Chip (Institutional)

In Taiwan stock chip data, we have 21 datasets as follows:

• Individual Stock Margin Purchase / Short Sale TaiwanStockMarginPurchaseShortSale
• Total Market Margin Purchase / Short Sale TaiwanStockTotalMarginPurchaseShortSale
• Individual Stock Institutional Investors Buy/Sell TaiwanStockInstitutionalInvestorsBuySell
• Individual Stock Institutional Investors Buy/Sell (Wide) TaiwanStockInstitutionalInvestorsBuySellWide
• Total Market Institutional Investors Buy/Sell TaiwanStockTotalInstitutionalInvestors
• Foreign Investor Shareholding TaiwanStockShareholding
• Shareholders Holding Shares Distribution TaiwanStockHoldingSharesPer
• Securities Lending Transaction Details TaiwanStockSecuritiesLending
• Margin Short Sale Suspension (Short Sale Covering Date) TaiwanStockMarginShortSaleSuspension
• Credit Limit Daily Short Sale Balances TaiwanDailyShortSaleBalances
• Securities Trader Information TaiwanSecuritiesTraderInfo
• Taiwan Stock Trading Daily Report by Branch (query by stock_id) TaiwanStockTradingDailyReport
• Taiwan Stock Trading Daily Report by Branch (query by securities_trader_id) TaiwanStockTradingDailyReport
• Taiwan Stock Warrant Trading Daily Report by Branch (query by stock_id) TaiwanStockWarrantTradingDailyReport
• Taiwan Stock Warrant Trading Daily Report by Branch (query by securities_trader_id) TaiwanStockWarrantTradingDailyReport
• Taiwan Stock Government Bank Buy/Sell TaiwanstockGovernmentBankBuySell
• Taiwan Total Exchange Margin Maintenance TaiwanTotalExchangeMarginMaintenance
• Daily Securities Trader Branch Aggregate Statistics TaiwanStockTradingDailyReportSecIdAgg
• Block Trading Daily Report TaiwanStockBlockTradingDailyReport
• Block Trade Daily Transactions TaiwanStockBlockTrade
• Loan Collateral Balance TaiwanStockLoanCollateralBalance
• Disposition Securities Period TaiwanStockDispositionSecuritiesPeriod
• Day Trading Borrowing Fee Rate TaiwanStockDayTradingBorrowingFeeRate

---

Individual Stock Margin Purchase / Short Sale TaiwanStockMarginPurchaseShortSale

• Data range: 2001-01-01 ~ now
• Data update time Monday to Friday 21:00, the actual update time is based on the API data.

Example

PackagePythonRPackage-Async

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_stock_margin_purchase_short_sale(
    stock_id="2330",
    start_date='2020-04-02',
    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 API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanStockMarginPurchaseShortSale",
    "data_id": "2330",
    "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'])

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
token = "" # Refer to login to obtain the API key
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanStockMarginPurchaseShortSale",
        data_id= "2330",
        start_date= "2020-01-02",
        end_date= "2020-04-12",
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
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_stock_margin_purchase_short_sale(
    stock_id_list=['2330', '2317', '2454', '3008'],
    start_date='2024-01-01',
    end_date='2024-12-31',
    use_async=True,
)
cost = datetime.datetime.now() - start
logger.info(cost)

Output

DataFrameSchema

	date	stock_id	MarginPurchaseBuy	MarginPurchaseCashRepayment	MarginPurchaseLimit	MarginPurchaseSell	MarginPurchaseTodayBalance	MarginPurchaseYesterdayBalance	Note	OffsetLoanAndShort	ShortSaleBuy	ShortSaleCashRepayment	ShortSaleLimit	ShortSaleSell	ShortSaleTodayBalance	ShortSaleYesterdayBalance
0	2020-04-06	2330	1914	8	6482595	1269	26285	25648	X	0	0	24	6482595	0	0	24
1	2020-04-07	2330	1049	13	6482595	2655	24666	26285	X	0	0	0	6482595	0	0	0
2	2020-04-08	2330	1192	3	6482595	1569	24286	24666		0	0	0	6482595	0	0	0
3	2020-04-09	2330	499	28	6482595	1362	23395	24286		209	0	0	6482595	398	398	0
4	2020-04-10	2330	1227	24	6482595	794	23804	23395		53	156	0	6482595	156	398	398

{
    date: str, # date
    stock_id: str, # stock symbol
    MarginPurchaseBuy: int64, # margin purchase buy
    MarginPurchaseCashRepayment: int64, # margin purchase cash repayment
    MarginPurchaseLimit: int64, # margin purchase limit
    MarginPurchaseSell: int64, # margin purchase sell
    MarginPurchaseTodayBalance: int64, # margin purchase today balance
    MarginPurchaseYesterdayBalance: int64, # margin purchase yesterday balance
    Note: str, # note
    OffsetLoanAndShort: int64, # offset loan and short
    ShortSaleBuy: int64, # short sale buy
    ShortSaleCashRepayment: int64, # short sale cash repayment
    ShortSaleLimit: int64, # short sale limit
    ShortSaleSell: int64, # short sale sell
    ShortSaleTodayBalance: int64, # short sale today balance
    ShortSaleYesterdayBalance: int64 # short sale yesterday balance
}

Get all data for a specific date in one request (only available for backer, sponsor members)

Example

PackagePython-requestR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_stock_margin_purchase_short_sale(
    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 API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanStockMarginPurchaseShortSale",
    "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 API key
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanStockMarginPurchaseShortSale",
        start_date= "2020-01-02"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrameSchema

	date	stock_id	MarginPurchaseBuy	MarginPurchaseCashRepayment	MarginPurchaseLimit	MarginPurchaseSell	MarginPurchaseTodayBalance	MarginPurchaseYesterdayBalance	Note	OffsetLoanAndShort	ShortSaleBuy	ShortSaleCashRepayment	ShortSaleLimit	ShortSaleSell	ShortSaleTodayBalance	ShortSaleYesterdayBalance
0	2020-04-01	0050	193	15	263750	163	3189	3174		0	65	1	263750	13	2283	2336
1	2020-04-01	0051	0	0	2375	0	5	5		0	0	0	2375	0	0	0
2	2020-04-01	0052	0	0	7500	0	128	128		0	0	0	7500	0	0	0
3	2020-04-01	0053	0	0	1622	0	1	1		0	0	0	1622	0	0	0
4	2020-04-01	0054	0	0	2531	0	0	0	X	0	0	0	2531	0	0	0

{
    date: str, # date
    stock_id: str, # stock symbol
    MarginPurchaseBuy: int64, # margin purchase buy
    MarginPurchaseCashRepayment: int64, # margin purchase cash repayment
    MarginPurchaseLimit: int64, # margin purchase limit
    MarginPurchaseSell: int64, # margin purchase sell
    MarginPurchaseTodayBalance: int64, # margin purchase today balance
    MarginPurchaseYesterdayBalance: int64, # margin purchase yesterday balance
    Note: str, # note
    OffsetLoanAndShort: int64, # offset loan and short
    ShortSaleBuy: int64, # short sale buy
    ShortSaleCashRepayment: int64, # short sale cash repayment
    ShortSaleLimit: int64, # short sale limit
    ShortSaleSell: int64, # short sale sell
    ShortSaleTodayBalance: int64, # short sale today balance
    ShortSaleYesterdayBalance: int64 # short sale yesterday balance
}

---

Taiwan Total Market Margin Purchase / Short Sale TaiwanStockTotalMarginPurchaseShortSale

• Data range: 2001-01-01 ~ now
• Data update time Monday to Friday 21:00, 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_stock_margin_purchase_short_sale_total(
    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 API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanStockTotalMarginPurchaseShortSale",
    "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)
url = 'https://api.finmindtrade.com/api/v4/data'
token = "" # Refer to login to obtain the API key
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanStockTotalMarginPurchaseShortSale",
        start_date= "2020-01-02",
        end_date= "2020-04-12"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrameSchema

	TodayBalance	YesBalance	buy	date	name	Return	sell
0	5463820	5471770	236127	2020-04-01	MarginPurchase	10986	233091
1	91965082000	91898116000	4046643000	2020-04-01	MarginPurchaseMoney	196619000	3783058000
2	541704	556742	57266	2020-04-01	ShortSale	6151	48379
3	535401	541704	50779	2020-04-06	ShortSale	3700	48176
4	93198509000	91965082000	6440842000	2020-04-06	MarginPurchaseMoney	71638000	5135777000

{
    TodayBalance: int64, # today balance
    YesBalance: int64, # yesterday balance
    buy: int64, # buy
    date: str, # date
    name: str, # category
    Return: int64, # cash/securities repayment
    sell: int64 # sell
}

---

Institutional Investors Buy/Sell TaiwanStockInstitutionalInvestorsBuySell

• Data range: 2005-01-01 ~ now
• Coverage: listed (TWSE), OTC (TPEx) and emerging market companies, distinguished by stock_id (use TaiwanStockInfo to look up market type)
• Data update time Monday to Friday 20:00, 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_stock_institutional_investors(
    stock_id="2330",
    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 API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanStockInstitutionalInvestorsBuySell",
    "data_id": "2330",
    "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)
url = 'https://api.finmindtrade.com/api/v4/data'
token = "" # Refer to login to obtain the API key
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanStockInstitutionalInvestorsBuySell",
        data_id= "2330",
        start_date= "2020-04-01",
        end_date= "2020-04-12"
    ),
    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_stock_institutional_investors(
    stock_id_list=['2330', '2317', '2454', '3008'],
    start_date='2024-01-01',
    end_date='2024-12-31',
    use_async=True,
)
cost = datetime.datetime.now() - start
logger.info(cost)

Output

DataFrameSchema

	date	stock_id	buy	name	sell
0	2020-04-01	2330	31304729	Foreign_Investor	29057663
1	2020-04-01	2330	0	Foreign_Dealer_Self	0
2	2020-04-01	2330	900000	Investment_Trust	239000
3	2020-04-01	2330	79000	Dealer_self	807000
4	2020-04-01	2330	189000	Dealer_Hedging	493500

{
    date: str, # date
    stock_id: str, # stock symbol
    buy: int64, # buy
    name: str, # category
    sell: int64 # sell
}

Get all data for a specific date in one request (only available for backer, sponsor members)

Example

PackagePython-requestR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_stock_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 API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanStockInstitutionalInvestorsBuySell",
    "start_date": "2020-04-01",
}
data = requests.get(url, headers=headers, params=parameter)
data = data.json()
data = pd.DataFrame(data['data'])
print(data)

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

Output

DataFrameSchema

	date	stock_id	buy	name	sell
0	2020-04-01	0050	458249	Foreign_Investor	4492000
1	2020-04-01	0050	0	Foreign_Dealer_Self	0
2	2020-04-01	0050	54000	Investment_Trust	0
3	2020-04-01	0050	0	Dealer_self	0
4	2020-04-01	0050	2050000	Dealer_Hedging	905000

{
    date: str, # date
    stock_id: str, # stock symbol
    buy: int64, # buy
    name: str, # category
    sell: int64 # sell
}

---

Institutional Investors Buy/Sell (Wide) TaiwanStockInstitutionalInvestorsBuySellWide

• Same source data as TaiwanStockInstitutionalInvestorsBuySell, but in wide format: one row per trading day, with each institutional investor's buy/sell flattened into its own column — no manual pivot needed.
• Data range: 2005-01-01 ~ now
• Coverage: listed (TWSE), OTC (TPEx) and emerging market companies, distinguished by stock_id (use TaiwanStockInfo to look up market type)
• Data update time Monday to Friday 20:00, the actual update time is based on the API data.

New vs old era columns (important)

The classification of the three institutional investors has changed over the years. This wide table includes all historical classifications; a classification is always 0 in eras where it did not yet exist:

Column	Available era	Other eras
Foreign_Investor (foreign, excl. foreign dealer self)	all	—
Investment_Trust	all	—
Dealer (dealer, combined)	old era (before ~2014-12-01) and Emerging	0 afterwards
Dealer_self (dealer, proprietary)	new era (from 2014-12-01)	0 before
Dealer_Hedging (dealer, hedging)	new era (from 2014-12-01)	0 before
Foreign_Dealer_Self (foreign dealer self)	new era (from 2018-01-15)	0 before

• From 2014-12-01, the combined Dealer was split into Dealer_self (proprietary) and Dealer_Hedging (hedging).
• From 2018-01-15, Foreign_Dealer_Self was split out from foreign investors.
• For a continuous "dealer total" across eras, sum Dealer + Dealer_self + Dealer_Hedging (only one group is non-zero in any era, so there is no double counting).

Example

PackagePython-requestR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_stock_institutional_investors_wide(
    stock_id="2330",
    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 = "" # login to get the token
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanStockInstitutionalInvestorsBuySellWide",
    "data_id": "2330",
    "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)
url = 'https://api.finmindtrade.com/api/v4/data'
token = "" # login to get the token
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanStockInstitutionalInvestorsBuySellWide",
        data_id= "2330",
        start_date= "2020-04-01",
        end_date= "2020-04-12"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrameSchema

	date	stock_id	Foreign_Investor_buy	Foreign_Investor_sell	Foreign_Dealer_Self_buy	Foreign_Dealer_Self_sell	Investment_Trust_buy	Investment_Trust_sell	Dealer_buy	Dealer_sell	Dealer_self_buy	Dealer_self_sell	Dealer_Hedging_buy	Dealer_Hedging_sell
0	2020-04-01	2330	31304729	29057663	0	0	900000	239000	0	0	79000	807000	189000	493500

{
    date: str, # date
    stock_id: str, # stock id
    Foreign_Investor_buy: int64, # foreign investor buy
    Foreign_Investor_sell: int64, # foreign investor sell
    Foreign_Dealer_Self_buy: int64, # foreign dealer self buy
    Foreign_Dealer_Self_sell: int64, # foreign dealer self sell
    Investment_Trust_buy: int64, # investment trust buy
    Investment_Trust_sell: int64, # investment trust sell
    Dealer_buy: int64, # dealer buy (combined, old era)
    Dealer_sell: int64, # dealer sell (combined, old era)
    Dealer_self_buy: int64, # dealer proprietary buy
    Dealer_self_sell: int64, # dealer proprietary sell
    Dealer_Hedging_buy: int64, # dealer hedging buy
    Dealer_Hedging_sell: int64 # dealer hedging sell
}

---

Taiwan Total Market Institutional Investors Buy/Sell TaiwanStockTotalInstitutionalInvestors

• Data range: 2004-04-01 ~ now
• Data update time Monday to Friday 15:00, 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_stock_institutional_investors_total(
    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 API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanStockTotalInstitutionalInvestors",
    "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)
url = 'https://api.finmindtrade.com/api/v4/data'
token = "" # Refer to login to obtain the API key
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanStockTotalInstitutionalInvestors",
        start_date= "2020-01-02",
        end_date='2020-04-12'
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrameSchema

	buy	date	name	sell
0	123150	2020-04-01	Foreign_Dealer_Self	266220
1	3681729831	2020-04-01	Dealer_Hedging	5539788946
2	33759089839	2020-04-01	Foreign_Investor	38466572585
3	3039112340	2020-04-01	Investment_Trust	853138940
4	789316840	2020-04-01	Dealer_self	912143500

{
    buy: int64, # buy
    date: str, # date
    name: str, # category
    sell: int64 # sell
}

---

Foreign Investor Shareholding TaiwanStockShareholding

• Data range: 2004-02-01 ~ now
• Data update time Monday to Friday 21:00, 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_stock_shareholding(
    stock_id="2330",
    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 API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanStockShareholding",
    "data_id": "2330",
    "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)
url = 'https://api.finmindtrade.com/api/v4/data'
token = "" # Refer to login to obtain the API key
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanStockShareholding",
        data_id= "2330",
        start_date= "2020-01-02",
        end_date="2020-04-12"
    ),
    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_stock_shareholding(
    stock_id_list=['2330', '2317', '2454', '3008'],
    start_date='2024-01-01',
    end_date='2024-12-31',
    use_async=True,
)
cost = datetime.datetime.now() - start
logger.info(cost)

Output

DataFrameSchema

	date	stock_id	stock_name	InternationalCode	ForeignInvestmentRemainingShares	ForeignInvestmentShares	ForeignInvestmentRemainRatio	ForeignInvestmentSharesRatio	ForeignInvestmentUpperLimitRatio	ChineseInvestmentUpperLimitRatio	NumberOfSharesIssued	RecentlyDeclareDate	note
0	2020-04-01	2330	台積電	TW0002330008	6309042842	19621337616	24.33	75.66	100	100	25930380458	2019-05-27
1	2020-04-06	2330	台積電	TW0002330008	6304552683	19625827775	24.31	75.68	100	100	25930380458	2019-05-27
2	2020-04-07	2330	台積電	TW0002330008	6283562246	19646818212	24.23	75.76	100	100	25930380458	2019-05-27
3	2020-04-08	2330	台積電	TW0002330008	6273338931	19657041527	24.19	75.8	100	100	25930380458	2019-05-27
4	2020-04-09	2330	台積電	TW0002330008	6267988722	19662391736	24.17	75.82	100	100	25930380458	2019-05-27

{
    date: str, # date
    stock_id: str, # stock symbol
    stock_name: str, # stock name
    InternationalCode: str, # international stock code
    ForeignInvestmentRemainingShares: int64, # remaining shares available for foreign investment
    ForeignInvestmentShares: int64, # shares held by foreign investors
    ForeignInvestmentRemainRatio: float64, # ratio of shares available for foreign investment
    ForeignInvestmentSharesRatio: float64, # foreign shareholding ratio
    ForeignInvestmentUpperLimitRatio: float64, # foreign investment upper limit
    ChineseInvestmentUpperLimitRatio: float64, # mainland Chinese investment upper limit
    NumberOfSharesIssued: int64, # number of shares issued
    RecentlyDeclareDate: str, # most recent declaration date
    note: str # note
}

Get all data for a specific date in one request (only available for backer, sponsor members)

Example

PackagePython-requestR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_stock_shareholding(
    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 API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanStockShareholding",
    "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 API key
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanStockShareholding",
        start_date= "2020-01-02"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrameSchema

	date	stock_id	stock_name	InternationalCode	ForeignInvestmentRemainingShares	ForeignInvestmentShares	ForeignInvestmentRemainRatio	ForeignInvestmentSharesRatio	ForeignInvestmentUpperLimitRatio	ChineseInvestmentUpperLimitRatio	NumberOfSharesIssued	RecentlyDeclareDate	note
0	2020-04-01	0050	元大台灣50	TW0000050004	960256795	94743205	91.01	8.98	100	100	1055000000	2019-07-18
1	2020-04-01	0051	元大中型100	TW0000051002	9471000	29000	99.69	0.3	100	100	9500000	2019-07-18
2	2020-04-01	0052	富邦科技	TW0000052000	29957000	43000	99.85	0.14	100	100	30000000	2019-07-18
3	2020-04-01	0053	元大電子	TW0000053008	6466950	21050	99.67	0.32	100	100	6488000	2019-07-18
4	2020-04-01	0054	元大台商50	TW0000054006	9955000	169000	98.33	1.66	100	100	10124000	2019-07-18

{
    date: str, # date
    stock_id: str, # stock symbol
    stock_name: str, # stock name
    InternationalCode: str, # international stock code
    ForeignInvestmentRemainingShares: int64, # remaining shares available for foreign investment
    ForeignInvestmentShares: int64, # shares held by foreign investors
    ForeignInvestmentRemainRatio: float64, # ratio of shares available for foreign investment
    ForeignInvestmentSharesRatio: float64, # foreign shareholding ratio
    ForeignInvestmentUpperLimitRatio: float64, # foreign investment upper limit
    ChineseInvestmentUpperLimitRatio: float64, # mainland Chinese investment upper limit
    NumberOfSharesIssued: int64, # number of shares issued
    RecentlyDeclareDate: str, # most recent declaration date
    note: str # note
}

---

Shareholders Holding Shares Distribution TaiwanStockHoldingSharesPer (only available for backer, sponsor members)

• Data range: 2010-01-29 ~ now

Example

PackagePython-requestRPackage-Async

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_stock_holding_shares_per(
    stock_id="2330",
    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 API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanStockHoldingSharesPer",
    "data_id": "2330",
    "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)
url = 'https://api.finmindtrade.com/api/v4/data'
token = "" # Refer to login to obtain the API key
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanStockHoldingSharesPer",
        data_id= "2330",
        start_date= "2020-01-02",
        end_date='2020-04-12'
    ),
    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_stock_holding_shares_per(
    stock_id_list=['2330', '2317', '2454', '3008'],
    start_date='2024-01-01',
    end_date='2024-12-31',
    use_async=True,
)
cost = datetime.datetime.now() - start
logger.info(cost)

Output

DataFrameSchema

	date	stock_id	HoldingSharesLevel	people	percent	unit
0	2020-04-01	2330	1-999	165122	0.12	33289900
1	2020-04-01	2330	1000-5000	227692	1.69	440404454
2	2020-04-01	2330	10001-15000	10408	0.49	128127693
3	2020-04-01	2330	100001-200000	1628	0.86	225202876
4	2020-04-01	2330	15001-20000	5068	0.34	89929303

{
    date: str, # date
    stock_id: str, # stock symbol
    HoldingSharesLevel: str, # shareholding level
    people: int64, # number of people
    percent: float64, # ratio
    unit: int64 # number of shares
}

Get all data for a specific date in one request (only available for backer, sponsor members)

Example

PackagePython-requestR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_stock_holding_shares_per(
    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 API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanStockHoldingSharesPer",
    "start_date": "2020-04-01",
}
data = requests.get(url, headers=headers, params=parameter)
data = data.json()
data = pd.DataFrame(data['data'])
print(data)

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

Output

DataFrameSchema

	date	stock_id	HoldingSharesLevel	people	percent	unit
0	2020-04-01	0050	1-999	44173	1.02	10834763
1	2020-04-01	0050	1000-5000	96465	17.7	186791648
2	2020-04-01	0050	5001-10000	10364	7.57	79902735
3	2020-04-01	0050	10001-15000	2819	3.41	36075583
4	2020-04-01	0050	15001-20000	1557	2.69	28426726

{
    date: str, # date
    stock_id: str, # stock symbol
    HoldingSharesLevel: str, # shareholding level
    people: int64, # number of people
    percent: float64, # ratio
    unit: int64 # number of shares
}

---

Securities Lending Transaction Details TaiwanStockSecuritiesLending

• Data range: 2001-05-01 ~ now
• Data update time Monday to Friday 15:00, 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_stock_securities_lending(
    stock_id="2330",
    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 API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanStockSecuritiesLending",
    "data_id": "2330",
    "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)
url = 'https://api.finmindtrade.com/api/v4/data'
token = "" # Refer to login to obtain the API key
response = httr::GET(
url = url,
    query = list(
        dataset="TaiwanStockSecuritiesLending",
        data_id="2330",
        start_date= "2020-01-02",
        end_date='2020-04-12'
    ),
    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_stock_securities_lending(
    stock_id_list=['2330', '2317', '2454', '3008'],
    start_date='2024-01-01',
    end_date='2024-12-31',
    use_async=True,
)
cost = datetime.datetime.now() - start
logger.info(cost)

Output

DataFrameSchema

	date	stock_id	transaction_type	volume	fee_rate	close	original_return_date	original_lending_period
0	2020-04-01	2330	議借	1330	1.36	271.5	2020-09-30	182
1	2020-04-01	2330	議借	800	0.41	271.5	2020-09-30	182
2	2020-04-01	2330	議借	850	0.41	271.5	2020-09-30	182
3	2020-04-01	2330	議借	500	0.5	271.5	2020-09-30	182
4	2020-04-01	2330	議借	160	0.36	271.5	2020-09-30	182

{
    date: str, # date
    stock_id: str, # stock symbol
    transaction_type: str, # transaction type
    volume: int64, # transaction volume
    fee_rate: float64, # transaction fee rate
    close: float64, # closing price
    original_return_date: str, # agreed return date
    original_lending_period: int64 # agreed lending period (days)
}

Get all data for a specific date in one request (only available for backer, sponsor members)

Example

PackagePythonR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_stock_securities_lending(
    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 API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanStockSecuritiesLending",
    "start_date": "2020-04-01",
}
data = requests.get(url, headers=headers, params=parameter)
data = data.json()
data = pd.DataFrame(data['data'])
print(data)

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

Output

DataFrameSchema

	date	stock_id	transaction_type	volume	fee_rate	close	original_return_date	original_lending_period
0	2020-04-01	1101	議借	760	0.25	39	2020-09-30	182
1	2020-04-01	1101	議借	397	0.25	39	2020-09-30	182
2	2020-04-01	1101	競價	436	0.7	39	2020-09-30	182
3	2020-04-01	1102	議借	150	0.25	38.6	2020-09-30	182
4	2020-04-01	1102	議借	770	1.05	38.6	2020-09-30	182

{
    date: str, # date
    stock_id: str, # stock symbol
    transaction_type: str, # transaction type
    volume: int64, # transaction volume
    fee_rate: float64, # transaction fee rate
    close: float64, # closing price
    original_return_date: str, # agreed return date
    original_lending_period: int64 # agreed lending period (days)
}

---

Margin Short Sale Suspension (Short Sale Covering Date) TaiwanStockMarginShortSaleSuspension

• Data range: 2015-01-01 ~ now
• Data update time Monday to Friday 21:00, 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 API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanStockMarginShortSaleSuspension",
    "data_id": "0050",
    "start_date": "2015-01-01",
    "end_date": "2015-04-12",
}
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)
url = 'https://api.finmindtrade.com/api/v4/data'
token = "" # Refer to login to obtain the API key
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanStockMarginShortSaleSuspension",
        data_id="0050",
        start_date= "2015-01-01",
        end_date= "2015-04-12"
    ),
    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_stock_margin_short_sale_suspension(
    stock_id_list=['2330', '2317', '2454', '3008'],
    start_date='2024-01-01',
    end_date='2024-12-31',
    use_async=True,
)
cost = datetime.datetime.now() - start
logger.info(cost)

Output

DataFrameSchema

	stock_id	date	end_date	reason
0	0050	2015-10-20	2015-10-23	分配收益
1	0050	2016-07-22	2016-07-27	分配收益
2	0050	2017-02-02	2017-02-07	分配收益
3	0050	2017-07-25	2017-07-28	分配收益
4	0050	2018-01-23	2018-01-26	分配收益

{
    stock_id: str, # stock symbol
    date: str, # start date
    end_date: str, # end date
    reason: str # suspension reason
}

Get all data for a specific date in one request (only available for backer, sponsor members)

Example

Python-requestR

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanStockMarginShortSaleSuspension",
    "start_date": "2015-10-20",
}
data = requests.get(url, headers=headers, params=parameter)
data = data.json()
data = pd.DataFrame(data['data'])
print(data)

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
token = "" # Refer to login to obtain the API key
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanStockMarginShortSaleSuspension",
        start_date= "2015-10-20"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrameSchema

	stock_id	date	end_date	reason
0	0050	2015-10-20	2015-10-23	分配收益
1	0056	2015-10-20	2015-10-23	分配收益

{
    stock_id: str, # stock symbol
    date: str, # start date
    end_date: str, # end date
    reason: str # suspension reason
}

---

Credit Limit Daily Short Sale Balances TaiwanDailyShortSaleBalances

• Data range: 2005-07-01 ~ now
• Data update time Monday to Friday 21:00, 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_daily_short_sale_balances(
    stock_id="2330",
    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 API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanDailyShortSaleBalances",
    "data_id": "2330",
    "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)
url = 'https://api.finmindtrade.com/api/v4/data'
token = "" # Refer to login to obtain the API key
response = httr::GET(
url = url,
    query = list(
        dataset="TaiwanDailyShortSaleBalances",
        data_id="2330",
        start_date= "2020-01-02",
        end_date='2020-04-12'
    ),
    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_daily_short_sale_balances(
    stock_id_list=['2330', '2317', '2454', '3008'],
    start_date='2024-01-01',
    end_date='2024-12-31',
    use_async=True,
)
cost = datetime.datetime.now() - start
logger.info(cost)

Output

DataFrameSchema

	stock_id	MarginShortSalesPreviousDayBalance	MarginShortSalesShortSales	MarginShortSalesShortCovering	MarginShortSalesStockRedemption	MarginShortSalesCurrentDayBalance	MarginShortSalesQuota	SBLShortSalesPreviousDayBalance	SBLShortSalesShortSales	SBLShortSalesReturns	SBLShortSalesAdjustments	SBLShortSalesCurrentDayBalance	SBLShortSalesQuota	SBLShortSalesShortCovering	date
0	2330	1975000	0	1573000	378000	24000	-2107339478	47947858	487000	0	0	48434858	7526895	0	2020-04-01
1	2330	24000	0	0	24000	0	-2107339478	48434858	44000	60000	0	48418858	7563083	0	2020-04-06
2	2330	0	0	0	0	0	-2107339478	48418858	62000	0	0	48480858	7635835	0	2020-04-07
3	2330	0	0	0	0	0	-2107339478	48480858	933000	7345000	0	42068858	7688249	0	2020-04-08
4	2330	0	398000	0	0	398000	-2107339478	42068858	46000	2000	0	42112858	7642682	0	2020-04-09

{
    stock_id: str, # stock symbol
    MarginShortSalesPreviousDayBalance: int32, # previous day balance (margin short sales)
    MarginShortSalesShortSales: int32, # sell (margin short sales)
    MarginShortSalesShortCovering: int32, # buy (margin short sales)
    MarginShortSalesStockRedemption: int32, # stock redemption (margin short sales)
    MarginShortSalesCurrentDayBalance: int32, # current day balance (margin short sales)
    MarginShortSalesQuota: int32, # quota (margin short sales)
    SBLShortSalesPreviousDayBalance: int32, # previous day balance (SBL short sales)
    SBLShortSalesShortSales: int32, # sell (SBL short sales)
    SBLShortSalesReturns: int32, # returns (SBL short sales)
    SBLShortSalesAdjustments: int32, # current day adjustment (SBL short sales)
    SBLShortSalesCurrentDayBalance: int32, # current day balance (SBL short sales)
    SBLShortSalesQuota: int32, # quota (SBL short sales)
    SBLShortSalesShortCovering: int32, # inventory adjustment (SBL short sales)
    date: str # date
}

Get all data for a specific date in one request (only available for backer, sponsor members)

Example

PackagePython-requestR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_daily_short_sale_balances(
    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 API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanDailyShortSaleBalances",
    "start_date": "2021-05-20",
}
data = requests.get(url, headers=headers, params=parameter)
data = data.json()
data = pd.DataFrame(data['data'])
print(data)

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

Output

DataFrameSchema

	stock_id	MarginShortSalesPreviousDayBalance	MarginShortSalesShortSales	MarginShortSalesShortCovering	MarginShortSalesStockRedemption	MarginShortSalesCurrentDayBalance	MarginShortSalesQuota	SBLShortSalesPreviousDayBalance	SBLShortSalesShortSales	SBLShortSalesReturns	SBLShortSalesAdjustments	SBLShortSalesCurrentDayBalance	SBLShortSalesQuota	SBLShortSalesShortCovering	date
0	0050	2336000	13000	65000	1000	2283000	263750000	25527000	0	0	0	25527000	2397551	0	2020-04-01
1	0051	0	0	0	0	0	2375000	1000	0	0	0	1000	4053	0	2020-04-01
2	0052	0	0	0	0	0	7500000	34000	0	0	0	34000	17168	0	2020-04-01
3	0053	0	0	0	0	0	1622000	0	0	0	0	0	3158	0	2020-04-01
4	0054	0	0	0	0	0	2531000	0	0	0	0	0	1357	0	2020-04-01

{
    stock_id: str, # stock symbol
    MarginShortSalesPreviousDayBalance: int32, # previous day balance (margin short sales)
    MarginShortSalesShortSales: int32, # sell (margin short sales)
    MarginShortSalesShortCovering: int32, # buy (margin short sales)
    MarginShortSalesStockRedemption: int32, # stock redemption (margin short sales)
    MarginShortSalesCurrentDayBalance: int32, # current day balance (margin short sales)
    MarginShortSalesQuota: int32, # quota (margin short sales)
    SBLShortSalesPreviousDayBalance: int32, # previous day balance (SBL short sales)
    SBLShortSalesShortSales: int32, # sell (SBL short sales)
    SBLShortSalesReturns: int32, # returns (SBL short sales)
    SBLShortSalesAdjustments: int32, # current day adjustment (SBL short sales)
    SBLShortSalesCurrentDayBalance: int32, # current day balance (SBL short sales)
    SBLShortSalesQuota: int32, # quota (SBL short sales)
    SBLShortSalesShortCovering: int32, # inventory adjustment (SBL short sales)
    date: str # date
}

---

Securities Trader Information TaiwanSecuritiesTraderInfo

• Provides securities trader related information, used with the Taiwan Stock Trading Daily Report by Branch (TaiwanStockTradingDailyReport). Use the securities_trader_id to query all stock transactions of a particular securities trader.

Example

PackagePython-requestR

from FinMind.data import DataLoader

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

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

Output

DataFrameSchema

	securities_trader_id	securities_trader	date	address	phone
0	1020	合庫	2011-12-02	台北市大安區忠孝東路四段325號2樓(部分)、經紀部複委託科地址：台北市松山區長安東路二段225號5樓	02-27528000
1	1021	合庫- 台中	2011-12-02	台中市西區民權路91號6樓	04-22255141
2	1022	合庫-台南	2011-12-02	台南市北區成功路48號3樓	06-2260148
3	1023	合庫-高雄	2011-12-02	高雄市大勇路97號5樓	07-5319755
4	1024	合庫-嘉義	2011-12-02	嘉義市國華街279號2樓	05-2220016

{
    securities_trader_id: str, # securities trader code
    securities_trader: str, # securities trader name
    date: str, # opening date
    address: str, # address
    phone: str # phone
}

---

Taiwan Stock Trading Daily Report by Branch (query by stock_id) TaiwanStockTradingDailyReport (only available for sponsor members)

• Provides Taiwan stock trading by branch information for listed (TWSE), OTC, and emerging stocks!
• Data range: 2021-06-30 ~ now
• Due to the large data volume, only one day of data is provided per request.
• Data update time Monday to Friday 21:00, the actual update time is based on the API data.
• Some data is missing on the following dates: 2022-10-31~2022-11-03, 2023-01-11~2023-01-17.
• Enabling Async can significantly reduce data fetch time. In Colab tests, downloading 2,175 stocks takes only 4 minutes 20 seconds.

A price of 0 on emerging-stock dealer (自營商) branches is expected, not missing data

Emerging stocks (where TaiwanStockInfo reports the stock_id as emerging) trade on a quote-driven negotiation market run by recommending securities firms, unlike the central-matching auction used for listed/OTC stocks. The recommending firms act as market makers, continuously quoting bid/ask and filling orders with their own capital and inventory — i.e. the dealer (自營商) branch, whose securities_trader_id ends in T. Because that daily market-making position trades both sides across many price levels, there is no single representative execution price, so price is reported as 0 for that branch row (buy/sell share counts are still correct).

• This is a structural characteristic of the emerging market, not missing or erroneous data; ordinary (non-dealer) branches of the same emerging stock still carry normal execution prices.
• Listed (TWSE) and OTC stocks are unaffected — every branch, including dealers, has an execution price.
• Reference: TPEx — Emerging Stock Trading System.

Example

PackagePackage-AsyncPython-requestR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_stock_trading_daily_report(
    stock_id="2330",
    date='2022-06-16',
)
print(df)

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

token = ""

data_loader = DataLoader()
data_loader.login_by_token(token)

date = "2025-12-08"
start = datetime.datetime.now()
df = data_loader.taiwan_stock_trading_daily_report(
    date=date,
    use_async=True,
)
cost = datetime.datetime.now() - start
logger.info(cost)
# 0:04:20

import requests
import pandas as pd

url = 'https://api.finmindtrade.com/api/v4/taiwan_stock_trading_daily_report'
token = "" # Refer to login to obtain the API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "data_id": "2330",
    "date": "2022-06-16",
}
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 API key
url = 'https://api.finmindtrade.com/api/v4/taiwan_stock_trading_daily_report'
response = httr::GET(
    url = url,
    query = list(
        data_id="2330",
        start_date= "2022-06-16",
        token = token # Refer to login to obtain the API key
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrameSchema

	securities_trader	price	buy	sell	securities_trader_id	stock_id	date
0	合庫	508	4000	2000	1020	2330	2022-06-16
1	合庫	509	3480	0	1020	2330	2022-06-16
2	合庫	510	2310	50	1020	2330	2022-06-16
3	合庫	511	1169	0	1020	2330	2022-06-16
4	合庫	512	1300	10000	1020	2330	2022-06-16

{
    securities_trader: str, # securities trader name
    price: float64, # transaction price
    buy: int32, # shares bought
    sell: int32, # shares sold
    securities_trader_id: str, # securities trader code
    stock_id: str, # stock symbol
    date: str # date
}

---

Taiwan Stock Trading Daily Report by Branch (query by securities_trader_id) TaiwanStockTradingDailyReport (only available for sponsor members)

• Provides Taiwan stock trading by branch information for listed (TWSE), OTC, and emerging stocks!
• Data range: 2021-06-30 ~ now
• Due to the large data volume, only one day of data is provided per request.
• Data update time Monday to Friday 21:00, the actual update time is based on the API data.
• Some data is missing on the following dates: 2022-10-31~2022-11-03, 2023-01-11~2023-01-17.

Example

PackagePython-requestR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_stock_trading_daily_report(
    securities_trader_id="1102",
    date='2022-06-16',
)
print(df)

import requests
import pandas as pd

token = "" # Refer to login to obtain the API key
headers = {"Authorization": f"Bearer {token}"}
url = 'https://api.finmindtrade.com/api/v4/taiwan_stock_trading_daily_report'
parameter = {
    "securities_trader_id": "1020",
    "date": "2022-06-16",
}
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 API key
url = 'https://api.finmindtrade.com/api/v4/taiwan_stock_trading_daily_report'
response = httr::GET(
    url = url,
    query = list(
        securities_trader_id="1020",
        start_date= "2022-06-16",
        token = token # Refer to login to obtain the API key
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrameSchema

	securities_trader	price	buy	sell	securities_trader_id	stock_id	date
0	合庫	122.25	19000	0	1020	0050	2022-06-16
1	合庫	122.3	80000	0	1020	0050	2022-06-16
2	合庫	122.35	10000	0	1020	0050	2022-06-16
3	合庫	122.5	1300	0	1020	0050	2022-06-16
4	合庫	122.55	20000	0	1020	0050	2022-06-16
...	...	...	...	...	...	...	...
3211	合庫	107	1000	50000	1020	9958	2022-06-16
3212	合庫	107.5	0	32000	1020	9958	2022-06-16
3213	合庫	108	0	2000	1020	9958	2022-06-16
3214	合庫	108.5	150	0	1020	9958	2022-06-16
3215	合庫	16.05	1000	0	1020	9962	2022-06-16

{
    securities_trader: str, # securities trader name
    price: float64, # transaction price
    buy: int32, # shares bought
    sell: int32, # shares sold
    securities_trader_id: str, # securities trader code
    stock_id: str, # stock symbol
    date: str # date
}

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: 2021-06-30 ~ now.
• Providing the dataset and date parameters returns the branch-level trading data of all stocks for that day.
• Downloads the whole-day parquet via a signed URL, avoiding file-by-file queries — suitable for batch analysis covering the entire market.
• Data update time: Monday to Friday 21:00. The actual update time is based on the API data.
• Some data is missing on these dates: 2022-10-31~2022-11-03, 2023-01-11~2023-01-17.

Example

PackagePython-requestR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_stock_trading_daily_report(
    date='2022-06-16',
    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": "TaiwanStockTradingDailyReport",
    "date": "2022-06-16",
}
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="TaiwanStockTradingDailyReport",
        date= "2022-06-16"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
con = content(response, "raw")
data <- read_parquet(con)
close(con)
head(data)

Output

DataFrameSchema

	securities_trader	price	buy	sell	securities_trader_id	stock_id	date
0	合庫	508	4000	2000	1020	2330	2022-06-16
1	合庫	509	3480	0	1020	2330	2022-06-16
2	合庫	510	2310	50	1020	2330	2022-06-16
3	合庫	511	1169	0	1020	2330	2022-06-16
4	合庫	512	1300	10000	1020	2330	2022-06-16

{
    securities_trader: str, # securities trader name
    price: float64, # deal price
    buy: int32, # shares bought
    sell: int32, # shares sold
    securities_trader_id: str, # securities trader code
    stock_id: str, # stock symbol
    date: str # date
}

---

Taiwan Stock Warrant Trading Daily Report by Branch (query by stock_id) TaiwanStockWarrantTradingDailyReport (only available for sponsor members)

• Data range: 2023-06-21 ~ now
• Due to the large data volume, only one day of data is provided per request.
• Data update time Monday to Friday 01:00, 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_stock_warrant_trading_daily_report(
    stock_id="084655",
    date='2023-06-21',
)
print(df)

import requests
import pandas as pd

token = "" # Refer to login to obtain the API key
headers = {"Authorization": f"Bearer {token}"}
url = 'https://api.finmindtrade.com/api/v4/taiwan_stock_warrant_trading_daily_report'
parameter = {
    "data_id": "084655",
    "date": "2023-06-21",
}
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/taiwan_stock_warrant_trading_daily_report'
token = "" # Refer to login to obtain the API key
response = httr::GET(
    url = url,
    query = list(
        data_id="084655",
        start_date= "2023-06-21",
        token = token # Refer to login to obtain the API key
    ),
    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_stock_warrant_trading_daily_report(
    date=date,
    use_async=True,
)
cost = datetime.datetime.now() - start
logger.info(cost)

Output

DataFrameSchema

	securities_trader	price	buy	sell	securities_trader_id	stock_id	date
0	元富	2.48	0	4000	5920	084655	2023-06-21
1	凱基	2.48	4000	0	9200	084655	2023-06-21

{
    securities_trader: str, # securities trader name
    price: float64, # transaction price
    buy: int32, # shares bought
    sell: int32, # shares sold
    securities_trader_id: str, # securities trader code
    stock_id: str, # stock symbol
    date: str # date
}

---

Taiwan Stock Warrant Trading Daily Report by Branch (query by securities_trader_id) TaiwanStockWarrantTradingDailyReport (only available for sponsor members)

• Data range: 2023-06-21 ~ now
• Due to the large data volume, only one day of data is provided per request.
• Data update time Monday to Friday 23:00, 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/taiwan_stock_warrant_trading_daily_report'
token = "" # Refer to login to obtain the API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "securities_trader_id": "5920",
    "date": "2023-06-21",
}
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/taiwan_stock_warrant_trading_daily_report'
token = "" # Refer to login to obtain the API key
response = httr::GET(
    url = url,
    query = list(
        securities_trader_id="5920",
        start_date= "2023-06-21",
        token = token # Refer to login to obtain the API key
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrameSchema

	securities_trader	price	buy	sell	securities_trader_id	stock_id	date
0	元富	0.97	50000	0	5920	07741U	2023-06-21
1	元富	0.98	50000	0	5920	07741U	2023-06-21
2	元富	1.52	100000	0	5920	07742U	2023-06-21
3	元富	1.56	49000	0	5920	07742U	2023-06-21

{
    securities_trader: str, # securities trader name
    price: float64, # transaction price
    buy: int32, # shares bought
    sell: int32, # shares sold
    securities_trader_id: str, # securities trader code
    stock_id: str, # stock symbol
    date: str # date
}

Fetch all warrant trading 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: 2023-06-21 ~ now.
• Providing the dataset and date parameters returns the branch-level trading data of all warrants for that day.
• Downloads the whole-day parquet via a signed URL, avoiding file-by-file queries — suitable for batch analysis covering all warrants in the market.
• Updated daily. 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_stock_warrant_trading_daily_report(
    date='2023-06-21',
    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": "TaiwanStockWarrantTradingDailyReport",
    "date": "2023-06-21",
}
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="TaiwanStockWarrantTradingDailyReport",
        date= "2023-06-21"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
con = content(response, "raw")
data <- read_parquet(con)
close(con)
head(data)

Output

DataFrameSchema

	securities_trader	price	buy	sell	securities_trader_id	stock_id	date
0	元富	2.48	0	4000	5920	084655	2023-06-21
1	凱基	2.48	4000	0	9200	084655	2023-06-21

{
    securities_trader: str, # securities trader name
    price: float64, # deal price
    buy: int32, # shares bought
    sell: int32, # shares sold
    securities_trader_id: str, # securities trader code
    stock_id: str, # stock symbol
    date: str # date
}

---

Taiwan Stock Government Bank Buy/Sell TaiwanStockGovernmentBankBuySell (only available for sponsor members)

• Data range: 2021-06-30 ~ now
• Due to the large data volume, only one day of data is provided per request.
• Data update time Monday to Friday 23:30, 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_stock_government_bank_buy_sell(
    start_date='2023-01-17',
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanStockGovernmentBankBuySell",
    "start_date": "2023-01-17",
}
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 API key
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanStockGovernmentBankBuySell",
        start_date= "2023-01-17"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrameSchema

	date	stock_id	buy_amount	sell_amount	buy	sell	bank_name
0	2023-01-17	0050	43992298.6	53309904.25	372595	451744	兆豐
1	2023-01-17	5202	288.0	303.50	20	20	第一
2	2023-01-17	5202	0.0	59.45	0	4	華南
3	2023-01-17	5203	82800.0	0.00	1000	0	兆豐
4	2023-01-17	5203	249000.0	583600.00	3000	7000	臺銀

{
    date: str, # date
    stock_id: str, # stock symbol
    buy_amount: float64, # buy amount
    sell_amount: float64, # sell amount
    buy: int64, # shares bought
    sell: int64, # shares sold
    bank_name: str # bank name
}

Taiwan Total Exchange Margin Maintenance TaiwanTotalExchangeMarginMaintenance (only available for backer, sponsor members)

• Data range: 2001-01-05 ~ now
• Data update time Monday to Friday 21:00, 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_total_exchange_margin_maintenance(
    start_date='2024-04-01',
    end_date='2024-05-01'
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanTotalExchangeMarginMaintenance",
    "start_date": "2020-04-01",
    "end_date": "2020-05-01",
}
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 API key
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanTotalExchangeMarginMaintenance",
        start_date= "2024-04-01",
        end_date='2024-05-01'
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrameSchema

	date	TotalExchangeMarginMaintenance
0	2024-04-01	166.007
1	2024-04-02	167.079
2	2024-04-03	167.085
3	2024-04-08	167.119
4	2024-04-09	167.095

{
    date: str, # date
    TotalExchangeMarginMaintenance: float64 # total exchange margin maintenance ratio
}

Daily Securities Trader Branch Aggregate Statistics TaiwanStockTradingDailyReportSecIdAgg (only available for sponsor members)

• Provides Taiwan stock trading by branch information for listed (TWSE), OTC, and emerging stocks!
• Data range: 2021-06-30 ~ now
• Data update time Monday to Friday 21:00, 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_stock_trading_daily_report_secid_agg(
    stock_id="2330",
    securities_trader_id="1020",
    start_date= "2024-07-01",
    end_date="2024-07-15",
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/taiwan_stock_trading_daily_report_secid_agg"
token = "" # Refer to login to obtain the API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "data_id": "2330",
    "securities_trader_id": "1020",
    "start_date": "2024-07-01",
    "end_date": '2024-07-15',
}
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/taiwan_stock_trading_daily_report_secid_agg'
token = "" # Refer to login to obtain the API key
response = httr::GET(
    url = url,
    query = list(
        data_id="2330",
        securities_trader_id="1020",
        start_date= "2024-07-01",
        end_date='2024-07-15'
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrameSchema

	securities_trader	securities_trader_id	stock_id	date	buy_volume	sell_volume	buy_price	sell_price
0	合庫	1020	2330	2024-07-01	12157	12460	968.08	973.84
0	合庫	1020	2330	2024-07-02	12735	21885	964.54	964.63
0	合庫	1020	2330	2024-07-03	10535	29381	973.16	974.69
0	合庫	1020	2330	2024-07-04	28107	59459	1001.99	1000.88
0	合庫	1020	2330	2024-07-05	10435	11075	1004.18	1004.5

{
    securities_trader: str, # securities trader name
    securities_trader_id: str, # securities trader code
    stock_id: str, # stock symbol
    date: str, # date
    buy_volume: int64, # total shares bought
    sell_volume: int64, # total shares sold
    buy_price: float, # average buy price
    sell_price: float, # average sell price
}

Block Trading Daily Report TaiwanStockBlockTradingDailyReport (only available for sponsor members)

• Data range: 2026-04-28 ~ now
• Only requires a date input, no stock symbol needed.

Example

PythonR

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the API key
parameter = {
    "dataset": "TaiwanStockBlockTradingDailyReport",
    "start_date": "2026-04-28",
    "token": token,
}
resp = requests.get(url, params=parameter)
data = resp.json()
data = pd.DataFrame(data["data"])
print(data.head())

library(httr)
library(data.table)
url = "https://api.finmindtrade.com/api/v4/data"
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanStockBlockTradingDailyReport",
        start_date="2026-04-28",
        token=""
    )
)
data = content(response)
df = do.call('rbind', lapply(data$data, as.data.frame))
head(df)

Output

Schema

{
    securities_trader: str, # securities trader
    price: float, # price
    buy: int, # shares bought
    sell: int, # shares sold
    trade_type: str, # trade type (paired/conversion, etc.)
    securities_trader_id: str, # securities trader code
    stock_id: str, # stock symbol
    date: str, # date
}

Block Trade Daily Transactions TaiwanStockBlockTrade (only available for sponsor members)

• Data range: 2005-04-04 ~ now
• Covers block trades (tick-by-tick) for listed (TWSE) and OTC stocks.

Example

PackagePythonR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_stock_block_trade(
    stock_id="2330",
    start_date="2026-04-01",
    end_date="2026-04-30",
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanStockBlockTrade",
    "data_id": "2330",
    "start_date": "2026-04-01",
    "end_date": "2026-04-30",
}
data = requests.get(url, headers=headers, params=parameter)
data = data.json()
data = pd.DataFrame(data["data"])
print(data.head())

library(httr)
library(data.table)
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the API key
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanStockBlockTrade",
        data_id="2330",
        start_date="2026-04-01",
        end_date="2026-04-30",
        token=token
    )
)
data = content(response)
df = do.call("rbind", lapply(data$data, as.data.frame))
head(df)

Output

DataFrameSchema

	date	stock_id	trade_type	price	volume	trading_money
0	2026-04-01	2330	配對交易	1871.29	25000	46782250
1	2026-04-01	2330	配對交易	1871.28	25000	46782000
2	2026-04-01	2330	配對交易	1865.28	15000	27979200
3	2026-04-01	2330	配對交易	1855	1078000	1999690000
4	2026-04-01	2330	配對交易	1849.42	137104	253562880

{
    date: str, # date
    stock_id: str, # stock symbol
    trade_type: str, # trade type (paired trade, etc.)
    price: float, # transaction price
    volume: int, # transaction volume
    trading_money: int, # trading money
}

Loan Collateral Balance TaiwanStockLoanCollateralBalance (only available for sponsor members)

• Data range: 2006-10-02 ~ now
• Data update time Monday to Friday after market close, the actual update time is based on the API data.
• Includes the collateral balance (in thousand shares) for margin purchase, securities firm securities-business loans, securities firm unrestricted-purpose loans, securities finance secured loans, and securities finance settlement margin for listed (TWSE) and OTC stocks.

Example

PackagePythonR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_stock_loan_collateral_balance(
    stock_id="2330",
    start_date="2026-04-01",
    end_date="2026-04-30",
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanStockLoanCollateralBalance",
    "data_id": "2330",
    "start_date": "2026-04-01",
    "end_date": "2026-04-30",
}
data = requests.get(url, headers=headers, params=parameter)
data = data.json()
data = pd.DataFrame(data["data"])
print(data.head())

library(httr)
library(data.table)
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the API key
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanStockLoanCollateralBalance",
        data_id="2330",
        start_date="2026-04-01",
        end_date="2026-04-30",
        token=token
    )
)
data = content(response)
df = do.call("rbind", lapply(data$data, as.data.frame))
head(df)

Output

DataFrameSchema

	date	stock_id	market	MarginPreviousDayBalance	MarginBuy	MarginSell	MarginCashRedemption	MarginCurrentDayBalance	MarginNextDayQuota	SecuritiesFirmLoanPreviousDayBalance	SecuritiesFirmLoanBuy	SecuritiesFirmLoanSell	SecuritiesFirmLoanCashRedemption	SecuritiesFirmLoanReplacement	SecuritiesFirmLoanCurrentDayBalance	SecuritiesFirmLoanNextDayQuota	UnrestrictedLoanPreviousDayBalance	UnrestrictedLoanBuy	UnrestrictedLoanSell	UnrestrictedLoanCashRedemption	UnrestrictedLoanReplacement	UnrestrictedLoanCurrentDayBalance	UnrestrictedLoanNextDayQuota	SecuritiesFinanceSecuredLoanPreviousDayBalance	SecuritiesFinanceSecuredLoanBuy	SecuritiesFinanceSecuredLoanSell	SecuritiesFinanceSecuredLoanCashRedemption	SecuritiesFinanceSecuredLoanReplacement	SecuritiesFinanceSecuredLoanCurrentDayBalance	SecuritiesFinanceSecuredLoanNextDayQuota	SettlementMarginPreviousDayBalance	SettlementMarginBuy	SettlementMarginSell	SettlementMarginCashRedemption	SettlementMarginReplacement	SettlementMarginCurrentDayBalance	SettlementMarginNextDayQuota
0	2026-04-01	2330	集中市場	26608	1268	1485	38	26353	6483131	21	0	0	0	0	21	1296626	119953	851	142	46	215	120401	2593252	16700	111	11	30	6	16764	0	0	0	0	0	0	0	1296626
1	2026-04-02	2330	集中市場	26353	1312	873	194	26598	6483131	21	0	0	0	0	21	1296626	120401	621	139	70	416	120397	2593252	16764	134	6	19	18	16855	0	0	0	0	0	0	0	1296626
2	2026-04-07	2330	集中市場	26598	391	970	19	26000	6483131	21	0	0	0	0	21	1296626	120397	539	54	36	236	120610	2593252	16855	160	2	67	29	16917	0	0	0	0	0	0	0	1296626
3	2026-04-08	2330	集中市場	26000	2882	2057	28	26797	6483131	21	65	0	0	0	86	1296626	120616	776	75	134	222	120961	2593252	16917	88	21	28	9	16947	0	0	0	0	0	0	0	1296626
4	2026-04-09	2330	集中市場	26604	998	953	13	26636	6483131	86	0	65	0	0	21	1296626	120961	481	19	83	198	121142	2593252	16947	126	1	27	13	17032	0	0	0	0	0	0	0	1296626

{
    date: str, # date
    stock_id: str, # stock symbol
    market: str, # market type (TWSE/OTC)
    MarginPreviousDayBalance: int, # margin purchase - previous day balance
    MarginBuy: int, # margin purchase - buy
    MarginSell: int, # margin purchase - sell
    MarginCashRedemption: int, # margin purchase - cash redemption
    MarginCurrentDayBalance: int, # margin purchase - current day balance
    MarginNextDayQuota: int, # margin purchase - next business day quota
    SecuritiesFirmLoanPreviousDayBalance: int, # securities firm securities-business loan - previous day balance
    SecuritiesFirmLoanBuy: int, # securities firm securities-business loan - buy
    SecuritiesFirmLoanSell: int, # securities firm securities-business loan - sell
    SecuritiesFirmLoanCashRedemption: int, # securities firm securities-business loan - cash redemption
    SecuritiesFirmLoanReplacement: int, # securities firm securities-business loan - replacement
    SecuritiesFirmLoanCurrentDayBalance: int, # securities firm securities-business loan - current day balance
    SecuritiesFirmLoanNextDayQuota: int, # securities firm securities-business loan - next business day quota
    UnrestrictedLoanPreviousDayBalance: int, # securities firm unrestricted-purpose loan - previous day balance
    UnrestrictedLoanBuy: int, # securities firm unrestricted-purpose loan - buy
    UnrestrictedLoanSell: int, # securities firm unrestricted-purpose loan - sell
    UnrestrictedLoanCashRedemption: int, # securities firm unrestricted-purpose loan - cash redemption
    UnrestrictedLoanReplacement: int, # securities firm unrestricted-purpose loan - replacement
    UnrestrictedLoanCurrentDayBalance: int, # securities firm unrestricted-purpose loan - current day balance
    UnrestrictedLoanNextDayQuota: int, # securities firm unrestricted-purpose loan - next business day quota
    SecuritiesFinanceSecuredLoanPreviousDayBalance: int, # securities finance secured loan - previous day balance
    SecuritiesFinanceSecuredLoanBuy: int, # securities finance secured loan - buy
    SecuritiesFinanceSecuredLoanSell: int, # securities finance secured loan - sell
    SecuritiesFinanceSecuredLoanCashRedemption: int, # securities finance secured loan - cash redemption
    SecuritiesFinanceSecuredLoanReplacement: int, # securities finance secured loan - replacement
    SecuritiesFinanceSecuredLoanCurrentDayBalance: int, # securities finance secured loan - current day balance
    SecuritiesFinanceSecuredLoanNextDayQuota: int, # securities finance secured loan - next business day quota
    SettlementMarginPreviousDayBalance: int, # securities finance settlement margin - previous day balance
    SettlementMarginBuy: int, # securities finance settlement margin - buy
    SettlementMarginSell: int, # securities finance settlement margin - sell
    SettlementMarginCashRedemption: int, # securities finance settlement margin - cash redemption
    SettlementMarginReplacement: int, # securities finance settlement margin - replacement
    SettlementMarginCurrentDayBalance: int, # securities finance settlement margin - current day balance
    SettlementMarginNextDayQuota: int, # securities finance settlement margin - next business day quota
}

Disposition Securities Period TaiwanStockDispositionSecuritiesPeriod (only available for backer, sponsor members)

• Data range: 2001-01-01 ~ now
• Data covers listed (TWSE), OTC, and emerging stocks.
• Data update time Monday to Saturday 20:00~23:00, the actual update time is based on the API data.

Example

PackagePythonR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_stock_disposition_securities_period(
    stock_id="6477",
    start_date='2025-01-01',
    end_date='2025-02-01',
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanStockDispositionSecuritiesPeriod",
    "data_id": "6477",
    "start_date": "2025-01-01",
    "end_date": "2025-02-01",
}
data = requests.get(url, headers=headers, params=parameter)
data = data.json()
data = pd.DataFrame(data['data'])

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
token = "" # Refer to login to obtain the API key
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanStockDispositionSecuritiesPeriod",
        data_id= "6477",
        start_date= "2025-01-01",
        end_date= "2025-02-01"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table

Output

DataFrameSchema

	date	stock_id	stock_name	disposition_cnt	condition	measure	period_start	period_end
0	2025-01-09	6477	安集	1	連續三次及當日沖銷標準	第一次處置	2025-01-10	2025-02-05

{
    date: str, # announcement date
    stock_id: str, # stock symbol
    stock_name: str, # stock name
    disposition_cnt: int32, # cumulative count
    condition: str, # disposition condition
    measure: str, # disposition measure
    period_start: str, # disposition start date
    period_end: str, # disposition end date
}

Get all data for a specific date in one request (only available for backer, sponsor members)

Example

PackagePython-requestR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_stock_disposition_securities_period(
    start_date='2025-01-09',
    end_date: "2025-01-09",
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanStockDispositionSecuritiesPeriod",
    "start_date": "2025-01-09",
    "end_date": "2025-01-09",
}
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 = "" # Refer to login to obtain the API key
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanStockDispositionSecuritiesPeriod",
        start_date= "2025-01-09",
        end_date: "2025-01-09",
        token = "" # Refer to login to obtain the API key
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrameSchema

	date	stock_id	stock_name	disposition_cnt	condition	measure	period_start	period_end
0	2025-01-09	6477	安集	1	連續三次及當日沖銷標準	第一次處置	2025-01-10	2025-02-05
1	2025-01-09	9103	美德醫療-DR	1	最近十個營業日已有六次	第二次處置	2025-01-10	2025-02-03

{
    date: str, # announcement date
    stock_id: str, # stock symbol
    stock_name: str, # stock name
    disposition_cnt: int32, # cumulative count
    condition: str, # disposition condition
    measure: str, # disposition measure
    period_start: str, # disposition start date
    period_end: str, # disposition end date
}

---

Day Trading Borrowing Fee Rate TaiwanStockDayTradingBorrowingFeeRate (only available for backer, sponsor members)

• Data range: 2015-06-01 ~ now
• Data update time Monday to Friday 19:00~22:00, the actual update time is based on the API data.

Example

PackagePythonR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_stock_day_trading_borrowing_fee_rate(
    stock_id="2330",
    start_date='2024-12-01',
    end_date='2025-01-01',
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanStockDayTradingBorrowingFeeRate",
    "data_id": "2330",
    "start_date": "2024-12-01",
    "end_date": "2025-01-01",
}
data = requests.get(url, headers=headers, params=parameter)
data = data.json()
data = pd.DataFrame(data['data'])

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
token = "" # Refer to login to obtain the API key
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanStockDayTradingBorrowingFeeRate",
        data_id= "2330",
        start_date= "2024-12-01",
        end_date= "2025-01-01"
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table

Output

DataFrameSchema

	date	stock_id	stock_name	InvestorBorrowedShares	InvestorBorrowingFeeRate
0	2024-12-02	6477	安集	15000	7

{
    date: str, # announcement date
    stock_id: str, # stock symbol
    stock_name: str, # stock name
    InvestorBorrowedShares: int32, # borrowed shares
    InvestorBorrowingFeeRate: float64, # borrowing fee rate
}

Get all data for a specific date in one request (only available for backer, sponsor members)

Example

PackagePython-requestR

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
df = api.taiwan_stock_disposition_securities_period(
    start_date='2024-12-02',
    end_date: "2024-12-02",
)

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
token = "" # Refer to login to obtain the API key
headers = {"Authorization": f"Bearer {token}"}
parameter = {
    "dataset": "TaiwanStockDayTradingBorrowingFeeRate",
    "start_date": "2024-12-02",
    "end_date": "2024-12-02",
}
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)
url = 'https://api.finmindtrade.com/api/v4/data'
token = "" # Refer to login to obtain the API key
response = httr::GET(
    url = url,
    query = list(
        dataset="TaiwanStockDayTradingBorrowingFeeRate",
        start_date= "2024-12-02",
        end_date: "2024-12-02",
        token = "" # Refer to login to obtain the API key
    ),
    add_headers(Authorization = paste("Bearer", token))
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrameSchema

	date	stock_id	stock_name	InvestorBorrowedShares	InvestorBorrowingFeeRate
0	2024-12-02	00631L	元大台灣50正2	5000	1
1	2024-12-02	1438	三地開發	1	3
2	2024-12-02	2312	金寶	1000	2
3	2024-12-02	2324	仁寶	1000	0.1
4	2024-12-02	2330	台積電	15000	7

{
    date: str, # announcement date
    stock_id: str, # stock symbol
    stock_name: str, # stock name
    InvestorBorrowedShares: int32, # borrowed shares
    InvestorBorrowingFeeRate: float64, # borrowing fee rate
}

---