValue>33659999999.999996</RevenueForecastValue>
<RevenuePrevious>49.11B</RevenuePrevious>
<RevenuePreviousValue>49110000000</RevenuePreviousValue>
<RevenueValue i:nil="true"/>
<Session>24</Session>
<Symbol>GFNORTEO:MM</Symbol>
</Markets.EarningsRevenuesItem>
<Markets.EarningsRevenuesItem>
<Actual/>
<ActualValue i:nil="true"/>
<CalendarReference>2023-09-30</CalendarReference>
<Country>Mexico</Country>
<Currency>MXN</Currency>
<Date>2023-10-19</Date>
<FiscalReference>Q3</FiscalReference>
<FiscalTag>FY2023Q3</FiscalTag>
<Forecast>0.46</Forecast>
<ForecastValue>0.46</ForecastValue>
<Importance>1</Importance>
<LastUpdate>2023-09-26T13:41:00</LastUpdate>
<MarketCapUSD>1764022385</MarketCapUSD>
<MarketRelease>by_day_end</MarketRelease>
<Name>Alpek</Name>
<Previous>0.05</Previous>
<PreviousValue>0.05</PreviousValue>
<Revenue/>
<RevenueForecast>35.08B</RevenueForecast>
<RevenueForecastValue>35080000000</RevenueForecastValue>
<RevenuePrevious>59.75B</RevenuePrevious>
<RevenuePreviousValue>59750000000</RevenuePreviousValue>
<RevenueValue i:nil="true"/>
<Session>24</Session>
<Symbol>ALPEKA:MM</Symbol>
</Markets.EarningsRevenuesItem>
<Markets.EarningsRevenuesItem>
<Actual/>
<ActualValue i:nil="true"/>
<CalendarReference>2023-09-30</CalendarReference>
<Country>Mexico</Country>
<Currency>MXN</Currency>
<Date>2023-10-19</Date>
<FiscalReference>Q3</FiscalReference>
<FiscalTag>FY2023Q3</FiscalTag>
<Forecast>0.58</Forecast>
<ForecastValue>0.58</ForecastValue>
<Importance>1</Importance>
<LastUpdate>2023-09-26T13:41:00</LastUpdate>
<MarketCapUSD>3161983285</MarketCapUSD>
<MarketRelease>by_day_end</MarketRelease>
<Name>Kimberly-Clark de México</Name>
<Previous>0.40</Previous>
<PreviousValue>0.4</PreviousValue>
<Revenue/>
<RevenueForecast>13.61B</RevenueForecast>
<RevenueForecastValue>13610000000</RevenueForecastValue>
<RevenuePrevious>12.79B</RevenuePrevious>
<RevenuePreviousValue>12790000000</RevenuePreviousValue>
<RevenueValue i:nil="true"/>
<Session>24</Session>
<Symbol>KIMBERA:MM</Symbol>
</Markets.EarningsRevenuesItem>
</ArrayOfMarkets.EarningsRevenuesItem>
By country and date
Using Requests:
import requests
api_key = 'YOUR_API_KEY'
url = f'https://api.tradingeconomics.com/earnings-revenues/country/mexico?d1=2016-01-01&d2=2023-12-31&c={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 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=${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");
/earnings-revenues/country/{country}?d1=yyyy-mm-dd&d2=yyyy-mm-dd
| 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2016-02-23 | 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 |
/earnings-revenues/country/{country}?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 México","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 México","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":65230000000.000008,"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"}]
/earnings-revenues/country/{country}?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 México,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 México,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
/earnings-revenues/country/{country}?d1=yyyy-mm-dd&d2=yyyy-mm-dd?f=xml
<ArrayOfMarkets.EarningsRevenuesItem xmlns:i="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://schemas.datacontract.org/2004/07/APILib.DB">
<Markets.EarningsRevenuesItem>
<Actual>1.78</Actual>
<ActualValue>1.78</ActualValue>
<CalendarReference>2015-12-31</CalendarReference>
<Country>Mexico</Country>
<Currency>MXN</Currency>
<Date>2016-01-28</Date>
<FiscalReference>Q4</FiscalReference>
<FiscalTag>FY2015Q4</FiscalTag>
<Forecast>1.74</Forecast>
<ForecastValue>1.74</ForecastValue>
<Importance>2</Importance>
<LastUpdate>2023-08-14T15:50:00</LastUpdate>
<MarketCapUSD>24273536951</MarketCapUSD>
<MarketRelease>by_day_end</MarketRelease>
<Name>Banorte</Name>
<Previous>1.38</Previous>
<PreviousValue>1.38</PreviousValue>
<Revenue>22.68B</Revenue>
<RevenueForecast/>
<RevenueForecastValue i:nil="true"/>
<RevenuePrevious>23.50B</RevenuePrevious>
<RevenuePreviousValue>23500000000</RevenuePreviousValue>
<RevenueValue>22680000000</RevenueValue>
<Session>24</Session>
<Symbol>GFNORTEO:MM</Symbol>
</Markets.EarningsRevenuesItem>
<Markets.EarningsRevenuesItem>
<Actual>0.39</Actual>
<ActualValue>0.39</ActualValue>
<CalendarReference>2015-12-31</CalendarReference>
<Country>Mexico</Country>
<Currency>MXN</Currency>
<Date>2016-02-02</Date>
<FiscalReference>Q4</FiscalReference>
<FiscalTag>FY2015Q4</FiscalTag>
<Forecast>0.36</Forecast>
<ForecastValue>0.36</ForecastValue>
<Importance>1</Importance>
<LastUpdate>2023-08-14T16:22:00</LastUpdate>
<MarketCapUSD>3161983285</MarketCapUSD>
<MarketRelease>by_day_end</MarketRelease>
<Name>Kimberly-Clark de México</Name>
<Previous>0.29</Previous>
<PreviousValue>0.29</PreviousValue>
<Revenue>8.48B</Revenue>
<RevenueForecast/>
<RevenueForecastValue i:nil="true"/>
<RevenuePrevious>7.64B</RevenuePrevious>
<RevenuePreviousValue>7640000000</RevenuePreviousValue>
<RevenueValue>8480000000</RevenueValue>
<Session>24</Session>
<Symbol>KIMBERA:MM</Symbol>
</Markets.EarningsRevenuesItem>
<Markets.EarningsRevenuesItem>
<Actual>0.06</Actual>
<ActualValue>0.06</ActualValue>
<CalendarReference>2015-12-31</CalendarReference>
<Country>Mexico</Country>
<Currency>USD</Currency>
<Date>2016-02-04</Date>
<FiscalReference>Q4</FiscalReference>
<FiscalTag>FY2015Q4</FiscalTag>
<Forecast>-0.01</Forecast>
<ForecastValue>-0.01</ForecastValue>
<Importance>2</Importance>
<LastUpdate>2023-08-14T15:18:00</LastUpdate>
<MarketCapUSD>9778800000</MarketCapUSD>
<MarketRelease>before_open</MarketRelease>
<Name>Cemex</Name>
<Previous>-0.12</Previous>
<PreviousValue>-0.12</PreviousValue>
<Revenue>3.08B</Revenue>
<RevenueForecast/>
<RevenueForecastValue i:nil="true"/>
<RevenuePrevious>3.46B</RevenuePrevious>
<RevenuePreviousValue>3460000000</RevenuePreviousValue>
<RevenueValue>3080000000</RevenueValue>
<Session>13</Session>
<Symbol>CX:US</Symbol>
</Markets.EarningsRevenuesItem>
<Markets.EarningsRevenuesItem>
<Actual>0.02</Actual>
<ActualValue>0.02</ActualValue>
<CalendarReference>2015-12-31</CalendarReference>
<Country>Mexico</Country>
<Currency>USD</Currency>
<Date>2016-02-10</Date>
<FiscalReference>Q4</FiscalReference>
<FiscalTag>FY2015Q4</FiscalTag>
<Forecast>0.04</Forecast>
<ForecastValue>0.04</ForecastValue>
<Importance>3</Importance>
<LastUpdate>2023-08-14T15:51:00</LastUpdate>
<MarketCapUSD>36952525663</MarketCapUSD>
<MarketRelease>by_day_end</MarketRelease>
<Name>Grupo México</Name>
<Previous>0.05</Previous>
<PreviousValue>0.05</PreviousValue>
<Revenue>1.99B</Revenue>
<RevenueForecast/>
<RevenueForecastValue i:nil="true"/>
<RevenuePrevious>2.29B</RevenuePrevious>
<RevenuePreviousValue>2290000000</RevenuePreviousValue>
<RevenueValue>1990000000</RevenueValue>
<Session>24</Session>
<Symbol>GMEXICOB:MM</Symbol>
</Markets.EarningsRevenuesItem>
<Markets.EarningsRevenuesItem>
<Actual>0.03</Actual>
<ActualValue>0.03</ActualValue>
<CalendarReference>2015-12-31</CalendarReference>
<Country>Mexico</Country>
<Currency>MXN</Currency>
<Date>2016-02-10</Date>
<FiscalReference>Q4</FiscalReference>
<FiscalTag>FY2015Q4</FiscalTag>
<Forecast>0.29</Forecast>
<ForecastValue>0.29</ForecastValue>
<Importance>1</Importance>
<LastUpdate>2023-08-14T12:59:00</LastUpdate>
<MarketCapUSD>3103881530</MarketCapUSD>
<MarketRelease>by_day_end</MarketRelease>
<Name>Alfa</Name>
<Previous>1.88</Previous>
<PreviousValue>1.88</PreviousValue>
<Revenue>65.23B</Revenue>
<RevenueForecast/>
<RevenueForecastValue i:nil="true"/>
<RevenuePrevious>64.22B</RevenuePrevious>
<RevenuePreviousValue>64220000000</RevenuePreviousValue>
<RevenueValue>65230000000.000008</RevenueValue>
<Session>24</Session>
<Symbol>ALFAA:MM</Symbol>
</Markets.EarningsRevenuesItem>
</ArrayOfMarkets.EarningsRevenuesItem>
By index
Using Requests:
import requests
api_key = 'YOUR_API_KEY'
url = f'https://api.tradingeconomics.com/earnings-revenues/index/ndx:ind?c={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 api_key = 'YOUR_API_KEY'
const response = await axios.get(`https://api.tradingeconomics.com/earnings-revenues/index/ndx:ind?c=${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");
/earnings-revenues/index/{index}
| 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 |
/earnings-revenues/index/{index}?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"}]
/earnings-revenues/index/{index}?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
/earnings-revenues/index/{index}?f=xml
<ArrayOfMarkets.EarningsRevenuesItem xmlns:i="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://schemas.datacontract.org/2004/07/APILib.DB">
<Markets.EarningsRevenuesItem>
<Actual>0.64</Actual>
<ActualValue>0.64</ActualValue>
<CalendarReference>2023-07-31</CalendarReference>
<Country>United States</Country>
<Currency>USD</Currency>
<Date>2023-09-05</Date>
<FiscalReference>Q4</FiscalReference>
<FiscalTag>FY2023Q4</FiscalTag>
<Forecast>0.49</Forecast>
<ForecastValue>0.49</ForecastValue>
<Importance>2</Importance>
<LastUpdate>2023-09-27T13:36:00</LastUpdate>
<MarketCapUSD>23519115193</MarketCapUSD>
<MarketRelease>after_close</MarketRelease>
<Name>Zscaler</Name>
<Previous>0.25</Previous>
<PreviousValue>0.25</PreviousValue>
<Revenue>455M</Revenue>
<RevenueForecast>430.38M</RevenueForecast>
<RevenueForecastValue>430380000</RevenueForecastValue>
<RevenuePrevious>318.06M</RevenuePrevious>
<RevenuePreviousValue>318060000</RevenuePreviousValue>
<RevenueValue>455000000</RevenueValue>
<Session>21</Session>
<Symbol>ZS:US</Symbol>
</Markets.EarningsRevenuesItem>
<Markets.EarningsRevenuesItem>
<Actual>4.09</Actual>
<ActualValue>4.09</ActualValue>
<CalendarReference>2023-08-31</CalendarReference>
<Country>United States</Country>
<Currency>USD</Currency>
<Date>2023-09-14</Date>
<FiscalReference>Q3</FiscalReference>
<FiscalTag>FY2023Q3</FiscalTag>
<Forecast>3.97</Forecast>
<ForecastValue>3.97</ForecastValue>
<Importance>3</Importance>
<LastUpdate>2023-09-27T13:39:00</LastUpdate>
<MarketCapUSD>235821800000</MarketCapUSD>
<MarketRelease>after_close</MarketRelease>
<Name>Adobe Systems</Name>
<Previous>3.4</Previous>
<PreviousValue>3.4</PreviousValue>
<Revenue>4.89B</Revenue>
<RevenueForecast>4.87B</RevenueForecast>
<RevenueForecastValue>4870000000</RevenueForecastValue>
<RevenuePrevious>4.43B</RevenuePrevious>
<RevenuePreviousValue>4430000000</RevenuePreviousValue>
<RevenueValue>4890000000</RevenueValue>
<Session>21</Session>
<Symbol>ADBE:US</Symbol>
</Markets.EarningsRevenuesItem>
<Markets.EarningsRevenuesItem>
<Actual>0.34</Actual>
<ActualValue>0.34</ActualValue>
<CalendarReference>2023-07-31</CalendarReference>
<Country>United States</Country>
<Currency>USD</Currency>
<Date>2023-09-14</Date>
<FiscalReference>Q4</FiscalReference>
<FiscalTag>FY2023Q4</FiscalTag>
<Forecast>0.32</Forecast>
<ForecastValue>0.32</ForecastValue>
<Importance>3</Importance>
<LastUpdate>2023-09-27T13:39:00</LastUpdate>
<MarketCapUSD>41585000000</MarketCapUSD>
<MarketRelease>after_close</MarketRelease>
<Name>Copart</Name>
<Previous>0.56</Previous>
<PreviousValue>0.56</PreviousValue>
<Revenue>997.6M</Revenue>
<RevenueForecast>962.78M</RevenueForecast>
<RevenueForecastValue>962780000</RevenueForecastValue>
<RevenuePrevious>883.39M</RevenuePrevious>
<RevenuePreviousValue>883390000</RevenuePreviousValue>
<RevenueValue>997600000</RevenueValue>
<Session>21</Session>
<Symbol>CPRT:US</Symbol>
</Markets.EarningsRevenuesItem>
</ArrayOfMarkets.EarningsRevenuesItem>
By index and date
Using Requests:
import requests
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={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 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=${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");
/earnings-revenues/index/{index}?d1=yyyy-mm-dd&d2=yyyy-mm-dd
| 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 | 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 |
/earnings-revenues/index/{index}?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"}]
/earnings-revenues/index/{index}?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
/earnings-revenues/index/{index}?d1=yyyy-mm-dd&d2=yyyy-mm-dd?f=xml
<ArrayOfMarkets.EarningsRevenuesItem xmlns:i="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://schemas.datacontract.org/2004/07/APILib.DB">
<Markets.EarningsRevenuesItem>
<Actual>1.03</Actual>
<ActualValue>1.03</ActualValue>
<CalendarReference>2015-11-30</CalendarReference>
<Country>United States</Country>
<Currency>USD</Currency>
<Date>2016-01-07</Date>
<FiscalReference>Q1</FiscalReference>
<FiscalTag>FY2016Q1</FiscalTag>
<Forecast>0.96</Forecast>
<ForecastValue>0.96</ForecastValue>
<Importance>2</Importance>
<LastUpdate>2023-08-14T12:16:00</LastUpdate>
<MarketCapUSD>19017600000</MarketCapUSD>
<MarketRelease>before_open</MarketRelease>
<Name>Walgreens Boots Alliance</Name>
<Previous/>
<PreviousValue i:nil="true"/>
<Revenue>29.03B</Revenue>
<RevenueForecast/>
<RevenueForecastValue i:nil="true"/>
<RevenuePrevious/>
<RevenuePreviousValue i:nil="true"/>
<RevenueValue>29030000000</RevenueValue>
<Session>13</Session>
<Symbol>WBA:US</Symbol>
</Markets.EarningsRevenuesItem>
<Markets.EarningsRevenuesItem>
<Actual>0.16</Actual>
<ActualValue>0.16</ActualValue>
<CalendarReference>2015-12-31</CalendarReference>
<Country>United States</Country>
<Currency>USD</Currency>
<Date>2016-01-12</Date>
<FiscalReference>Q4</FiscalReference>
<FiscalTag>FY2015Q4</FiscalTag>
<Forecast>0.15</Forecast>
<ForecastValue>0.15</ForecastValue>
<Importance>3</Importance>
<LastUpdate>2023-08-14T11:50:00</LastUpdate>
<MarketCapUSD>60972400000</MarketCapUSD>
<MarketRelease>after_close</MarketRelease>
<Name>CSX</Name>
<Previous>0.16</Previous>
<PreviousValue>0.16</PreviousValue>
<Revenue/>
<RevenueForecast/>
<RevenueForecastValue i:nil="true"/>
<RevenuePrevious/>
<RevenuePreviousValue i:nil="true"/>
<RevenueValue i:nil="true"/>
<Session>21</Session>
<Symbol>CSX:US</Symbol>
</Markets.EarningsRevenuesItem>
<Markets.EarningsRevenuesItem>
<Actual>0.74</Actual>
<ActualValue>0.74</ActualValue>
<CalendarReference>2015-12-31</CalendarReference>
<Country>United States</Country>
<Currency>USD</Currency>
<Date>2016-01-14</Date>
<FiscalReference>Q4</FiscalReference>
<FiscalTag>FY2015Q4</FiscalTag>
<Forecast>0.63</Forecast>
<ForecastValue>0.63</ForecastValue>
<Importance>3</Importance>
<LastUpdate>2023-08-14T12:00:00</LastUpdate>
<MarketCapUSD>148799600000</MarketCapUSD>
<MarketRelease>before_open</MarketRelease>
<Name>Intel</Name>
<Previous>0.74</Previous>
<PreviousValue>0.74</PreviousValue>
<Revenue>14.91B</Revenue>
<RevenueForecast/>
<RevenueForecastValue i:nil="true"/>
<RevenuePrevious>14.72B</RevenuePrevious>
<RevenuePreviousValue>14720000000</RevenuePreviousValue>
<RevenueValue>14910000000</RevenueValue>
<Session>13</Session>
<Symbol>INTC:US</Symbol>
</Markets.EarningsRevenuesItem>
</ArrayOfMarkets.EarningsRevenuesItem>
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” |