Derivative

In Taiwan stock derivatives data, we have 19 datasets, as follows:

Futures and Options Daily Trading Information Overview TaiwanFutOptDailyInfo¶

Example

PackagePython-requestR

from FinMind.data import DataLoader

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

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanFutOptDailyInfo",
}
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 = "" # Refer to login to obtain the token
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanFutOptDailyInfo"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = response %>% content
df = do.call('cbind',data$data) %>%data.table
head(df)

Output

DataFrameSchema

	code	type	name
0	AAA	TaiwanOptionDaily	南亞1000股選擇權
1	AAO	TaiwanOptionDaily	南亞選擇權
2	ABA	TaiwanOptionDaily	中鋼1000股選擇權
3	ABO	TaiwanOptionDaily	中鋼選擇權
4	ACA	TaiwanOptionDaily	聯電選擇權

{
    code: str, # product code
    type: str, # type
    name: str # product name
}

Futures Daily Trading Information TaiwanFuturesDaily¶

Data range: 1998-07-01 ~ now
Data update time: Monday to Friday 16:30. The actual update time is based on the API data.

Example

PackagePython-requestRPackage-Async

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_futures_daily(
    futures_id='TX',
    start_date='2020-04-01',
    end_date='2020-04-12',
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanFuturesDaily",
    "data_id":"TX",
    "start_date": "2020-04-01",
    "end_date": "2020-04-12",
}
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 = "" # Refer to login to obtain the token
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanFuturesDaily",
        data_id="TX",
        start_date= "2020-04-01",
        end_date= "2020-04-12"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = response %>% content
df = do.call('cbind',data$data) %>%data.table
head(df)

from FinMind.data import DataLoader
from loguru import logger
import datetime

api = DataLoader()
api.login_by_token(api_token='token')

start = datetime.datetime.now()
df = api.taiwan_futures_daily(
    futures_id_list=['TXF', 'MXF', 'EXF'],
    start_date='2024-01-01',
    end_date='2024-12-31',
    use_async=True,
)
cost = datetime.datetime.now() - start
logger.info(cost)

Output

DataFrameSchema

	date	futures_id	contract_date	open	max	min	close	spread	spread_per	volume	settlement_price	open_interest	trading_session
0	2020-04-01	TX	202004	9588	9650	9551	9552	-43	-0.45	116273	9555	83725	position
1	2020-04-01	TX	202004	9630	9665	9551	9575	-20	-0.21	73771	0	0	after_market
2	2020-04-01	TX	202005	9523	9580	9484	9486	-43	-0.45	1266	9486	6435	position
3	2020-04-01	TX	202005	9565	9595	9486	9526	-3	-0.03	452	0	0	after_market
4	2020-04-01	TX	202006	9452	9508	9415	9419	-36	-0.38	106	9419	5547	position

{
    date: str, # date
    futures_id: str, # futures code
    contract_date: str, # contract month
    open: float32, # open price
    max: float32, # max price
    min: float32, # min price
    close: float32, # close price
    spread: float32, # price change
    spread_per: float32, # price change percentage
    volume: float64, # trading volume
    settlement_price: float32, # settlement price
    open_interest: float64, # open interest
    trading_session: str # trading session
}

Example

PackagePython-requestR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_futures_daily(
    start_date='2020-04-01'
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanFuturesDaily",
    "start_date": "2020-04-01",
    "end_date": "2020-04-12",
}
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 = "" # Refer to login to obtain the token
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanFuturesDaily",
        start_date= "2020-04-01"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = response %>% content
df = do.call('rbind',data$data) %>%data.table
head(df)

Output

DataFrameSchema

	date	futures_id	contract_date	open	max	min	close	spread	spread_per	volume	settlement_price	open_interest	trading_session
0	2020-04-01	BRF	202005	0	0	0	0	0	0	0	681	381	position
1	2020-04-01	BRF	202005	690	704	681	681	-9	-1.3	45	0	0	after_market
2	2020-04-01	BRF	202006	795	799	774	774	-30	-3.73	63	774	435	position
3	2020-04-01	BRF	202006	818	833	789.5	791	-13	-1.62	77	0	0	after_market
4	2020-04-01	BRF	202007	881	881	874.5	874.5	7	0.81	3	874.5	3	position

{
    date: str, # date
    futures_id: str, # futures code
    contract_date: str, # contract month
    open: float32, # open price
    max: float32, # max price
    min: float32, # min price
    close: float32, # close price
    spread: float32, # price change
    spread_per: float32, # price change percentage
    volume: float64, # trading volume
    settlement_price: float32, # settlement price
    open_interest: float64, # open interest
    trading_session: str # trading session
}

Data range: 2011-01-03 ~ now
Data update time: Monday to Friday 16:30. The actual update time is based on the API data.
Only one day of data can be queried at a time.
data_id (futures code) is required. To get all futures products for a day at once, use Fetch all data for a specific date at once.

Example

PackagePython-requestR

from FinMind.data import DataLoader

api = DataLoader()
api.login_by_token(api_token='token')
df = api.taiwan_futures_kbar(
    futures_id='TX',
    date='2024-01-02',
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanFuturesKBar",
    "data_id": "TX",
    "start_date": "2024-01-02",
}
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 = "" # Refer to login to obtain the token
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanFuturesKBar",
        data_id="TX",
        start_date="2024-01-02"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = response %>% content
df = do.call('cbind',data$data) %>% data.table
head(df)

Output

DataFrameSchema

	date	futures_id	contract_date	minute	open	high	low	close	volume
0	2024-01-02	TX	202401	08:45:00	17800	17810	17795	17805	150
1	2024-01-02	TX	202401	08:46:00	17805	17815	17800	17812	98
2	2024-01-02	TX	202401	08:47:00	17812	17820	17810	17818	75
3	2024-01-02	TX	202402	08:45:00	17750	17760	17745	17755	12
4	2024-01-02	TX	202402	08:46:00	17755	17765	17750	17760	8

{
    date: str, # date
    futures_id: str, # futures code
    contract_date: str, # contract month
    minute: str, # minute time
    open: float32, # open price
    high: float32, # high price
    low: float32, # low price
    close: float32, # close price
    volume: int64, # trading volume
}

Fetch all data for a specific date at once (available only to sponsorpro members)¶

(Due to the large data volume, each request only provides one day's data.)

Data range: 2011-01-03 ~ now, one trading day at a time.
Providing the dataset and date parameters returns the minute KBars of all futures products for that day.
Downloads the whole-day parquet via a signed URL — no need to query contract by contract.

Example

Python-requestR

import io
import requests
import pandas as pd

url = "https://api.finmindtrade.com/api/v4/storage_objects"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanFuturesKBar",
    "date": '2024-01-02',
}
resp = requests.get(url, headers=headers, params=parameter)
data = pd.read_parquet(io.BytesIO(resp.content))
print(data.head())

library(httr)
library(data.table)
library(dplyr)
library(arrow)

url = 'https://api.finmindtrade.com/api/v4/storage_objects'
token = "" # Refer to login to obtain the token
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanFuturesKBar",
        date= "2024-01-02"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
con = content(response, "raw")
data <- read_parquet(con)
close(con)
head(data)

Output

DataFrameSchema

	date	futures_id	contract_date	minute	open	high	low	close	volume
0	2024-01-02	BRF	202403	09:01:00	2353.5	2353.5	2353.5	2353.5	2
1	2024-01-02	BRF	202403	09:03:00	2353.5	2353.5	2353.5	2353.5	2
2	2024-01-02	BRF	202403	09:32:00	2365.5	2365.5	2365.5	2365.5	2
3	2024-01-02	BRF	202403	09:37:00	2365	2365	2365	2365	22
4	2024-01-02	BRF	202403	09:50:00	2369	2369	2369	2369	2

{
    date: str, # date
    futures_id: str, # futures code
    contract_date: str, # contract month
    minute: str, # minute time
    open: float64, # open price
    high: float64, # high price
    low: float64, # low price
    close: float64, # close price
    volume: int64, # trading volume
}

Options Daily Trading Information TaiwanOptionDaily¶

Data range: 2001-12-01 ~ now
Data update time: Monday to Friday 16:30. The actual update time is based on the API data.

Example

PackagePython-requestRPackage-Async

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_option_daily(
    option_id='TXO',
    start_date='2020-04-01',
    end_date='2020-04-02',
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanOptionDaily",
    "data_id":"TXO",
    "start_date": "2020-04-01",
    "end_date": "2020-04-02",
}
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 = "" # Refer to login to obtain the token
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanOptionDaily",
        data_id="TXO",
        start_date= "2020-04-01",
        end_date= "2020-04-02"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = response %>% content
df = do.call('rbind',data$data) %>%data.table

from FinMind.data import DataLoader
from loguru import logger
import datetime

api = DataLoader()
api.login_by_token(api_token='token')

start = datetime.datetime.now()
df = api.taiwan_option_daily(
    option_id_list=['TXO', 'TEO'],
    start_date='2024-01-01',
    end_date='2024-12-31',
    use_async=True,
)
cost = datetime.datetime.now() - start
logger.info(cost)

Output

DataFrameSchema

	date	option_id	contract_date	strike_price	call_put	open	max	min	close	volume	open_interest	trading_session
0	2020-04-01	TXO	202004W1	8300	put	0.1	0.2	0.1	0.1	325	6253	position
1	2020-04-01	TXO	202004W1	8300	put	0.2	0.2	0.1	0.2	382	0	after_market
2	2020-04-01	TXO	202004W1	8400	put	0.1	0.1	0.1	0.1	152	1710	position
3	2020-04-01	TXO	202004W1	8400	put	0.3	0.3	0.1	0.1	96	0	after_market
4	2020-04-01	TXO	202004W1	8500	put	0.1	0.1	0.1	0.1	94	3464	position

{
    date: str, # date
    option_id: str, # option code
    contract_date: str, # contract month
    strike_price:float32, # strike price
    call_put: str, # call/put
    open: float32, # open price
    max: float32, # max price
    min: float32, # min price
    close: float32, # close price
    volume: float64, # trading volume
    settlement_price: float32, # settlement price
    open_interest: float64, # open interest
    trading_session: str # trading session
}

Example

PackagePython-requestR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_option_daily(
    start_date='2020-04-01'
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanOptionDaily",
    "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 = "" # Refer to login to obtain the token
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanOptionDaily",
        start_date= "2020-04-01"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = response %>% content
df = do.call('rbind',data$data) %>%data.table
head(df)

Output

DataFrameSchema

	date	option_id	contract_date	strike_price	call_put	open	max	min	close	volume	settlement_price	open_interest	trading_session
0	2020-04-01	CAO	202004	55	put	2.22	2.22	2.22	2.22	5	2.48	15	position
1	2020-04-01	CAO	202004	40	call	0	0	0	0	0	13.7	0	position
2	2020-04-01	CAO	202004	40	put	0	0	0	0	0	0.01	0	position
3	2020-04-01	CAO	202004	41	call	0	0	0	0	0	12.7	0	position
4	2020-04-01	CAO	202004	41	put	0	0	0	0	0	0.01	0	position

{
    date: str, # date
    option_id: str, # option code
    contract_date: str, # contract month
    strike_price: float32, # strike price
    call_put: str, # call/put
    open: float32, # open price
    max: float32, # max price
    min: float32, # min price
    close: float32, # close price
    volume: float64, # trading volume
    settlement_price: float32, # settlement price
    open_interest: float64, # open interest
    trading_session: str # trading session
}

Due to the large data volume, each request only provides one day's data.
data_id (futures code) is required. To get all futures products for a day at once, use Fetch all data for a specific date at once.
Data range: 2011-01-03 ~ now
Data update time: Monday to Friday 6:00. The actual update time is based on the API data.

How volume is counted

Tick volume is counted on a double-sided basis: each matched trade records both the buy side and the sell side once. As a result, the summed tick volume is about 2× the (single-side) volume in the daily data TaiwanFuturesDaily; for spread / combination orders, which contain two legs, the tick volume is about 4× the daily volume. Convert accordingly when reconciling tick volume against daily volume.

Per-contract-month reconciliation: for a given contract month (e.g. 202606) within the same trading session,

sum of volume of outright rows (contract_date = 202606) + sum of volume of spread rows containing that month (e.g. 202606/202607) ÷ 2 = 2 × TaiwanFuturesDaily volume of that month − 2 × negotiated block-trade lots of that month

The volume of a spread row is exactly 4× the volume of the corresponding spread contract (contract_date with two months) in TaiwanFuturesDaily. Each spread lot counts toward the daily volume of both legs, so divide spread volume by 2 when attributing it to a single month; do not add it in full.
Negotiated block trades are not included in tick data, but are included in daily volume. They occur mostly as rollovers ahead of settlement (equal lots in the near and next months) and are the main reason tick volume falls noticeably short of 2× daily volume around settlement week; this is not missing tick data. Details are published on the TAIFEX website under "Market Data > Daily Market Report > Block Trade > Negotiation": trades in the regular trading session count toward the regular session (position), and after-hours trades count toward that trading day's after-hours session (after_market).

Trading-day attribution of after-hours ticks

The after-hours (night) session follows the TAIFEX rule of being attributed to the next business day. The after-hours session for trading day D is the segment running from 15:00 on the previous business day until 05:00 on day D. Therefore, within a tick file:

00:00–05:00 ticks belong to the after-hours (after_market) session of trading day D in TaiwanFuturesDaily.
08:45–13:45 ticks belong to the regular session (position) of trading day D.
15:00–24:00 ticks belong to the after-hours session of the next trading day.

Example

PackagePython-requestR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_futures_tick(
    futures_id='MTX',
    date='2020-04-01'
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanFuturesTick",
    "data_id": "MTX",
    "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)
url = 'https://api.finmindtrade.com/api/v4/data'
token = "" # Refer to login to obtain the token
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanFuturesTick",
        data_id="MTX",
        start_date= "2020-01-02",
        token = "" # Refer to login to obtain the token
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrameSchema

	contract_date	date	futures_id	price	volume
0	202004	2020-04-01 00:00:01	MTX	9641	2
1	202004	2020-04-01 00:00:01	MTX	9641	2
2	202004	2020-04-01 00:00:01	MTX	9641	6
3	202004	2020-04-01 00:00:02	MTX	9640	2
4	202004	2020-04-01 00:00:02	MTX	9640	2

{
    ExercisePrice: float32,
    PutCall: str, # call/put
    contract_date: str, # contract month
    date: str, # date
    futures_id: str, # futures code
    price: float32, # deal price
    volume: int32 # volume
}

Fetch all data for a specific date at once (available only to sponsorpro members)¶

(Due to the large data volume, each request only provides one day's data.)

Data range: 2011-01-03 ~ now, one trading day at a time.
Providing the dataset and date parameters returns all market data for that day.
Downloads the whole-day parquet via a signed URL — no need to query contract by contract.

Example

PackagePython-requestR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_futures_tick(
    date='2026-01-02',
    use_object=True,
)

import io
import requests
import pandas as pd

url = "https://api.finmindtrade.com/api/v4/storage_objects"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanFuturesTick",
    "date": '2026-01-02',
}
resp = requests.get(url, headers=headers, params=parameter)
data = pd.read_parquet(io.BytesIO(resp.content))
print(data.head())

library(httr)
library(data.table)
library(dplyr)
library(arrow)

url = 'https://api.finmindtrade.com/api/v4/storage_objects'
token = "" # Refer to login to obtain the token
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanFuturesTick",
        date= "2026-01-02"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
con = content(response, "raw")
data <- read_parquet(con)
close(con)
head(data)

Output

DataFrameSchema

	contract_date	date	futures_id	price	volume
0	202601	2026-01-02 00:00:01	MTX	23100	2
1	202601	2026-01-02 00:00:01	MTX	23100	2
2	202601	2026-01-02 00:00:01	MTX	23100	6
3	202601	2026-01-02 00:00:02	MTX	23098	2
4	202601	2026-01-02 00:00:02	MTX	23098	2

{
    date: str, # date
    futures_id: str, # futures code
    contract_date: str, # contract month
    price: float32, # deal price
    volume: int32 # volume
}

Due to the large amount of data, only one day of data is provided per request
Data range: 2026-04-27 ~ now (accumulated daily since launch)
For research purposes, we recommend using data from 2026-06-12 onward; data before 2026-06-11 has gaps that cannot be backfilled
Data update time Monday to Friday, intraday and after market close, actual update time is based on the API data

Example

PackagePython-requestR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_futures_spread_tick(
    futures_id='CAF',
    date='2026-06-09'
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # login to get the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanFuturesSpreadTick",
    "data_id": "CAF",
    "start_date": "2026-06-09",
}
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)
url = 'https://api.finmindtrade.com/api/v4/data'
token = "" # login to get the token
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanFuturesSpreadTick",
        data_id="CAF",
        start_date= "2026-06-09",
        token = "" # login to get the token
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrameSchema

	contract_date	date	time	futures_id	price	volume	near_price	far_price	spread_to_spread
0	202606/202607	2026-06-09	08:45:03	CAF	0.5	4	100.5	101	0
1	202606/202607	2026-06-09	08:45:05	CAF	0.5	4	101	101.5	0
2	202606/202607	2026-06-09	08:50:38	CAF	0.6	4	100	100.6	1
3	202606/202607	2026-06-09	08:50:38	CAF	0.61	4	100	100.61	1

{
    date: str, # date
    time: str, # time
    futures_id: str, # futures code
    contract_date: str, # contract months (near/far)
    price: float32, # spread deal price
    volume: int32, # volume
    near_price: float32, # near month price
    far_price: float32, # far month price
    spread_to_spread: int32 # spread-to-spread deal flag (1 yes, 0 no)
}

Due to the large data volume, each request only provides one day's data.
data_id (option code) is required. To get all option products for a day at once, use Fetch all data for a specific date at once.
Data range: 2011-01-03 ~ now.
Data update time: Monday to Friday 6:00. The actual update time is based on the API data.

Example

PackagePython-requestRPackage-Async

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_option_tick(
    option_id='OCO',
    date='2020-04-01'
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanOptionTick",
    "data_id": "OCO",
    "start_date": "2019-09-05",
}
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)
url = 'https://api.finmindtrade.com/api/v4/data'
token = "" # Refer to login to obtain the token
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanOptionTick",
        data_id="OCO",
        start_date= "2019-09-05",
        token = "" # Refer to login to obtain the token
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

from FinMind.data import DataLoader
from loguru import logger
import datetime

api = DataLoader()
api.login_by_token(api_token='token')

date = '2025-12-08'
start = datetime.datetime.now()
df = api.taiwan_option_tick(
    option_id_list=['TXO', 'TEO'],
    date=date,
    use_async=True,
)
cost = datetime.datetime.now() - start
logger.info(cost)

Output

DataFrameSchema

	ExercisePrice	PutCall	contract_date	date	option_id	price	volume
0	20.5	P	202004	2020-04-01 10:26:58	OCO	0.29	1
1	20.5	P	202004	2020-04-01 10:26:58	OCO	0.29	1
2	21	P	202004	2020-04-01 10:26:58	OCO	0.44	2
3	21	P	202004	2020-04-01 10:26:58	OCO	0.44	2
4	21	P	202004	2020-04-01 10:26:58	OCO	0.44	4

{
    ExercisePrice: float32,
    PutCall: str, # call/put
    contract_date: str, # contract month
    date: str, # date
    option_id: str, # option code
    price: float32, # deal price
    volume: int32 # volume
}

Fetch all data for a specific date at once (available only to sponsorpro members)¶

(Due to the large data volume, each request only provides one day's data.)

Data range: 2011-01-03 ~ now, one trading day at a time.
Providing the dataset and date parameters returns all market data for that day.
Downloads the whole-day parquet via a signed URL — no need to query contract by contract.

Example

PackagePython-requestR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_option_tick(
    date='2026-01-02',
    use_object=True,
)

import io
import requests
import pandas as pd

url = "https://api.finmindtrade.com/api/v4/storage_objects"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanOptionTick",
    "date": '2026-01-02',
}
resp = requests.get(url, headers=headers, params=parameter)
data = pd.read_parquet(io.BytesIO(resp.content))
print(data.head())

library(httr)
library(data.table)
library(dplyr)
library(arrow)

url = 'https://api.finmindtrade.com/api/v4/storage_objects'
token = "" # Refer to login to obtain the token
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanOptionTick",
        date= "2026-01-02"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
con = content(response, "raw")
data <- read_parquet(con)
close(con)
head(data)

Output

DataFrameSchema

	ExercisePrice	PutCall	contract_date	date	option_id	price	volume
0	22000	C	202601	2026-01-02 10:00:01	TXO	0.50	1
1	22000	C	202601	2026-01-02 10:00:02	TXO	0.50	1
2	22000	P	202601	2026-01-02 10:00:03	TXO	0.80	2
3	22000	P	202601	2026-01-02 10:00:04	TXO	0.80	2
4	22500	C	202601	2026-01-02 10:00:05	TXO	1.20	4

{
    date: str, # date
    option_id: str, # option code
    ExercisePrice: float32, # exercise price
    contract_date: str, # contract month
    PutCall: str, # call/put
    price: float32, # deal price
    volume: int32 # volume
}

Futures Top Three Institutional Investors Trading TaiwanFuturesInstitutionalInvestors¶

Data range: 2018-06-05 ~ now
Data update time: Monday to Friday 18:00. The actual update time is based on the API data.

Example

PackagePython-requestRPackage-Async

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_futures_institutional_investors(
    data_id='TX',
    start_date='2020-04-01',
    end_date='2020-04-12',
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanFuturesInstitutionalInvestors",
    "data_id": "TX",# "TXO"
    "start_date": "2020-04-01",
    "end_date": "2020-04-12",
}
resp = requests.get(url, headers=headers, params=parameter)
data = resp.json()
df = pd.DataFrame(data["data"])
print(df.head())

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
token = "" # Refer to login to obtain the token
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanFuturesInstitutionalInvestors",
        data_id="TX",
        start_date= "2020-04-01",
        end_date= "2020-04-12",
        token = "" # Refer to login to obtain the token
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

from FinMind.data import DataLoader
from loguru import logger
import datetime

api = DataLoader()
api.login_by_token(api_token='token')

start = datetime.datetime.now()
df = api.taiwan_futures_institutional_investors(
    futures_id_list=['TXF', 'MXF', 'EXF'],
    start_date='2024-01-01',
    end_date='2024-12-31',
    use_async=True,
)
cost = datetime.datetime.now() - start
logger.info(cost)

Output

DataFrameSchema

	name	date	institutional_investors	long_deal_volume	long_deal_amount	short_deal_volume	short_deal_amount	long_open_interest_balance_volume	long_open_interest_balance_amount	short_open_interest_balance_volume	short_open_interest_balance_amount
0	TX	2020-04-01	自營商	15050	28875620	15325	29415959	19022	36062632	15962	30209225
1	TX	2020-04-01	外資	79042	151832089	75938	145876617	65435	124990394	14318	27292956
2	TX	2020-04-01	投信	30	57341	1313	2510881	3770	7204470	37345	71365191
3	TX	2020-04-06	自營商	15412	29817592	14569	28153648	19528	38087211	15628	30423409
4	TX	2020-04-06	投信	1135	2226831	53	102477	3800	7465480	36293	71299930

{
    name: str, # product name
    date: str, # date
    institutional_investors: str, # investor type
    long_deal_volume: int32, # long-side trading volume (lots)
    long_deal_amount: int32, # long-side contract amount
    short_deal_volume: int32, # short-side trading volume (lots)
    short_deal_amount: int32, # short-side contract amount
    long_open_interest_balance_volume: int32, # long-side open interest (lots)
    long_open_interest_balance_amount: int32, # long-side open interest contract amount
    short_open_interest_balance_volume: int32, # short-side open interest (lots)
    short_open_interest_balance_amount: int32 # short-side open interest contract amount
}

Example

PackagePython-requestR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_futopt_institutional_investors(
    start_date='2020-04-01'
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanFuturesInstitutionalInvestors",
    "start_date": "2019-04-03",
}
resp = requests.get(url, headers=headers, params=parameter)
data = resp.json()
df = pd.DataFrame(data["data"])
print(df)

library(httr)
library(data.table)
library(dplyr)
token = "" # Refer to login to obtain the token
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanFuturesInstitutionalInvestors",
        start_date= "2019-04-03"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = response %>% content
df = do.call('rbind',data$data) %>%data.table
head(df)

Output

DataFrameSchema

	name	date	institutional_investors	long_deal_volume	long_deal_amount	short_deal_volume	short_deal_amount	long_open_interest_balance_volume	long_open_interest_balance_amount	short_open_interest_balance_volume	short_open_interest_balance_amount
0	ETF	2020-04-01	外資	782	492994	840	541759	4462	3167434	2552	846756
1	ETF	2020-04-01	投信	0	0	0	0	2702	1071881	4079	2791150
2	ETF	2020-04-01	自營商	405	151407	431	161203	4493	2209637	4931	2386376
3	ETO	2020-04-01	投信	0	0	0	0	0	0	0	0
4	ETO	2020-04-01	外資	0	0	0	0	0	0	0	0

{
    name: str, # product name
    date: str, # date
    institutional_investors: str, # investor type
    long_deal_volume: int32, # long-side trading volume (lots)
    long_deal_amount: int32, # long-side contract amount
    short_deal_volume: int32, # short-side trading volume (lots)
    short_deal_amount: int32, # short-side contract amount
    long_open_interest_balance_volume: int32, # long-side open interest (lots)
    long_open_interest_balance_amount: int32, # long-side open interest contract amount
    short_open_interest_balance_volume: int32, # short-side open interest (lots)
    short_open_interest_balance_amount: int32 # short-side open interest contract amount
}

Options Top Three Institutional Investors Trading TaiwanOptionInstitutionalInvestors¶

Data range: 2018-06-05 ~ now
Data update time: Monday to Friday 16:00. The actual update time is based on the API data.

Example

PackagePython-requestRPackage-Async

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_option_institutional_investors(
    data_id='TXO',
    start_date='2020-04-01',
    end_date='2020-04-12',
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanOptionInstitutionalInvestors",
    "data_id": "TXO",
    "start_date": "2020-04-01",
    "end_date": "2020-04-12",
}
resp = requests.get(url, headers=headers, params=parameter)
data = resp.json()
df = pd.DataFrame(data["data"])
print(df)

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
token = "" # Refer to login to obtain the token
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanOptionInstitutionalInvestors",
        data_id="TX",# "TXO"
        start_date= "2020-04-01",
        end_date= "2020-04-12",
        token = "" # Refer to login to obtain the token
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

from FinMind.data import DataLoader
from loguru import logger
import datetime

api = DataLoader()
api.login_by_token(api_token='token')

start = datetime.datetime.now()
df = api.taiwan_option_institutional_investors(
    option_id_list=['TXO', 'TEO'],
    start_date='2024-01-01',
    end_date='2024-12-31',
    use_async=True,
)
cost = datetime.datetime.now() - start
logger.info(cost)

Output

DataFrameSchema

	name	date	call_put	institutional_investors	long_deal_volume	long_deal_amount	short_deal_volume	short_deal_amount	long_open_interest_balance_volume	long_open_interest_balance_amount	short_open_interest_balance_volume	short_open_interest_balance_amount
0	TXO	2020-04-01	買權	自營商	139973	370181	163094	356201	58152	504601	81614	517097
1	TXO	2020-04-01	買權	投信	0	0	0	0	0	0	0	0
2	TXO	2020-04-01	買權	外資	69409	214529	61586	224112	75953	630438	55645	586723
3	TXO	2020-04-06	買權	自營商	124528	453602	132575	475720	67677	646018	99186	671818
4	TXO	2020-04-06	賣權	投信	0	0	0	0	0	0	0	0

{
    name: str, # product name
    date: str, # date
    call_put: str, # call/put
    institutional_investors: str, # investor type
    long_deal_volume: int32, # long-side trading volume (lots)
    long_deal_amount: int32, # long-side contract amount
    short_deal_volume: int32, # short-side trading volume (lots)
    short_deal_amount: int32, # short-side contract amount
    long_open_interest_balance_volume: int32, # long-side open interest (lots)
    long_open_interest_balance_amount: int32, # long-side open interest contract amount
    short_open_interest_balance_volume: int32, # short-side open interest (lots)
    short_open_interest_balance_amount: int32 # short-side open interest contract amount
}

Example

PackagePython-requestR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_futopt_institutional_investors(
    data_id='TXO',
    start_date='2020-04-01'
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanOptionInstitutionalInvestors",
    "start_date": "2019-04-03",
}
resp = requests.get(url, headers=headers, params=parameter)
data = resp.json()
df = pd.DataFrame(data["data"])
print(df)

library(httr)
library(data.table)
library(dplyr)
token = "" # Refer to login to obtain the token
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanOptionInstitutionalInvestors",
        start_date= "2019-04-03"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = response %>% content
df = do.call('rbind',data$data) %>%data.table
head(df)

Output

DataFrameSchema

	name	date	institutional_investors	long_deal_volume	long_deal_amount	short_deal_volume	short_deal_amount	long_open_interest_balance_volume	long_open_interest_balance_amount	short_open_interest_balance_volume	short_open_interest_balance_amount
0	TXO	2020-04-01	自營商	139973	370181	163094	356201	58152	504601	81614	517097
1	TXO	2020-04-01	投信	0	0	0	0	0	0	0	0
2	TXO	2020-04-01	外資	69409	214529	61586	224112	75953	630438	55645	586723
3	TXO	2020-04-06	自營商	124528	453602	132575	475720	67677	646018	99186	671818
4	TXO	2020-04-06	投信	0	0	0	0	0	0	0	0

{
    name: str, # product name
    date: str, # date
    call_put: str, # call/put
    institutional_investors: str, # investor type
    long_deal_volume: int32, # long-side trading volume (lots)
    long_deal_amount: int32, # long-side contract amount
    short_deal_volume: int32, # short-side trading volume (lots)
    short_deal_amount: int32, # short-side contract amount
    long_open_interest_balance_volume: int32, # long-side open interest (lots)
    long_open_interest_balance_amount: int32, # long-side open interest contract amount
    short_open_interest_balance_volume: int32, # short-side open interest (lots)
    short_open_interest_balance_amount: int32 # short-side open interest contract amount
}

Data range: 2021-10-12 ~ now
Data update time: Monday to Saturday 05:00. The actual update time is based on the API data.

Example

Python-requestRPackage-Async

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanFuturesInstitutionalInvestorsAfterHours",
    "data_id": "TX",
    "start_date": "2021-10-12",
    "end_date": "2024-04-12",
}
resp = requests.get(url, headers=headers, params=parameter)
data = resp.json()
df = pd.DataFrame(data["data"])
print(df.head())

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
token = "" # Refer to login to obtain the token
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanFuturesInstitutionalInvestorsAfterHours",
        data_id="TX",
        start_date= "2021-10-12",
        end_date= "2024-04-12"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

from FinMind.data import DataLoader
from loguru import logger
import datetime

api = DataLoader()
api.login_by_token(api_token='token')

start = datetime.datetime.now()
df = api.taiwan_futures_institutional_investors_after_hours(
    futures_id_list=['TXF', 'MXF', 'EXF'],
    start_date='2024-01-01',
    end_date='2024-12-31',
    use_async=True,
)
cost = datetime.datetime.now() - start
logger.info(cost)

Output

DataFrameSchema

	futures_id	date	institutional_investors	long_deal_volume	long_deal_amount	short_deal_volume	short_deal_amount
0	TX	2021-10-12	自營商	1690	5615098	1516	5034732
1	TX	2021-10-12	投信	0	0	0	0
2	TX	2021-10-12	外資	16315	54215114	14737	48973486
3	TX	2021-10-13	自營商	2307	7608759	2252	7427497
4	TX	2021-10-13	投信	0	0	0	0

{
    name: str, # product name
    date: str, # date
    institutional_investors: str, # investor type
    long_deal_volume: int32, # long-side trading volume (lots)
    long_deal_amount: int32, # long-side contract amount
    short_deal_volume: int32, # short-side trading volume (lots)
    short_deal_amount: int32 # short-side contract amount
}

Example

Python-requestR

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanFuturesInstitutionalInvestorsAfterHours",
    "start_date": "2021-10-12",
}
resp = requests.get(url, headers=headers, params=parameter)
data = resp.json()
df = pd.DataFrame(data["data"])
print(df)

library(httr)
library(data.table)
library(dplyr)
token = "" # Refer to login to obtain the token
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanFuturesInstitutionalInvestorsAfterHours",
        start_date= "2021-10-12"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = response %>% content
df = do.call('rbind',data$data) %>%data.table
head(df)

Output

DataFrameSchema

	futures_id	date	institutional_investors	long_deal_volume	long_deal_amount	short_deal_volume	short_deal_amount
0	F1F	2021-10-12	自營商	39	13769	41	14477
1	F1F	2021-10-12	投信	0	0	0	0
2	F1F	2021-10-12	外資	83	29320	35	12349
3	MTX	2021-10-12	自營商	2454	2037796	2761	2292564
4	MTX	2021-10-12	投信	0	0	0	0

{
    name: str, # product name
    date: str, # date
    institutional_investors: str, # investor type
    long_deal_volume: int32, # long-side trading volume (lots)
    long_deal_amount: int32, # long-side contract amount
    short_deal_volume: int32, # short-side trading volume (lots)
    short_deal_amount: int32 # short-side contract amount
}

Data range: 2021-10-12 ~ now
Data update time: Monday to Saturday 05:00. The actual update time is based on the API data.

Example

Python-requestRPackage-Async

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanOptionInstitutionalInvestorsAfterHours",
    "data_id": "TXO",
    "start_date": "2021-10-12",
    "end_date": "2024-04-12",
}
resp = requests.get(url, headers=headers, params=parameter)
data = resp.json()
df = pd.DataFrame(data["data"])
print(df)

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
token = "" # Refer to login to obtain the token
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanOptionInstitutionalInvestorsAfterHours",
        data_id="TXO",
        start_date= "2021-10-12",
        end_date= "2024-04-12"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

from FinMind.data import DataLoader
from loguru import logger
import datetime

api = DataLoader()
api.login_by_token(api_token='token')

start = datetime.datetime.now()
df = api.taiwan_option_institutional_investors_after_hours(
    option_id_list=['TXO', 'TEO'],
    start_date='2024-01-01',
    end_date='2024-12-31',
    use_async=True,
)
cost = datetime.datetime.now() - start
logger.info(cost)

Output

DataFrameSchema

	option_id	date	call_put	institutional_investors	long_deal_volume	long_deal_amount	short_deal_volume	short_deal_amount
0	TXO	2021-10-12	CALL	自營商	14018	45608	14478	48062
1	TXO	2021-10-12	CALL	投信	0	0	0	0
2	TXO	2021-10-12	CALL	外資	16060	78585	14961	68018
3	TXO	2021-10-12	PUT	自營商	12802	50821	15570	66005
4	TXO	2021-10-12	PUT	投信	0	0	0	0

{
    name: str, # product name
    date: str, # date
    call_put: str, # call/put
    institutional_investors: str, # investor type
    long_deal_volume: int32, # long-side trading volume (lots)
    long_deal_amount: int32, # long-side contract amount
    short_deal_volume: int32, # short-side trading volume (lots)
    short_deal_amount: int32, # short-side contract amount
    long_open_interest_balance_volume: int32, # long-side open interest (lots)
    long_open_interest_balance_amount: int32, # long-side open interest contract amount
    short_open_interest_balance_volume: int32, # short-side open interest (lots)
    short_open_interest_balance_amount: int32 # short-side open interest contract amount
}

Example

Python-requestR

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanOptionInstitutionalInvestorsAfterHours",
    "start_date": "2021-10-12",
}
resp = requests.get(url, headers=headers, params=parameter)
data = resp.json()
df = pd.DataFrame(data["data"])
print(df)

library(httr)
library(data.table)
library(dplyr)
token = "" # Refer to login to obtain the token
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanOptionInstitutionalInvestorsAfterHours",
        start_date= "2021-10-12"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = response %>% content
df = do.call('rbind',data$data) %>%data.table
head(df)

Output

DataFrameSchema

	option_id	date	call_put	institutional_investors	long_deal_volume	long_deal_amount	short_deal_volume	short_deal_amount
0	TXO	2021-10-12	CALL	自營商	14018	45608	14478	48062
1	TXO	2021-10-12	CALL	投信	0	0	0	0
2	TXO	2021-10-12	CALL	外資	16060	78585	14961	68018
3	TXO	2021-10-12	PUT	自營商	12802	50821	15570	66005
4	TXO	2021-10-12	PUT	投信	0	0	0	0

{
    name: str, # product name
    date: str, # date
    call_put: str, # call/put
    institutional_investors: str, # investor type
    long_deal_volume: int32, # long-side trading volume (lots)
    long_deal_amount: int32, # long-side contract amount
    short_deal_volume: int32, # short-side trading volume (lots)
    short_deal_amount: int32 # short-side contract amount
}

Futures Daily Trading Volume by Dealer TaiwanFuturesDealerTradingVolumeDaily¶

Data range: 2021-04-01 ~ now
Data update time: Monday to Friday 19:00. The actual update time is based on the API data.

Example

PackagePython-requestRPackage-Async

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_futures_dealer_trading_volume_daily(
    futures_id='TX',
    start_date='2020-07-01',
    end_date='2020-07-12',
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanFuturesDealerTradingVolumeDaily",
    "data_id": "TX",
    "start_date": "2020-07-01",
    "end_date": "2020-10-02",
}
resp = requests.get(url, headers=headers, params=parameter)
data = resp.json()
df = pd.DataFrame(data["data"])
print(df.head())

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
token = "" # Refer to login to obtain the token
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanFuturesDealerTradingVolumeDaily",
        data_id="TX",
        start_date="2020-07-01",
        end_date="2020-10-02"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

from FinMind.data import DataLoader
from loguru import logger
import datetime

api = DataLoader()
api.login_by_token(api_token='token')

start = datetime.datetime.now()
df = api.taiwan_futures_dealer_trading_volume_daily(
    futures_id_list=['TXF', 'MXF', 'EXF'],
    start_date='2024-01-01',
    end_date='2024-12-31',
    use_async=True,
)
cost = datetime.datetime.now() - start
logger.info(cost)

Output

DataFrameSchema

	date	dealer_code	dealer_name	futures_id	volume	is_after_hour
0	2020-07-01	B224999	中國信託商業銀行自營	TX	1500	False
1	2020-07-01	F001000	國泰期貨	TX	1789	False
2	2020-07-01	F002000	永豐期貨	TX	9664	False
3	2020-07-01	F002999	永豐期貨自營	TX	0	False
4	2020-07-01	F004000	凱基期貨	TX	43882	False

{
    date: str, # date
    dealer_code: str, # dealer code
    dealer_name: str, # dealer name
    futures_id: str, # futures code
    volume: int32, # trading volume
    is_after_hour: int32 # after-hours trading
}

Example

PackagePython-requestR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_futures_dealer_trading_volume_daily(
    start_date='2021-07-01'
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanFuturesDealerTradingVolumeDaily",
    "start_date": "2020-07-01",
}
resp = requests.get(url, headers=headers, params=parameter)
data = resp.json()
df = pd.DataFrame(data["data"])
df

library(httr)
library(data.table)
library(dplyr)
token = "" # Refer to login to obtain the token
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanFuturesDealerTradingVolumeDaily",
        start_date="2020-07-01"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = response %>% content
df = do.call('rbind',data$data) %>%data.table
head(df)

Output

DataFrameSchema

	date	dealer_code	dealer_name	futures_id	volume	is_after_hour
0	2021-07-01	F021000	元大期貨	BRF	0	True
1	2021-07-01	F004000	凱基期貨	BRF	0	True
2	2021-07-01	F020000	群益期貨	BRF	0	True
3	2021-07-01	F002000	永豐期貨	BRF	0	True
4	2021-07-01	F008000	統一期貨	BRF	1	True

{
    date: str, # date
    dealer_code: str, # dealer code
    dealer_name: str, # dealer name
    futures_id: str, # futures code
    volume: int32, # trading volume
    is_after_hour: int32 # after-hours trading
}

Options Daily Trading Volume by Dealer TaiwanOptionDealerTradingVolumeDaily¶

Data range: 2021-04-01 ~ now
Data update time: Monday to Friday 18:00. The actual update time is based on the API data.

Example

PackagePython-requestRPackage-Async

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_option_dealer_trading_volume_daily(
    option_id='TXO',
    start_date='2020-07-01',
    end_date='2020-07-12',
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanOptionDealerTradingVolumeDaily",
    "data_id": "TXO",
    "start_date": "2020-07-01",
    "end_date": "2020-10-02",
}
resp = requests.get(url, headers=headers, params=parameter)
data = resp.json()
df = pd.DataFrame(data["data"])
df

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
token = "" # Refer to login to obtain the token
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanOptionDealerTradingVolumeDaily",
        data_id="TXO",
        start_date="2020-07-01",
        end_date="2020-10-02"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

from FinMind.data import DataLoader
from loguru import logger
import datetime

api = DataLoader()
api.login_by_token(api_token='token')

start = datetime.datetime.now()
df = api.taiwan_option_dealer_trading_volume_daily(
    option_id_list=['TXO', 'TEO'],
    start_date='2024-01-01',
    end_date='2024-12-31',
    use_async=True,
)
cost = datetime.datetime.now() - start
logger.info(cost)

Output

DataFrameSchema

	date	dealer_code	dealer_name	option_id	volume	is_after_hour
0	2020-07-01	B224999	中國信託商業銀行自營	TXO	13390	False
1	2020-07-01	F001000	國泰期貨	TXO	17478	False
2	2020-07-01	F002000	永豐期貨	TXO	75395	False
3	2020-07-01	F002999	永豐期貨自營	TXO	98	False
4	2020-07-01	F004000	凱基期貨	TXO	159164	False

{
    date: str, # date
    dealer_code: str, # dealer code
    dealer_name: str, # dealer name
    option_id: str, # option code
    volume: int32, # trading volume
    is_after_hour: int32 # after-hours trading
}

Example

PackagePython-requestR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_option_dealer_trading_volume_daily(
    start_date='2021-07-01'
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanOptionDealerTradingVolumeDaily",
    "start_date": "2021-07-01",
}
resp = requests.get(url, headers=headers, params=parameter)
data = resp.json()
df = pd.DataFrame(data["data"])
df

library(httr)
library(data.table)
library(dplyr)
token = "" # Refer to login to obtain the token
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanOptionDealerTradingVolumeDaily",
        start_date="2021-07-01"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = response %>% content
df = do.call('rbind',data$data) %>%data.table
head(df)

Output

DataFrameSchema

	date	dealer_code	dealer_name	option_id	volume	is_after_hour
0	2021-07-01	F021000	元大期貨	ETC	1	False
1	2021-07-01	F034999	澳帝華期貨自營	ETC	42	False
2	2021-07-01	F004000	凱基期貨	ETC	0	False
3	2021-07-01	S890999	法銀巴黎證券自營	ETC	83	False
4	2021-07-01	F002000	永豐期貨	ETC	0	False

{
    date: str, # date
    dealer_code: str, # dealer code
    dealer_name: str, # dealer name
    option_id: str, # option code
    volume: int32, # trading volume
    is_after_hour: int32 # after-hours trading
}

Data range: 1998-07-01 ~ now
Data update time: Monday to Friday 16:30. The actual update time is based on the API data.

Example

PackagePython-requestRPackage-Async

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_futures_open_interest_large_traders(
    futures_id='TJF',
    start_date='2024-09-01',
    end_date='2024-09-02',
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanFuturesOpenInterestLargeTraders",
    "data_id":"TJF",
    "start_date": "2024-09-01",
    "end_date": "2024-09-02",
}
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 = "" # Refer to login to obtain the token
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanFuturesOpenInterestLargeTraders",
        data_id="TJF",
        start_date= "2024-09-01",
        end_date= "2024-09-02"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = response %>% content
df = do.call('cbind',data$data) %>%data.table
head(df)

from FinMind.data import DataLoader
from loguru import logger
import datetime

api = DataLoader()
api.login_by_token(api_token='token')

start = datetime.datetime.now()
df = api.taiwan_futures_open_interest_large_traders(
    futures_id_list=['TXF', 'MXF', 'EXF'],
    start_date='2024-01-01',
    end_date='2024-12-31',
    use_async=True,
)
cost = datetime.datetime.now() - start
logger.info(cost)

Output

DataFrameSchema

	name	contract_type	buy_top5_trader_open_interest	buy_top5_trader_open_interest_per	buy_top10_trader_open_interest	buy_top10_trader_open_interest_per	sell_top5_trader_open_interest	sell_top5_trader_open_interest_per	sell_top10_trader_open_interest	sell_top10_trader_open_interest_per	market_open_interest	buy_top5_specific_open_interest	buy_top5_specific_open_interest_per	buy_top10_specific_open_interest	buy_top10_specific_open_interest_per	sell_top5_specific_open_interest	sell_top5_specific_open_interest_per	sell_top10_specific_open_interest	sell_top10_specific_open_interest_per	date	futures_id
0	東證期貨	202409	93	74.4	113	90.4	102	81.6	118	94.4	125	16	12.8	16	12.8	14	11.2	14	11.2	2024-09-02	TJF
1	東證期貨	202409	133	62.7	170	80.2	172	81.1	194	91.5	212	16	7.5	16	7.5	42	19.8	42	19.5	2024-09-02	TJF

{
    name: str, # product name
    contract_type: str, # contract month
    buy_top5_trader_open_interest: int32, # total open interest of top 5 buy-side traders
    buy_top5_trader_open_interest_per: float32, # percentage of top 5 buy-side traders
    buy_top10_trader_open_interest: int32, # total open interest of top 10 buy-side traders
    buy_top10_trader_open_interest_per: float32, # percentage of top 10 buy-side traders
    sell_top5_trader_open_interest: int32, # total open interest of top 5 sell-side traders
    sell_top5_trader_open_interest_per: float32, # percentage of top 5 sell-side traders
    sell_top10_trader_open_interest: int32, # total open interest of top 10 sell-side traders
    sell_top10_trader_open_interest_per: float32, # percentage of top 10 sell-side traders
    market_open_interest: int32, # total market open interest
    buy_top5_specific_open_interest: int32, # total open interest of top 5 buy-side specific institutions
    buy_top5_specific_open_interest_per: float32, # percentage of top 5 buy-side specific institutions
    buy_top10_specific_open_interest: int32, # total open interest of top 10 buy-side specific institutions
    buy_top10_specific_open_interest_per: float32, # percentage of top 10 buy-side specific institutions
    sell_top5_specific_open_interest: int32, # total open interest of top 5 sell-side specific institutions
    sell_top5_specific_open_interest_per: float32, # percentage of top 5 sell-side specific institutions
    sell_top10_specific_open_interest: int32, # total open interest of top 10 sell-side specific institutions
    sell_top10_specific_open_interest_per: float32, # percentage of top 10 sell-side specific institutions
    date: str, # date
    futures_id: str # futures code
}

Example

PackagePython-requestR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_futures_open_interest_large_traders(
    start_date='2024-09-02'
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanFuturesOpenInterestLargeTraders",
    "start_date": "2024-09-02",
}
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 = "" # Refer to login to obtain the token
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanFuturesOpenInterestLargeTraders",
        start_date= "2024-09-02"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = response %>% content
df = do.call('rbind',data$data) %>%data.table
head(df)

Output

DataFrameSchema

	name	contract_type	buy_top5_trader_open_interest	buy_top5_trader_open_interest_per	buy_top10_trader_open_interest	buy_top10_trader_open_interest_per	sell_top5_trader_open_interest	sell_top5_trader_open_interest_per	sell_top10_trader_open_interest	sell_top10_trader_open_interest_per	market_open_interest	buy_top5_specific_open_interest	buy_top5_specific_open_interest_per	buy_top10_specific_open_interest	buy_top10_specific_open_interest_per	sell_top5_specific_open_interest	sell_top5_specific_open_interest_per	sell_top10_specific_open_interest	sell_top10_specific_open_interest_per	date	futures_id
0	布蘭特原油期貨	202411	40	100	40	100	40	100	40	100	40	0	0	0	0	0	0	0	0	2024-09-02	BRF
1	布蘭特原油期貨	all	155	96.9	160	100	160	100	160	100	160	0	0	0	0	120	75	120	75	2024-09-02	BRF
2	臺灣生技期貨	202409	15	78.9	19	100	19	100	19	100	19	0	0	0	0	0	0	0	0	2024-09-02	BTF
3	臺灣生技期貨	all	16	80	20	100	20	100	20	100	20	0	0	0	0	0	0	0	0	2024-09-02	BTF
4	南亞期貨	202409	231	30.3	332	43.6	512	67.2	655	86	762	127	16.7	127	16.7	438	57.5	532	69.8	2024-09-02	CA

{
    name: str, # product name
    contract_type: str, # contract month
    buy_top5_trader_open_interest: int32, # total open interest of top 5 buy-side traders
    buy_top5_trader_open_interest_per: float32, # percentage of top 5 buy-side traders
    buy_top10_trader_open_interest: int32, # total open interest of top 10 buy-side traders
    buy_top10_trader_open_interest_per: float32, # percentage of top 10 buy-side traders
    sell_top5_trader_open_interest: int32, # total open interest of top 5 sell-side traders
    sell_top5_trader_open_interest_per: float32, # percentage of top 5 sell-side traders
    sell_top10_trader_open_interest: int32, # total open interest of top 10 sell-side traders
    sell_top10_trader_open_interest_per: float32, # percentage of top 10 sell-side traders
    market_open_interest: int32, # total market open interest
    buy_top5_specific_open_interest: int32, # total open interest of top 5 buy-side specific institutions
    buy_top5_specific_open_interest_per: float32, # percentage of top 5 buy-side specific institutions
    buy_top10_specific_open_interest: int32, # total open interest of top 10 buy-side specific institutions
    buy_top10_specific_open_interest_per: float32, # percentage of top 10 buy-side specific institutions
    sell_top5_specific_open_interest: int32, # total open interest of top 5 sell-side specific institutions
    sell_top5_specific_open_interest_per: float32, # percentage of top 5 sell-side specific institutions
    sell_top10_specific_open_interest: int32, # total open interest of top 10 sell-side specific institutions
    sell_top10_specific_open_interest_per: float32, # percentage of top 10 sell-side specific institutions
    date: str, # date
    futures_id: str # futures code
}

Data range: 1998-07-01 ~ now
Data update time: Monday to Friday 16:30. The actual update time is based on the API data.

Example

PackagePython-requestRPackage-Async

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_option_open_interest_large_traders(
    futures_id='CA',
    start_date='2024-09-01',
    end_date='2024-09-02',
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanOptionOpenInterestLargeTraders",
    "data_id":"CA",
    "start_date": "2024-09-01",
    "end_date": "2024-09-02",
}
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 = "" # Refer to login to obtain the token
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanOptionOpenInterestLargeTraders",
        data_id="CA",
        start_date= "2024-09-01",
        end_date= "2024-09-02"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = response %>% content
df = do.call('cbind',data$data) %>%data.table
head(df)

from FinMind.data import DataLoader
from loguru import logger
import datetime

api = DataLoader()
api.login_by_token(api_token='token')

start = datetime.datetime.now()
df = api.taiwan_option_open_interest_large_traders(
    option_id_list=['TXO', 'TEO'],
    start_date='2024-01-01',
    end_date='2024-12-31',
    use_async=True,
)
cost = datetime.datetime.now() - start
logger.info(cost)

Output

DataFrameSchema

	contract_type	date	put_call	name	option_id
0	202409	2024-09-02	call	南亞	CA
1	all	2024-09-02	call	南亞	CA
2	202409	2024-09-02	put	南亞	CA
3	all	2024-09-02	put	南亞	CA

{
    contract_type: str, # contract month
    buy_top5_trader_open_interest: int32, # total open interest of top 5 buy-side traders
    buy_top5_trader_open_interest_per: float32, # percentage of top 5 buy-side traders
    buy_top10_trader_open_interest: int32, # total open interest of top 10 buy-side traders
    buy_top10_trader_open_interest_per: float32, # percentage of top 10 buy-side traders
    sell_top5_trader_open_interest: int32, # total open interest of top 5 sell-side traders
    sell_top5_trader_open_interest_per: float32, # percentage of top 5 sell-side traders
    sell_top10_trader_open_interest: int32, # total open interest of top 10 sell-side traders
    sell_top10_trader_open_interest_per: float32, # percentage of top 10 sell-side traders
    market_open_interest: int32, # total market open interest
    buy_top5_specific_open_interest: int32, # total open interest of top 5 buy-side specific institutions
    buy_top5_specific_open_interest_per: float32, # percentage of top 5 buy-side specific institutions
    buy_top10_specific_open_interest: int32, # total open interest of top 10 buy-side specific institutions
    buy_top10_specific_open_interest_per: float32, # percentage of top 10 buy-side specific institutions
    sell_top5_specific_open_interest: int32, # total open interest of top 5 sell-side specific institutions
    sell_top5_specific_open_interest_per: float32, # percentage of top 5 sell-side specific institutions
    sell_top10_specific_open_interest: int32, # total open interest of top 10 sell-side specific institutions
    sell_top10_specific_open_interest_per: float32, # percentage of top 10 sell-side specific institutions
    date: str, # date
    put_call: str, # call/put
    name: str, # product name
    option_id: str # option code
}

Example

PackagePython-requestR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_option_open_interest_large_traders(
    start_date='2024-09-02'
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanOptionOpenInterestLargeTraders",
    "start_date": "2024-09-02",
}
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 = "" # Refer to login to obtain the token
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanOptionOpenInterestLargeTraders",
        start_date= "2024-09-02"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = response %>% content
df = do.call('rbind',data$data) %>%data.table
head(df)

Output

DataFrameSchema

	contract_type	date	put_call	name	option_id
0	202409	2024-09-02	call	南亞	CA
1	all	2024-09-02	call	南亞	CA
2	202409	2024-09-02	put	南亞	CA
3	all	2024-09-02	put	南亞	CA
4	202409	2024-09-02	call	中鋼	CB

{
    contract_type: str, # contract month
    buy_top5_trader_open_interest: int32, # total open interest of top 5 buy-side traders
    buy_top5_trader_open_interest_per: float32, # percentage of top 5 buy-side traders
    buy_top10_trader_open_interest: int32, # total open interest of top 10 buy-side traders
    buy_top10_trader_open_interest_per: float32, # percentage of top 10 buy-side traders
    sell_top5_trader_open_interest: int32, # total open interest of top 5 sell-side traders
    sell_top5_trader_open_interest_per: float32, # percentage of top 5 sell-side traders
    sell_top10_trader_open_interest: int32, # total open interest of top 10 sell-side traders
    sell_top10_trader_open_interest_per: float32, # percentage of top 10 sell-side traders
    market_open_interest: int32, # total market open interest
    buy_top5_specific_open_interest: int32, # total open interest of top 5 buy-side specific institutions
    buy_top5_specific_open_interest_per: float32, # percentage of top 5 buy-side specific institutions
    buy_top10_specific_open_interest: int32, # total open interest of top 10 buy-side specific institutions
    buy_top10_specific_open_interest_per: float32, # percentage of top 10 buy-side specific institutions
    sell_top5_specific_open_interest: int32, # total open interest of top 5 sell-side specific institutions
    sell_top5_specific_open_interest_per: float32, # percentage of top 5 sell-side specific institutions
    sell_top10_specific_open_interest: int32, # total open interest of top 10 sell-side specific institutions
    sell_top10_specific_open_interest_per: float32, # percentage of top 10 sell-side specific institutions
    date: str, # date
    put_call: str, # call/put
    name: str, # product name
    option_id: str # option code
}

Data range: 2007-10-08 ~ now
Data update time: Monday to Friday every 3 hours. The actual update time is based on the API data.

Example

Python-requestRPackage-Async

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanFuturesSpreadTrading",
    "data_id": "TX",
    "start_date": "2024-01-01",
    "end_date": "2024-12-31",
}
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 = "" # Refer to login to obtain the token
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanFuturesSpreadTrading",
        data_id="TX",
        start_date= "2024-01-01",
        end_date= "2024-12-31"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = response %>% content
df = do.call('cbind',data$data) %>%data.table
head(df)

from FinMind.data import DataLoader
from loguru import logger
import datetime

api = DataLoader()
api.login_by_token(api_token='token')

start = datetime.datetime.now()
df = api.taiwan_futures_spread_trading(
    futures_id_list=['TXF', 'MXF', 'EXF'],
    start_date='2024-01-01',
    end_date='2024-12-31',
    use_async=True,
)
cost = datetime.datetime.now() - start
logger.info(cost)

Output

DataFrameSchema

	date	futures_id	contract_date	open	max	min	close	best_bid	best_ask	historical_max	historical_min	spread_to_spread_volume	spread_to_single_volume	trading_session
0	2024-01-02	TX	202401/202402	-85.0	-72.0	-98.0	-78.0	-80.0	-77.0	565.0	-418.0	1234.0	567.0	position
1	2024-01-02	TX	202401/202403	-90.0	-80.0	-105.0	-85.0	-88.0	-82.0	600.0	-450.0	234.0	123.0	position

{
    date: str, # date
    futures_id: str, # futures code
    contract_date: str, # contract month
    open: float64, # open price
    max: float64, # max price
    min: float64, # min price
    close: float64, # close price
    best_bid: float64, # best bid price
    best_ask: float64, # best ask price
    historical_max: float64, # historical max price
    historical_min: float64, # historical min price
    spread_to_spread_volume: float64, # spread-to-spread trading volume
    spread_to_single_volume: float64, # spread-to-single trading volume
    trading_session: str # trading session
}

Data range: 1998-01-01 ~ now
Data update time: Monday to Friday every 3 hours. The actual update time is based on the API data.

Example

Python-requestRPackage-Async

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanFuturesFinalSettlementPrice",
    "data_id": "TX",
    "start_date": "2024-01-01",
    "end_date": "2024-12-31",
}
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 = "" # Refer to login to obtain the token
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanFuturesFinalSettlementPrice",
        data_id="TX",
        start_date= "2024-01-01",
        end_date= "2024-12-31"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = response %>% content
df = do.call('cbind',data$data) %>%data.table
head(df)

from FinMind.data import DataLoader
from loguru import logger
import datetime

api = DataLoader()
api.login_by_token(api_token='token')

start = datetime.datetime.now()
df = api.taiwan_futures_final_settlement_price(
    futures_id_list=['TXF', 'MXF', 'EXF'],
    start_date='2024-01-01',
    end_date='2024-12-31',
    use_async=True,
)
cost = datetime.datetime.now() - start
logger.info(cost)

Output

DataFrameSchema

	date	contract_month	futures_type	futures_id	futures_name	settlement_price
0	2024-01-17	202401	index	TX	臺股期貨	17881.0
1	2024-02-21	202402	index	TX	臺股期貨	18658.0
2	2024-03-20	202403	index	TX	臺股期貨	20199.0

{
    date: str, # expiration date
    contract_month: str, # contract month
    futures_type: str, # futures type (index/stock/commodity)
    futures_id: str, # futures code
    futures_name: str, # futures name
    settlement_price: float64, # final settlement price
    underlying_code: str, # underlying security code
    notional_value: float64 # notional contract value
}

Data range: 2001-01-01 ~ now
Data update time: Monday to Friday every 3 hours. The actual update time is based on the API data.

Example

Python-requestRPackage-Async

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanOptionFinalSettlementPrice",
    "data_id": "TXO",
    "start_date": "2024-01-01",
    "end_date": "2024-12-31",
}
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 = "" # Refer to login to obtain the token
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanOptionFinalSettlementPrice",
        data_id="TXO",
        start_date= "2024-01-01",
        end_date= "2024-12-31"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = response %>% content
df = do.call('cbind',data$data) %>%data.table
head(df)

from FinMind.data import DataLoader
from loguru import logger
import datetime

api = DataLoader()
api.login_by_token(api_token='token')

start = datetime.datetime.now()
df = api.taiwan_option_final_settlement_price(
    option_id_list=['TXO', 'TEO'],
    start_date='2024-01-01',
    end_date='2024-12-31',
    use_async=True,
)
cost = datetime.datetime.now() - start
logger.info(cost)

Output

DataFrameSchema

	date	contract_month	option_type	option_id	option_name	settlement_price
0	2024-01-17	202401	index	TXO	臺指選擇權	17881.0
1	2024-02-21	202402	index	TXO	臺指選擇權	18658.0
2	2024-03-20	202403	index	TXO	臺指選擇權	20199.0

{
    date: str, # expiration date
    contract_month: str, # contract month
    option_type: str, # option type (index/stock)
    option_id: str, # option code
    option_name: str, # option name
    settlement_price: float64, # final settlement price
    underlying_code: str, # underlying security code
    notional_value: float64 # notional contract value
}

Data range: 2026-03-01 ~ now
Data update time: Monday to Saturday, 18:00. The actual update time is based on the API data.

Example

Python-requestR

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanOptionVix",
    "start_date": "2026-06-01",
    "end_date": "2026-06-02",
}
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 = "" # Refer to login to obtain the token
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanOptionVix",
        start_date= "2026-06-01",
        end_date= "2026-06-02"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = response %>% content
df = do.call('cbind',data$data) %>%data.table
head(df)

Output

DataFrameSchema

	date	time	vix
0	2024-06-01	09:00:00	37.12
1	2024-06-01	12:07:45	36.05
2	2024-06-01	12:08:00	36.04

{
    date: str, # date
    time: str, # time
    vix: float64 # volatility index
}

Derivative

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