Derivative

In Taiwan stock derivatives data, we have 16 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
}

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 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-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
}

Due to the large data volume, each request only provides one day's data.
Data range: 2011-01-03 ~ now (data is incomplete between 2019-01-16 and 2019-06-30).
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
}

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
}

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