技術面

在台股技術面，我們擁有 12 種資料集，如下:

• 台股總覽 TaiwanStockInfo
• 台股總覽(含權證) TaiwanStockInfoWithWarrant
• 台灣股價資料表 TaiwanStockPrice
• 台灣還原股價資料表 TaiwanStockPriceAdj
• 台灣股價歷史逐筆資料表 TaiwanStockPriceTick
• 台灣個股PER、PBR資料表 TaiwanStockPER
• 每5秒委託成交統計 TaiwanStockStatisticsOfOrderBookAndTrade
• 台股加權指數 TaiwanVariousIndicators5Seconds
• 當日沖銷交易標的及成交量值 TaiwanStockDayTrading
• 加權、櫃買報酬指數 TaiwanStockTotalReturnIndex
• 台灣個股十年線資料表 TaiwanStock10Year
• 台股分 K 資料表 TaiwanStockKBar

---

台股總覽 TaiwanStockInfo

• 這張資料表主要是列出台灣所有上市上櫃的股票名稱，代碼和產業類別
• 資料更新時間 每天 1:30，實際更新時間以 API 資料為主

Example

Package

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
# api.login(user_id='user_id',password='password')
df = api.taiwan_stock_info()

Python-request

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
parameter = {
    "dataset": "TaiwanStockInfo",
    "token": "", # 參考登入，獲取金鑰
}
resp = requests.get(url, params=parameter)
data = resp.json()
data = pd.DataFrame(data["data"])
print(data.head())

R

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
url = url,
query = list(
    dataset = "TaiwanStockInfo",
    token = "" # 參考登入，獲取金鑰
    )
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrame

	industry_category	stock_id	stock_name	type	date
0	ETF	0050	元大台灣50	twse	2021-10-05
1	ETF	0051	元大中型100	twse	2021-10-05
2	ETF	0052	富邦科技	twse	2021-10-05
3	ETF	0053	元大電子	twse	2021-10-05
4	ETF	0054	元大台商50	twse	2021-10-05

Schema

{
    industry_category: str,
    stock_id: str,
    stock_name: str,
    type: str,
    date: str
}

---

台股總覽(含權證) TaiwanStockInfoWithWarrant

• 這張資料表主要是列出台灣所有上市上櫃的股票、權證名稱，代碼和產業類別
• 資料量超過 5 萬筆
• 資料更新時間 每天 1:30，實際更新時間以 API 資料為主

Example

Package

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
# api.login(user_id='user_id',password='password')
df = api.taiwan_stock_info_with_warrant()

Python-request

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
parameter = {
    "dataset": "TaiwanStockInfoWithWarrant",
    "token": "", # 參考登入，獲取金鑰
}
resp = requests.get(url, params=parameter)
data = resp.json()
data = pd.DataFrame(data["data"])
print(data.head())

R

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
url = url,
query = list(
    dataset = "TaiwanStockInfoWithWarrant",
    token = "" # 參考登入，獲取金鑰
    )
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrame

	industry_category	stock_id	stock_name	type	date
0	ETF	0050	元大台灣50	twse	2021-10-05
1	ETF	0051	元大中型100	twse	2021-10-05
2	ETF	0052	富邦科技	twse	2021-10-05
3	ETF	0053	元大電子	twse	2021-10-05
4	ETF	0054	元大台商50	twse	2021-10-05

Schema

{
    industry_category: str,
    stock_id: str,
    stock_name: str,
    type: str,
    date: str
}

---

股價日成交資訊 TaiwanStockPrice

• 資料區間：1994-10-01 ~ now
• 資料更新時間 星期一至五 17:30，實際更新時間以 API 資料為主

Example

Package

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
# api.login(user_id='user_id',password='password')
df = api.taiwan_stock_daily(
    stock_id='2330',
    start_date='2020-04-02',
    end_date='2020-04-12'
)

Python-request

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
parameter = {
    "dataset": "TaiwanStockPrice",
    "data_id": "2330",
    "start_date": "2020-04-02",
    "end_date": "2020-04-12",
    "token": "", # 參考登入，獲取金鑰
}
resp = requests.get(url, params=parameter)
data = resp.json()
data = pd.DataFrame(data["data"])
print(data.head())

R

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
url = url,
query = list(
    dataset="TaiwanStockPrice",
    data_id= "2330",
    start_date= "2020-04-02",
    end_date= "2020-04-08",
    token = "" # 參考登入，獲取金鑰
)
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrame

	date	stock_id	Trading_Volume	Trading_money	open	max	min	close	spread	Trading_turnover
0	2020-04-06	2330	59712754	16324198154	273	275.5	270	275.5	4	19971
1	2020-04-07	2330	48887346	13817936851	283.5	284	280.5	283	7.5	24281
2	2020-04-08	2330	38698826	11016972354	285	285.5	283	285	2	19126
3	2020-04-09	2330	29276430	8346209654	287.5	288	282.5	283	-2	15271
4	2020-04-10	2330	28206858	7894277586	280	282	279	279.5	-3.5	15833

Schema

{
    date: str,
    stock_id: str,
    Trading_Volume: int64,
    Trading_money: int64,
    open: float64,
    max: float64,
    min: float64,
    close: float64,
    spread: float64,
    Trading_turnover: float32
}

一次拿特定日期，所有資料(只限 backer、sponsor 使用)

Example

Package

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
# api.login(user_id='user_id',password='password')
df = api.taiwan_stock_daily(
    start_date='2020-04-06',
)

Python-request

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
parameter = {
    "dataset": "TaiwanStockPrice",
    "start_date": "2020-04-06",
    "token": "", # 參考登入，獲取金鑰
}
resp = requests.get(url, params=parameter)
data = resp.json()
data = pd.DataFrame(data["data"])
print(data.head())

R

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
url = url,
query = list(
    dataset="TaiwanStockPrice",
    start_date= "2020-04-06",
    token = "" # 參考登入，獲取金鑰
)
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrame

	date	stock_id	Trading_Volume	Trading_money	open	max	min	close	spread	Trading_turnover
0	2020-04-06	0050	12207626	935731083	76.95	77.1	75.75	77.05	1.15	5824
1	2020-04-06	0051	33000	953030	29.05	29.05	28.74	29.05	0.38	21
2	2020-04-06	0052	178700	10660088	59.4	60.05	58.75	60	1.25	56
3	2020-04-06	0053	17000	589750	34.66	35	34.48	34.84	0.18	17
4	2020-04-06	0054	10000	200040	19.87	20.03	19.87	20.03	0	4

Schema

{
    date: str,
    stock_id: str,
    Trading_Volume: int64,
    Trading_money: int64,
    open: float64,
    max: float64,
    min: float64,
    close: float64,
    spread: float64,
    Trading_turnover: int64
}

---

台灣還原股價資料表 TaiwanStockPriceAdj (只限 backer、sponsor 會員使用)

• 資料區間：1994-10-01 ~ now
• 資料更新時間 星期一至五 17:30，實際更新時間以 API 資料為主

Example

Package

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
# api.login(user_id='user_id',password='password')
df = api.taiwan_stock_daily_adj(
    stock_id='2330',
    start_date='2020-04-02',
    end_date='2020-04-12'
)

Python-request

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
parameter = {
    "dataset": "TaiwanStockPriceAdj",
    "data_id": "2330",
    "start_date": "2020-04-02",
    "end_date": "2020-04-12",
    "token": "", # 參考登入，獲取金鑰
}
resp = requests.get(url, params=parameter)
data = resp.json()
data = pd.DataFrame(data["data"])
print(data.head())

R

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
url = url,
query = list(
    dataset="TaiwanStockPriceAdj",
    data_id= "2330",
    start_date= "2020-04-02",
    end_date= "2020-04-08",
    token = "" # 參考登入，獲取金鑰
)
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrame

	date	stock_id	Trading_Volume	Trading_money	open	max	min	close	spread	Trading_turnover
0	2020-04-06	2330	59712754	16324198154	273	275.5	270	275.5	4	19971
1	2020-04-07	2330	48887346	13817936851	283.5	284	280.5	283	7.5	24281
2	2020-04-08	2330	38698826	11016972354	285	285.5	283	285	2	19126
3	2020-04-09	2330	29276430	8346209654	287.5	288	282.5	283	-2	15271
4	2020-04-10	2330	28206858	7894277586	280	282	279	279.5	-3.5	15833

Schema

{
    date: str,
    stock_id: str,
    Trading_Volume: int64,
    Trading_money: int64,
    open: float64,
    max: float64,
    min: float64,
    close: float64,
    spread: float64,
    Trading_turnover: float32
}

一次拿特定日期，所有資料(只限 backer、sponsor 使用)

Example

Python-request

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
parameter = {
    "dataset": "TaiwanStockPriceAdj",
    "start_date": "2020-04-06",
    "token": "", # 參考登入，獲取金鑰
}
resp = requests.get(url, params=parameter)
data = resp.json()
data = pd.DataFrame(data["data"])
print(data.head())

R

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
url = url,
query = list(
    dataset="TaiwanStockPriceAdj",
    start_date= "2020-04-06",
    token = "" # 參考登入，獲取金鑰
)
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrame

	date	stock_id	Trading_Volume	Trading_money	open	max	min	close	spread	Trading_turnover
0	2020-04-06	0050	12207626	935731083	76.95	77.1	75.75	77.05	1.15	5824
1	2020-04-06	0051	33000	953030	29.05	29.05	28.74	29.05	0.38	21
2	2020-04-06	0052	178700	10660088	59.4	60.05	58.75	60	1.25	56
3	2020-04-06	0053	17000	589750	34.66	35	34.48	34.84	0.18	17
4	2020-04-06	0054	10000	200040	19.87	20.03	19.87	20.03	0	4

Schema

{
    date: str,
    stock_id: str,
    Trading_Volume: int64,
    Trading_money: int64,
    open: float64,
    max: float64,
    min: float64,
    close: float64,
    spread: float64,
    Trading_turnover: float32
}

---

台灣股價歷史逐筆資料表 TaiwanStockPriceTick (只限 backer、sponsor 會員使用)

(由於資料量過大，單次請求只提供一天資料)

• 資料區間：2019-01-01 ~ now
• 輸入 dataset、stock_id、start_date 參數，會回傳 start_date 當天資料。
• 資料更新時間 星期一至五 15:30，實際更新時間以 API 資料為主

Example

Package

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
# api.login(user_id='user_id',password='password')
df = api.taiwan_stock_tick(
    stock_id='2330',
    date='2020-01-02'
)

Python-request

import requests
import pandas as pd

url = "https://api.finmindtrade.com/api/v4/data"
parameter = {
    "dataset": "TaiwanStockPriceTick",
    "data_id": "2330",
    "start_date": "2020-01-02",
    "token": "", # 參考登入，獲取金鑰
}
resp = requests.get(url, params=parameter)
data = resp.json()
data = pd.DataFrame(data["data"])
print(data.head())

R

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
url = url,
query = list(
    dataset="TaiwanStockPriceTick",
    data_id= "2330",
    start_date= "2020-01-02",
    token = "" # 參考登入，獲取金鑰
)
)
data = content(response)
df = do.call('cbind',data$data) %>%
data.table
head(df)

Output

DataFrame

	date	stock_id	deal_price	volume	Time	TickType
0	2020-01-02	2330	332.5	520	09:00:00.000	0
1	2020-01-02	2330	332.5	520	09:00:00.646	0
2	2020-01-02	2330	333	45	09:00:05.000	0
3	2020-01-02	2330	333	45	09:00:05.660	0
4	2020-01-02	2330	333	22	09:00:10.000	0

Schema

{
    date: str,
    stock_id: str,
    deal_price: float64,
    volume: int64,
    Time: str,
    TickType: int64
}

---

個股PER、PBR資料表 TaiwanStockPER

• 資料區間：2005-10-01 ~ now
• 資料更新時間 星期一至五 18:00，實際更新時間以 API 資料為主

Example

Package

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
# api.login(user_id='user_id',password='password')
df = api.taiwan_stock_per_pbr(
    stock_id='2330',
    start_date='2020-01-02'
)

Python-request

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
parameter = {
    "dataset": "TaiwanStockPER",
    "data_id": "2330",
    "start_date": "2020-04-01",
    "token": "", # 參考登入，獲取金鑰
}
data = requests.get(url, params=parameter)
data = data.json()
data = pd.DataFrame(data['data'])
print(data.head())

R

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(url = url,
                    query = list(
                    dataset="TaiwanStockPER",
                    data_id= "2330",
                    start_date= "2020-01-02",
                    token = "" # 參考登入，獲取金鑰
                    )
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrame

	date	stock_id	dividend_yield	PER	PBR
0	2020-01-02	2330	2.36	26.69	5.54
1	2020-01-03	2330	2.36	26.73	5.55
2	2020-01-06	2330	2.41	26.14	5.42
3	2020-01-07	2330	2.43	25.94	5.38
4	2020-01-08	2330	2.43	25.94	5.38

Schema

{
    date: str,
    stock_id: str,
    dividend_yield: float64,
    PER: float64,
    PBR: float64
}

---

每5秒委託成交統計 TaiwanStockStatisticsOfOrderBookAndTrade

(由於資料量過大，單次請求只提供一天資料)

• 資料區間：2005-01-01 ~ now

Example

Package

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
# api.login(user_id='user_id',password='password')
df = api.taiwan_stock_book_and_trade(
    date='2021-01-07'
)

Python-request

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
parameter = {
    "dataset": "TaiwanStockStatisticsOfOrderBookAndTrade",
    "start_date": "2021-01-07",
    "token": "", # 參考登入，獲取金鑰
}
data = requests.get(url, params=parameter)
data = data.json()
data = pd.DataFrame(data['data'])
print(data.head())

R

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
url = url,
query = list(
    dataset="TaiwanStockStatisticsOfOrderBookAndTrade",
    start_date= "2021-01-07",
    token = "" # 參考登入，獲取金鑰
)
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrame

	Time	TotalBuyOrder	TotalBuyVolume	TotalSellOrder	TotalSellVolume	TotalDealOrder	TotalDealVolume	TotalDealMoney	date
0	09:00:00	298618	3229222	365465	1730137	0	0	0	2021-01-07
1	09:00:05	301246	3254929	367886	1751034	17535	97251	4515	2021-01-07
2	09:00:10	304171	3283698	370338	1770414	31370	150557	7041	2021-01-07
3	09:00:15	307686	3325195	372828	1782960	40083	177080	8088	2021-01-07
4	09:00:20	310927	3345735	375220	1792055	47250	198536	9137	2021-01-07

Schema

{
    Time: str,
    TotalBuyOrder: str,
    TotalBuyVolume: int64,
    TotalSellOrder: int64,
    TotalSellVolume: int64,
    TotalDealOrder: int64,
    TotalDealVolume: int64,
    TotalDealMoney: int64,
    date: str,
}

---

加權指數 TaiwanVariousIndicators5Seconds

(由於資料量過大，單次請求只提供一天資料)

• 資料區間：2005-01-01 ~ now

Example

Package

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
# api.login(user_id='user_id',password='password')
df = api.tse(
    date='2020-07-01'
)

Python-request

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
parameter = {
    "dataset": "TaiwanVariousIndicators5Seconds",
    "start_date": "2020-07-01",
    "token": "", # 參考登入，獲取金鑰
}
data = requests.get(url, params=parameter)
data = data.json()
data = pd.DataFrame(data['data'])
print(data.head())

R

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
url = url,
query = list(
    dataset="TaiwanVariousIndicators5Seconds",
    start_date="2020-07-01",
    token = "" # 參考登入，獲取金鑰
)
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrame

	date	TAIEX
0	2020-07-01 09:00:00	11621.2
1	2020-07-01 09:00:05	11622.6
2	2020-07-01 09:00:10	11632.4
3	2020-07-01 09:00:15	11643.5
4	2020-07-01 09:00:20	11644.2

Schema

{
    date: str,
    TAIEX: float64
}

---

當日沖銷交易標的及成交量值 TaiwanStockDayTrading

• 資料區間：2014-01-01 ~ now
• 資料更新時間 星期一至五 21:30，實際更新時間以 API 資料為主

Example

Package

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
# api.login(user_id='user_id',password='password')
df = api.taiwan_stock_day_trading(
    stock_id='2330',
    start_date='2020-04-02',
    end_date='2020-04-12'
)

Python-request

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
parameter = {
    "dataset": "TaiwanStockDayTrading",
    "data_id": "2330",
    "start_date": "2020-04-02",
    "end_date": "2020-04-12",
    "token": "", # 參考登入，獲取金鑰
}
resp = requests.get(url, params=parameter)
data = resp.json()
data = pd.DataFrame(data["data"])
print(data.head())

R

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
url = url,
query = list(
    dataset="TaiwanStockDayTrading",
    data_id= "2330",
    start_date= "2020-04-02",
    end_date= "2020-04-08",
    token = "" # 參考登入，獲取金鑰
)
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrame

	stock_id	date	BuyAfterSale	Volume	BuyAmount	SellAmount
0	2330	2020-04-06	Y	8122000	2215280000	2218094500
1	2330	2020-04-07	Y	5128000	1450483500	1447872000
2	2330	2020-04-08	Y	2467000	702411500	702367000
3	2330	2020-04-09	Y	2583000	736745500	734035500
4	2330	2020-04-10	Y	1590000	445516000	444576000

Schema

{
    stock_id: str,
    date: str,
    BuyAfterSale: int64,
    Volume: int64,
    BuyAmount: int64,
    SellAmount: int64
}

一次拿特定日期，所有資料(只限 backer、sponsor 會員使用)

Example

Package

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
# api.login(user_id='user_id',password='password')
df = api.taiwan_stock_day_trading(
    start_date='2020-04-06',
)

Python-request

url = "https://api.finmindtrade.com/api/v4/data"
parameter = {
    "dataset": "TaiwanStockDayTrading",
    "start_date": "2020-04-06",
    "token": "", # 參考登入，獲取金鑰
}
res = requests.get(url, params=parameter)
temp = res.json()
data = pd.DataFrame(temp["data"])
print(data.head())

R

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
url = url,
query = list(
    dataset="TaiwanStockDayTrading",
    start_date= "2020-04-06",
    token = "" # 參考登入，獲取金鑰
)
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrame

	stock_id	date	BuyAfterSale	Volume	BuyAmount	SellAmount
0	0050	2020-04-06		1296000	99116100	99343200
1	0051	2020-04-06		2000	57680	57560
2	0052	2020-04-06		9000	536200	537700
3	0053	2020-04-06		0	0	0
4	0054	2020-04-06		0	0	0

Schema

{
    stock_id: str,
    date: str,
    BuyAfterSale: int64,
    Volume: int64,
    BuyAmount: int64,
    SellAmount: int64
}

---

加權、櫃買報酬指數 TaiwanStockTotalReturnIndex

• 資料區間：2003-01-01 ~ now
• 資料更新時間 星期一至五 16:50，實際更新時間以 API 資料為主

Example

Package

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
# api.login(user_id='user_id',password='password')
df = api.taiwan_stock_total_return_index(
    index_id="TAIEX",
    start_date='2020-04-02',
    end_date='2020-04-12'
)

Python-request

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
parameter = {
    "dataset": "TaiwanStockTotalReturnIndex",
    "data_id": "TAIEX", # 發行量加權股價報酬指數
    # "data_id": "TPEx", # 櫃買指數與報酬指數
    "start_date": "2020-04-02",
    "end_date": "2020-04-12",
    "token": "", # 參考登入，獲取金鑰
}
resp = requests.get(url, params=parameter)
data = resp.json()
data = pd.DataFrame(data["data"])
print(data.head())

R

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
url = url,
query = list(
    dataset="TaiwanStockTotalReturnIndex",
    data_id= "TAIEX", # 發行量加權股價報酬指數
    # data_id= "TPEx", # 櫃買指數與報酬指數
    start_date= "2020-04-02",
    end_date= "2020-04-08",
    token = "" # 參考登入，獲取金鑰
)
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrame

	price	stock_id	date
0	18356.5	TAIEX	2020-04-06
1	18688.6	TAIEX	2020-04-07
2	18952.7	TAIEX	2020-04-08
3	18922.6	TAIEX	2020-04-09
4	18994	TAIEX	2020-04-10

Schema

{
    price: float64,
    stock_id: str,
    date: str
}

---

台灣個股十年線資料表 TaiwanStock10Year (只限 backer、sponsor 會員使用)

• 資料區間：2011-01-24 ~ now
• 透過2500個交易日所計算出的平均價格
• 資料更新時間 星期一至五 20:00，實際更新時間以 API 資料為主

Example

Package

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
# api.login(user_id='user_id',password='password')
df = api.taiwan_stock_10year(
    stock_id='2330',
    start_date='2020-04-02',
    end_date='2020-04-12'
)

Python-request

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
parameter = {
    "dataset": "TaiwanStock10Year",
    "data_id": "2330",
    "start_date": "2020-04-02",
    "end_date": "2020-04-12",
    "token": "", # 參考登入，獲取金鑰
}
resp = requests.get(url, params=parameter)
data = resp.json()
data = pd.DataFrame(data["data"])
print(data.head())

R

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
url = url,
query = list(
    dataset="TaiwanStock10Year",
    data_id= "2330",
    start_date= "2020-04-02",
    end_date= "2020-04-12",
    token = "" # 參考登入，獲取金鑰
)
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrame

	date	stock_id	close
0	2020-04-06	2330	150.16
1	2020-04-07	2330	150.25
2	2020-04-08	2330	150.34
3	2020-04-09	2330	150.43
4	2020-04-10	2330	150.52

Schema

{
    date: str,
    stock_id: str,
    close: float64
}

一次拿特定日期，所有資料(只限 backer、sponsor 使用)

Example

Package

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
# api.login(user_id='user_id',password='password')
df = api.taiwan_stock_10year(
    start_date='2020-04-06',
)

Python-request

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
parameter = {
    "dataset": "TaiwanStock10Year",
    "start_date": "2020-04-06",
    "token": "", # 參考登入，獲取金鑰
}
resp = requests.get(url, params=parameter)
data = resp.json()
data = pd.DataFrame(data["data"])
print(data.head())

R

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
url = url,
query = list(
    dataset="TaiwanStock10Year",
    start_date= "2020-04-06",
    token = "" # 參考登入，獲取金鑰
)
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrame

	date	stock_id	close
0	2020-04-06	0050	66.5
1	2020-04-06	0053	28.68
2	2020-04-06	0055	14.31
3	2020-04-06	0056	24.59
4	2020-04-06	0061	16.28

Schema

{
    date: str,
    stock_id: str,
    close: float64,
}

---

台股分 K 資料表 TaiwanStockKBar (只限 sponsor 會員使用)

(由於資料量過大，單次請求只提供一天資料)

• 資料區間：2019-01-01 ~ now
• 資料更新時間 星期一至五 15:50，實際更新時間以 API 資料為主

Example

Package

from FinMind.data import DataLoader

api = DataLoader()
# api.login_by_token(api_token='token')
# api.login(user_id='user_id',password='password')
df = api.taiwan_stock_bar(
    stock_id='2330',
    date="2023-09-22"
)

Python-request

import requests
import pandas as pd
url = "https://api.finmindtrade.com/api/v4/data"
parameter = {
    "dataset": "TaiwanStockKBar",
    "data_id": "2330",
    "start_date": "2023-09-22",
    "token": "", # 參考登入，獲取金鑰
}
resp = requests.get(url, params=parameter)
data = resp.json()
data = pd.DataFrame(data["data"])
print(data.head())

R

library(httr)
library(data.table)
library(dplyr)
url = 'https://api.finmindtrade.com/api/v4/data'
response = httr::GET(
url = url,
query = list(
    dataset="TaiwanStockKBar",
    data_id= "2330",
    start_date= "2023-09-22",
    token = "" # 參考登入，獲取金鑰
)
)
data = content(response)
df = data$data %>%
do.call('rbind',.) %>%
data.table
head(df)

Output

DataFrame

	date	minute	stock_id	open	high	low	close	volume
0	2023-09-22	09:00:00	2330	523	524	522	524	3893
1	2023-09-22	09:01:00	2330	524	524	523	524	159
2	2023-09-22	09:02:00	2330	523	524	522	523	548
3	2023-09-22	09:03:00	2330	522	523	522	522	208
4	2023-09-22	09:04:00	2330	522	523	522	522	179

Schema

{
    date: str,
    minute: str,
    stock_id: str,
    open: float64,
    high: float64,
    low: float64,
    close: float64,
    volume: float32
}