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For Taiwan stock news and other data, we have 3 datasets, as listed below:

(Because the data volume is large, only one day's worth of data is returned per request.)

Example

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # See login section to obtain a token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanStockNews",
    "data_id":"2330",
    "start_date": "2020-04-01",
}
data = requests.get(url, headers=headers, params=parameter)
data = data.json()
data = pd.DataFrame(data['data'])
print(data.head())

library(httr)
library(data.table)
library(dplyr)
token = "" # See login section to obtain a token
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanStockNews",
        data_id="2330",
        start_date= "2020-04-01"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = response %>% content
df = do.call('cbind',data$data) %>%data.table
head(df)

Output

date stock_id description link source title
0 2020-04-01 00:00:00 2330 <a href="https://tw.news.yahoo.com/%E9%9B%B... https://tw.news.yahoo.com/%E9%9B%BB%E5%AD%90%E... Yahoo奇摩新聞
1 2020-04-01 00:57:33 2330 <a href="https://udn.com/news/story/6850/44... https://udn.com/news/story/6850/4458587 udn 聯合新聞網
2 2020-04-01 00:58:42 2330 <a href="https://finance.technews.tw/2020/0... https://finance.technews.tw/2020/04/01/tsmc-wa... 科技新報 TechNews
3 2020-04-01 00:59:38 2330 <a href="https://tw.news.yahoo.com/%E5%8F%B... https://tw.news.yahoo.com/%E5%8F%B0%E7%A9%8D%E... Yahoo奇摩股市
4 2020-04-01 02:40:00 2330 <a href="https://fnc.ebc.net.tw/FncNews/Con... https://fnc.ebc.net.tw/FncNews/Content/117374 東森財經新聞 
{
    date: str, # date
    stock_id: str, # stock code
    description: str, # description
    link: str, # link
    source: str, # source
    title: str # title
}

Taiwan Monthly Business Indicator Table TaiwanBusinessIndicator (only available to backer/sponsor members)

  • Data range: 1982-01-01 ~ now

Example

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # See login section to obtain a token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanBusinessIndicator",
    "start_date": "2024-04-01",
    "end_date": "2025-01-01",
}
data = requests.get(url, headers=headers, params=parameter)
data = data.json()
data = pd.DataFrame(data['data'])
print(data.head())

library(httr)
library(data.table)
library(dplyr)
token = "" # See login section to obtain a token
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanBusinessIndicator",
        start_date= "2020-04-01",
        end_date= "2025-01-01"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = response %>% content
df = do.call('cbind',data$data) %>%data.table
head(df)

Output

date leading leading_notrend coincident coincident_notrend lagging lagging_notrend monitoring monitoring_color
0 2024-01-01 92.32 99.85 90.8 98.21 91.82 99.31 27 G
1 2024-02-01 92.71 100.35 91.45 98.99 91.68 99.25 29 G
2 2024-03-01 93.19 100.95 92.28 99.97 91.6 99.23 31 G
3 2024-04-01 93.75 101.63 93.23 101.07 91.52 99.22 35 YR
4 2024-05-01 94.29 102.28 94.21 102.19 91.41 99.16 36 YR 
{
    date: str, # date
    leading: float32, # leading composite indicator
    leading_notrend: float32, # leading indicator excluding trend
    coincident: float32, # coincident composite indicator
    coincident_notrend: float32, # coincident indicator excluding trend
    lagging: float32, # lagging composite indicator
    lagging_notrend: float32, # lagging indicator excluding trend
    monitoring: float32, # business indicator composite score
    monitoring_color: str # business indicator signal
}

Per-Company Industry Chain TaiwanStockIndustryChain (only available to backer/sponsor members)

Example

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_stock_industry_chain()

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # See login section to obtain a token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanStockIndustryChain",
}
data = requests.get(url, headers=headers, params=parameter)
data = data.json()
data = pd.DataFrame(data['data'])
print(data.head())

library(httr)
library(data.table)
library(dplyr)
token = "" # See login section to obtain a token
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanStockIndustryChain"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = response %>% content
df = do.call('cbind',data$data) %>%data.table
head(df)

Output

stock_id industry sub_industry date
0 1218 食品 冷凍、罐頭、脫水、醃漬食品 2025-03-30
1 1218 食品 加工食品 2025-03-30
2 1219 食品 加工食品 2025-03-30
3 1219 雲端運算 系統整合 2025-03-30
4 1236 食品 乳製品 2025-03-30 
{
    stock_id: str, # stock code
    industry: str, # industry
    sub_industry: str, # sub-industry
    date: str # update date
}