% content df = do.call ( 'cbind' , data $ data ) %>% data.table head ( df ) Output DataFrame date leading leading_notrend coincident coincident_notrend lagging lagging_notrend monitoring monitoring_color 0 2024-01-01 92.32 99.85 90.8 98.21 91.82 99.31 27 G 1 2024-02-01 92.71 100.35 91.45 98.99 91.68 99.25 29 G 2 2024-03-01 93.19 100.95 92.28 99.97 91.6 99.23 31 G 3 2024-04-01 93.75 101.63 93.23 101.07 91.52 99.22 35 YR 4 2024-05-01 94.29 102.28 94.21 102.19 91.41 99.16 36 YR Schema { date: str, leading: float32, leading_notrend: float32, coincident: float32, coincident_notrend: float32, lagging: float32, lagging_notrend: float32, monitoring: float32, monitoring_color: str }","title":"\u53f0\u7063\u6bcf\u6708\u666f\u6c23\u5c0d\u7b56\u4fe1\u865f\u8868 TaiwanBusinessIndicator (\u53ea\u9650 backer\u3001sponsor \u6703\u54e1\u4f7f\u7528)"},{"location":"tutor/TaiwanMarket/RealTime/","text":"\u5728\u53f0\u80a1\u5373\u6642\u8cc7\u6599\uff0c\u6211\u5011\u64c1\u6709 4 \u7a2e\u8cc7\u6599\u96c6\uff0c\u5982\u4e0b: \u53f0\u80a1\u5373\u6642\u8cc7\u8a0a taiwan_stock_tick_snapshot \u671f\u8ca8\u3001\u9078\u64c7\u6b0a\u5373\u6642\u5831\u50f9\u7e3d\u89bd TaiwanFutOptTickInfo \u53f0\u80a1\u671f\u8ca8\u5373\u6642\u8cc7\u8a0a taiwan_futures_snapshot \u53f0\u80a1\u9078\u64c7\u6b0a\u5373\u6642\u8cc7\u8a0a taiwan_options_snapshot \u53f0\u80a1\u5373\u6642\u8cc7\u8a0a taiwan_stock_tick_snapshot (\u53ea\u9650 sponsor \u6703\u54e1\u4f7f\u7528) \u00b6 (\u7d04 10 \u79d2\u66f4\u65b0\u4e00\u6b21) 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_snapshot ( stock_id = \"2330\" ) Python-request import requests import pandas as pd url = \"https://api.finmindtrade.com/api/v4/taiwan_stock_tick_snapshot\" parameter = { \"data_id\" : \"2330\" , # \"data_id\": [\"2330\", \"2317\"], # \u4e00\u6b21\u62ff\u591a\u500b # \"data_id\": \"\", # \u4e00\u6b21\u5168\u90e8 \"token\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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/taiwan_stock_tick_snapshot\" response = httr :: GET ( url = url , query = list ( data_id = \"2330\" , # data_id=c(\"2330\", \"2317\"), # \u4e00\u6b21\u62ff\u591a\u500b # data_id=\"\", # \u4e00\u6b21\u5168\u90e8 token = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) data = content ( response ) df = data $ data %>% do.call ( 'rbind' , . ) %>% data.table head ( df ) Output DataFrame amount average_price buy_price buy_volume change_price change_rate close high low open sell_price sell_volume total_amount total_volume volume volume_ratio yesterday_volume date stock_id TickType 0 610000 611.42 609 208 3 0.49 610 614 608 614 610 821 5578566000 9124 1 0.49 18606 2021-12-08 10:31:23.098000 2330 1 Schema { amount: int32, average_price: float64, buy_price: float64, buy_volume: int64, change_price: str, change_rate: float64, close: float64, high: float64, low: float64, open: float64, sell_price: float64, sell_volume: int64, total_amount: int32, total_volume: int64, volume: int64, volume_ratio: float64, yesterday_volume: int64, date: str, stock_id: str, TickType: str # 0: \u7121\u6cd5\u5224\u65b7, 1: \u8ce3\u76e4\u6210\u4ea4, 2: \u8cb7\u76e4\u6210\u4ea4 } \u671f\u8ca8\u3001\u9078\u64c7\u6b0a\u5373\u6642\u5831\u50f9\u7e3d\u89bd TaiwanFutOptTickInfo \u00b6 \u76ee\u524d\u652f\u63f4\uff0c\u53f0\u6307\u671f\u3001\u53f0\u6307\u9078\u64c7\u6b0a\uff0c\u5373\u6642\u5831\u50f9 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_futopt_tick_info () Python-request import requests import pandas as pd url = \"https://api.finmindtrade.com/api/v4/data\" parameter = { \"dataset\" : \"TaiwanFutOptTickInfo\" , \"token\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"TaiwanFutOptTickInfo\" , token = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) data = response %>% content df = do.call ( 'cbind' , data $ data ) %>% data.table head ( df ) Output DataFrame code callput date name listing_date expire_price update_date 0 CAO00675R3 \u8ce3\u6b0a 2023/06 \u5357\u4e9e\u80a1\u7968\u9078\u64c7\u6b0a 2023-04-20 67.5 2023-06-05 1 CAO00850R3 \u8ce3\u6b0a 2023/06 \u5357\u4e9e\u80a1\u7968\u9078\u64c7\u6b0a 2023-01-31 85 2023-06-05 2 CBO00360R3 \u8ce3\u6b0a 2023/06 \u4e2d\u92fc\u80a1\u7968\u9078\u64c7\u6b0a 2023-01-31 36 2023-06-05 3 CCO00430R3 \u8ce3\u6b0a 2023/06 \u806f\u96fb\u80a1\u7968\u9078\u64c7\u6b0a 2023-04-20 43 2023-06-05 4 CCO00440R3 \u8ce3\u6b0a 2023/06 \u806f\u96fb\u80a1\u7968\u9078\u64c7\u6b0a 2023-01-31 44 2023-06-05 Schema { code: str, callput: str, date: str, name: str, listing_date: str, expire_price: float64, update_date: str } \u53f0\u80a1\u671f\u8ca8\u5373\u6642\u8cc7\u8a0a taiwan_futures_snapshot (\u53ea\u9650 sponsor \u6703\u54e1\u4f7f\u7528) \u00b6 (\u76ee\u524d\u652f\u63f4\u53f0\u6307\u671f\u3001\u7d04 30 \u79d2\u66f4\u65b0\u4e00\u6b21) 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 = data_loader . taiwan_futures_snapshot ( futures_id = \"TXF\" ) Python-request import requests import pandas as pd url = \"https://api.finmindtrade.com/api/v4/taiwan_futures_snapshot\" parameter = { \"data_id\" : \"TXF\" , # \u76ee\u524d\u53ea\u652f\u63f4\u53f0\u6307\u671f \"token\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } resp = requests . get ( url , params = parameter ) data = resp . json () df = pd . DataFrame ( data [ \"data\" ]) print ( df . head ()) R library ( httr ) library ( data.table ) library ( dplyr ) url = \"https://api.finmindtrade.com/api/v4/taiwan_futures_snapshot\" response = httr :: GET ( url = url , query = list ( data_id = \"TXF\" , # \u76ee\u524d\u53ea\u652f\u63f4\u53f0\u6307\u671f token = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) data = content ( response ) df = data $ data %>% do.call ( 'rbind' , . ) %>% data.table head ( df ) Output DataFrame open high low close change_price change_rate average_price volume total_volume amount total_amount yesterday_volume buy_price buy_volume sell_price sell_volume volume_ratio date futures_id TickType 0 16720 16800 16714 16762 51 0.31 16757.2 1 52822 16762 885151394 46576 16760 61 16765 8 1.13 2023-06-03 04:59:59.243 TXFR1 2 1 16720 16800 16714 16762 51 0.31 16757.2 1 52822 16762 885151394 46576 16760 61 16765 8 1.13 2023-06-03 04:59:59.243 TXFF3 2 2 16290 16291 16290 16291 68 0.42 16290.5 1 2 16291 32581 3 16266 2 16500 2 0.67 2023-06-02 23:01:10.244 TXFC4 1 3 16409 16468 16400 16449 64 0.39 16431.9 1 110 16449 1807505 90 16424 1 16450 1 1.22 2023-06-03 03:00:34.248 TXFH3 1 4 16300 16373 16300 16335 56 0.34 16340.8 1 10 16335 163408 8 16315 1 16355 1 1.25 2023-06-03 03:36:00.561 TXFL3 1 Schema { open: float64, high: float64, low: float64, close: float64, change_price: float64, change_rate: float64, average_price: float64, volume: int64, total_volume\" int64, amount: str, total_amount: str, yesterday_volume: int64, buy_price: float64, buy_volume: int64, sell_price: float64, sell_volume: int64, volume_ratio: float64, date: str, options_id: str, TickType: str # 0: \u7121\u6cd5\u5224\u65b7, 1: \u8ce3\u76e4\u6210\u4ea4, 2: \u8cb7\u76e4\u6210\u4ea4 } \u53f0\u80a1\u9078\u64c7\u6b0a\u5373\u6642\u8cc7\u8a0a taiwan_options_snapshot (\u53ea\u9650 sponsor \u6703\u54e1\u4f7f\u7528) \u00b6 (\u76ee\u524d\u652f\u63f4\u53f0\u6307\u9078\u64c7\u6b0a\u3001\u7d04 30 \u79d2\u66f4\u65b0\u4e00\u6b21) 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 = data_loader . taiwan_options_snapshot ( options_id = \"TXO\" ) Python-request import requests import pandas as pd url = \"https://api.finmindtrade.com/api/v4/taiwan_options_snapshot\" parameter = { \"data_id\" : \"TXO\" , # \u76ee\u524d\u53ea\u652f\u63f4\u53f0\u6307\u9078\u64c7\u6b0a \"token\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } resp = requests . get ( url , params = parameter ) data = resp . json () df = pd . DataFrame ( data [ \"data\" ]) print ( df . head ()) R library ( httr ) library ( data.table ) library ( dplyr ) url = \"https://api.finmindtrade.com/api/v4/taiwan_options_snapshot\" response = httr :: GET ( url = url , query = list ( data_id = \"TXO\" , # \u76ee\u524d\u53ea\u652f\u63f4\u53f0\u6307\u671f token = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) data = content ( response ) df = data $ data %>% do.call ( 'rbind' , . ) %>% data.table head ( df ) Output DataFrame open high low close change_price change_rate average_price volume total_volume amount total_amount yesterday_volume buy_price buy_volume sell_price sell_volume volume_ratio date options_id TickType 0 46 46 45.5 46 -12 -20.69 45.75 17 40 782 1830 2 0.6 5 0 0 20 2023-06-02 13:07:35.299 TXO14300T3 1 1 60 65 46.5 46.5 -17.5 -27.34 58.76 1 939 46 55172 936 48.5 2 65 3 1 2023-06-03 02:07:52.807 TXO15400S3 2 2 905 910 875 875 -45 -4.89 891.25 1 4 875 3565 2 1.1 6 0 0 2 2023-06-02 20:06:14.720 TXO17000U3 2 3 695 695 695 695 25 3.73 695 1 1 695 695 13 6.3 1 1030 2 0.08 2023-06-02 15:29:16.150 TXO16400L3 1 4 0 0 0 0 0 0 0 0 0 0 0 0 1.1 3 0 0 0 2023-06-04 08:30:00.000 TXO15000H3 0 Schema { open: float64, high: float64, low: float64, close: float64, change_price: float64, change_rate: float64, average_price: float64, volume: int64, total_volume\" int64, amount: str, total_amount: str, yesterday_volume: int64, buy_price: float64, buy_volume: int64, sell_price: float64, sell_volume: int64, volume_ratio: float64, date: str, options_id: str, TickType: str # 0: \u7121\u6cd5\u5224\u65b7, 1: \u8ce3\u76e4\u6210\u4ea4, 2: \u8cb7\u76e4\u6210\u4ea4 }","title":"\u5373\u6642\u8cc7\u6599"},{"location":"tutor/TaiwanMarket/RealTime/#taiwan_stock_tick_snapshot-sponsor","text":"(\u7d04 10 \u79d2\u66f4\u65b0\u4e00\u6b21) 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_snapshot ( stock_id = \"2330\" ) Python-request import requests import pandas as pd url = \"https://api.finmindtrade.com/api/v4/taiwan_stock_tick_snapshot\" parameter = { \"data_id\" : \"2330\" , # \"data_id\": [\"2330\", \"2317\"], # \u4e00\u6b21\u62ff\u591a\u500b # \"data_id\": \"\", # \u4e00\u6b21\u5168\u90e8 \"token\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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/taiwan_stock_tick_snapshot\" response = httr :: GET ( url = url , query = list ( data_id = \"2330\" , # data_id=c(\"2330\", \"2317\"), # \u4e00\u6b21\u62ff\u591a\u500b # data_id=\"\", # \u4e00\u6b21\u5168\u90e8 token = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) data = content ( response ) df = data $ data %>% do.call ( 'rbind' , . ) %>% data.table head ( df ) Output DataFrame amount average_price buy_price buy_volume change_price change_rate close high low open sell_price sell_volume total_amount total_volume volume volume_ratio yesterday_volume date stock_id TickType 0 610000 611.42 609 208 3 0.49 610 614 608 614 610 821 5578566000 9124 1 0.49 18606 2021-12-08 10:31:23.098000 2330 1 Schema { amount: int32, average_price: float64, buy_price: float64, buy_volume: int64, change_price: str, change_rate: float64, close: float64, high: float64, low: float64, open: float64, sell_price: float64, sell_volume: int64, total_amount: int32, total_volume: int64, volume: int64, volume_ratio: float64, yesterday_volume: int64, date: str, stock_id: str, TickType: str # 0: \u7121\u6cd5\u5224\u65b7, 1: \u8ce3\u76e4\u6210\u4ea4, 2: \u8cb7\u76e4\u6210\u4ea4 }","title":"\u53f0\u80a1\u5373\u6642\u8cc7\u8a0a taiwan_stock_tick_snapshot (\u53ea\u9650 sponsor \u6703\u54e1\u4f7f\u7528)"},{"location":"tutor/TaiwanMarket/RealTime/#taiwanfutopttickinfo","text":"\u76ee\u524d\u652f\u63f4\uff0c\u53f0\u6307\u671f\u3001\u53f0\u6307\u9078\u64c7\u6b0a\uff0c\u5373\u6642\u5831\u50f9 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_futopt_tick_info () Python-request import requests import pandas as pd url = \"https://api.finmindtrade.com/api/v4/data\" parameter = { \"dataset\" : \"TaiwanFutOptTickInfo\" , \"token\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"TaiwanFutOptTickInfo\" , token = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) data = response %>% content df = do.call ( 'cbind' , data $ data ) %>% data.table head ( df ) Output DataFrame code callput date name listing_date expire_price update_date 0 CAO00675R3 \u8ce3\u6b0a 2023/06 \u5357\u4e9e\u80a1\u7968\u9078\u64c7\u6b0a 2023-04-20 67.5 2023-06-05 1 CAO00850R3 \u8ce3\u6b0a 2023/06 \u5357\u4e9e\u80a1\u7968\u9078\u64c7\u6b0a 2023-01-31 85 2023-06-05 2 CBO00360R3 \u8ce3\u6b0a 2023/06 \u4e2d\u92fc\u80a1\u7968\u9078\u64c7\u6b0a 2023-01-31 36 2023-06-05 3 CCO00430R3 \u8ce3\u6b0a 2023/06 \u806f\u96fb\u80a1\u7968\u9078\u64c7\u6b0a 2023-04-20 43 2023-06-05 4 CCO00440R3 \u8ce3\u6b0a 2023/06 \u806f\u96fb\u80a1\u7968\u9078\u64c7\u6b0a 2023-01-31 44 2023-06-05 Schema { code: str, callput: str, date: str, name: str, listing_date: str, expire_price: float64, update_date: str }","title":"\u671f\u8ca8\u3001\u9078\u64c7\u6b0a\u5373\u6642\u5831\u50f9\u7e3d\u89bd TaiwanFutOptTickInfo"},{"location":"tutor/TaiwanMarket/RealTime/#taiwan_futures_snapshot-sponsor","text":"(\u76ee\u524d\u652f\u63f4\u53f0\u6307\u671f\u3001\u7d04 30 \u79d2\u66f4\u65b0\u4e00\u6b21) 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 = data_loader . taiwan_futures_snapshot ( futures_id = \"TXF\" ) Python-request import requests import pandas as pd url = \"https://api.finmindtrade.com/api/v4/taiwan_futures_snapshot\" parameter = { \"data_id\" : \"TXF\" , # \u76ee\u524d\u53ea\u652f\u63f4\u53f0\u6307\u671f \"token\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } resp = requests . get ( url , params = parameter ) data = resp . json () df = pd . DataFrame ( data [ \"data\" ]) print ( df . head ()) R library ( httr ) library ( data.table ) library ( dplyr ) url = \"https://api.finmindtrade.com/api/v4/taiwan_futures_snapshot\" response = httr :: GET ( url = url , query = list ( data_id = \"TXF\" , # \u76ee\u524d\u53ea\u652f\u63f4\u53f0\u6307\u671f token = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) data = content ( response ) df = data $ data %>% do.call ( 'rbind' , . ) %>% data.table head ( df ) Output DataFrame open high low close change_price change_rate average_price volume total_volume amount total_amount yesterday_volume buy_price buy_volume sell_price sell_volume volume_ratio date futures_id TickType 0 16720 16800 16714 16762 51 0.31 16757.2 1 52822 16762 885151394 46576 16760 61 16765 8 1.13 2023-06-03 04:59:59.243 TXFR1 2 1 16720 16800 16714 16762 51 0.31 16757.2 1 52822 16762 885151394 46576 16760 61 16765 8 1.13 2023-06-03 04:59:59.243 TXFF3 2 2 16290 16291 16290 16291 68 0.42 16290.5 1 2 16291 32581 3 16266 2 16500 2 0.67 2023-06-02 23:01:10.244 TXFC4 1 3 16409 16468 16400 16449 64 0.39 16431.9 1 110 16449 1807505 90 16424 1 16450 1 1.22 2023-06-03 03:00:34.248 TXFH3 1 4 16300 16373 16300 16335 56 0.34 16340.8 1 10 16335 163408 8 16315 1 16355 1 1.25 2023-06-03 03:36:00.561 TXFL3 1 Schema { open: float64, high: float64, low: float64, close: float64, change_price: float64, change_rate: float64, average_price: float64, volume: int64, total_volume\" int64, amount: str, total_amount: str, yesterday_volume: int64, buy_price: float64, buy_volume: int64, sell_price: float64, sell_volume: int64, volume_ratio: float64, date: str, options_id: str, TickType: str # 0: \u7121\u6cd5\u5224\u65b7, 1: \u8ce3\u76e4\u6210\u4ea4, 2: \u8cb7\u76e4\u6210\u4ea4 }","title":"\u53f0\u80a1\u671f\u8ca8\u5373\u6642\u8cc7\u8a0a taiwan_futures_snapshot (\u53ea\u9650 sponsor \u6703\u54e1\u4f7f\u7528)"},{"location":"tutor/TaiwanMarket/RealTime/#taiwan_options_snapshot-sponsor","text":"(\u76ee\u524d\u652f\u63f4\u53f0\u6307\u9078\u64c7\u6b0a\u3001\u7d04 30 \u79d2\u66f4\u65b0\u4e00\u6b21) 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 = data_loader . taiwan_options_snapshot ( options_id = \"TXO\" ) Python-request import requests import pandas as pd url = \"https://api.finmindtrade.com/api/v4/taiwan_options_snapshot\" parameter = { \"data_id\" : \"TXO\" , # \u76ee\u524d\u53ea\u652f\u63f4\u53f0\u6307\u9078\u64c7\u6b0a \"token\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } resp = requests . get ( url , params = parameter ) data = resp . json () df = pd . DataFrame ( data [ \"data\" ]) print ( df . head ()) R library ( httr ) library ( data.table ) library ( dplyr ) url = \"https://api.finmindtrade.com/api/v4/taiwan_options_snapshot\" response = httr :: GET ( url = url , query = list ( data_id = \"TXO\" , # \u76ee\u524d\u53ea\u652f\u63f4\u53f0\u6307\u671f token = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) data = content ( response ) df = data $ data %>% do.call ( 'rbind' , . ) %>% data.table head ( df ) Output DataFrame open high low close change_price change_rate average_price volume total_volume amount total_amount yesterday_volume buy_price buy_volume sell_price sell_volume volume_ratio date options_id TickType 0 46 46 45.5 46 -12 -20.69 45.75 17 40 782 1830 2 0.6 5 0 0 20 2023-06-02 13:07:35.299 TXO14300T3 1 1 60 65 46.5 46.5 -17.5 -27.34 58.76 1 939 46 55172 936 48.5 2 65 3 1 2023-06-03 02:07:52.807 TXO15400S3 2 2 905 910 875 875 -45 -4.89 891.25 1 4 875 3565 2 1.1 6 0 0 2 2023-06-02 20:06:14.720 TXO17000U3 2 3 695 695 695 695 25 3.73 695 1 1 695 695 13 6.3 1 1030 2 0.08 2023-06-02 15:29:16.150 TXO16400L3 1 4 0 0 0 0 0 0 0 0 0 0 0 0 1.1 3 0 0 0 2023-06-04 08:30:00.000 TXO15000H3 0 Schema { open: float64, high: float64, low: float64, close: float64, change_price: float64, change_rate: float64, average_price: float64, volume: int64, total_volume\" int64, amount: str, total_amount: str, yesterday_volume: int64, buy_price: float64, buy_volume: int64, sell_price: float64, sell_volume: int64, volume_ratio: float64, date: str, options_id: str, TickType: str # 0: \u7121\u6cd5\u5224\u65b7, 1: \u8ce3\u76e4\u6210\u4ea4, 2: \u8cb7\u76e4\u6210\u4ea4 }","title":"\u53f0\u80a1\u9078\u64c7\u6b0a\u5373\u6642\u8cc7\u8a0a taiwan_options_snapshot (\u53ea\u9650 sponsor \u6703\u54e1\u4f7f\u7528)"},{"location":"tutor/TaiwanMarket/Technical/","text":"\u5728\u53f0\u80a1\u6280\u8853\u9762\uff0c\u6211\u5011\u64c1\u6709 14 \u7a2e\u8cc7\u6599\u96c6\uff0c\u5982\u4e0b: \u53f0\u80a1\u7e3d\u89bd TaiwanStockInfo \u53f0\u80a1\u7e3d\u89bd(\u542b\u6b0a\u8b49) TaiwanStockInfoWithWarrant \u53f0\u7063\u80a1\u50f9\u8cc7\u6599\u8868 TaiwanStockPrice \u53f0\u80a1\u9031 K \u8cc7\u6599\u8868 TaiwanStockWeekPrice \u53f0\u80a1\u6708 K \u8cc7\u6599\u8868 TaiwanStockMonthPrice \u53f0\u7063\u9084\u539f\u80a1\u50f9\u8cc7\u6599\u8868 TaiwanStockPriceAdj \u53f0\u7063\u80a1\u50f9\u6b77\u53f2\u9010\u7b46\u8cc7\u6599\u8868 TaiwanStockPriceTick \u53f0\u7063\u500b\u80a1PER\u3001PBR\u8cc7\u6599\u8868 TaiwanStockPER \u6bcf5\u79d2\u59d4\u8a17\u6210\u4ea4\u7d71\u8a08 TaiwanStockStatisticsOfOrderBookAndTrade \u53f0\u80a1\u52a0\u6b0a\u6307\u6578 TaiwanVariousIndicators5Seconds \u7576\u65e5\u6c96\u92b7\u4ea4\u6613\u6a19\u7684\u53ca\u6210\u4ea4\u91cf\u503c TaiwanStockDayTrading \u52a0\u6b0a\u3001\u6ac3\u8cb7\u5831\u916c\u6307\u6578 TaiwanStockTotalReturnIndex \u53f0\u7063\u500b\u80a1\u5341\u5e74\u7dda\u8cc7\u6599\u8868 TaiwanStock10Year \u53f0\u80a1\u5206 K \u8cc7\u6599\u8868 TaiwanStockKBar \u53f0\u80a1\u7e3d\u89bd TaiwanStockInfo \u00b6 \u9019\u5f35\u8cc7\u6599\u8868\u4e3b\u8981\u662f\u5217\u51fa\u53f0\u7063\u6240\u6709\uff0c\u4e0a\u5e02\u3001\u4e0a\u6ac3\u3001\u8208\u6ac3\uff0c\u7684\u80a1\u7968\u540d\u7a31\uff0c\u4ee3\u78bc\u548c\u7522\u696d\u985e\u5225\uff01 \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u6bcf\u5929 1:30 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b 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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 \u5143\u5927\u53f0\u706350 twse 2021-10-05 1 ETF 0051 \u5143\u5927\u4e2d\u578b100 twse 2021-10-05 2 ETF 0052 \u5bcc\u90a6\u79d1\u6280 twse 2021-10-05 3 ETF 0053 \u5143\u5927\u96fb\u5b50 twse 2021-10-05 4 ETF 0054 \u5143\u5927\u53f0\u554650 twse 2021-10-05 Schema { industry_category: str, stock_id: str, stock_name: str, type: str, date: str } \u53f0\u80a1\u7e3d\u89bd(\u542b\u6b0a\u8b49) TaiwanStockInfoWithWarrant \u00b6 \u9019\u5f35\u8cc7\u6599\u8868\u4e3b\u8981\u662f\u5217\u51fa\u53f0\u7063\u6240\u6709\uff0c\u4e0a\u5e02\u3001\u4e0a\u6ac3\u3001\u8208\u6ac3\uff0c\u7684\u80a1\u7968\u3001\u6b0a\u8b49\u540d\u7a31\uff0c\u4ee3\u78bc\u548c\u7522\u696d\u985e\u5225\uff01 \u8cc7\u6599\u91cf\u8d85\u904e 5 \u842c\u7b46 \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u6bcf\u5929 1:30 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b 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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 \u5143\u5927\u53f0\u706350 twse 2021-10-05 1 ETF 0051 \u5143\u5927\u4e2d\u578b100 twse 2021-10-05 2 ETF 0052 \u5bcc\u90a6\u79d1\u6280 twse 2021-10-05 3 ETF 0053 \u5143\u5927\u96fb\u5b50 twse 2021-10-05 4 ETF 0054 \u5143\u5927\u53f0\u554650 twse 2021-10-05 Schema { industry_category: str, stock_id: str, stock_name: str, type: str, date: str } \u80a1\u50f9\u65e5\u6210\u4ea4\u8cc7\u8a0a TaiwanStockPrice \u00b6 \u63d0\u4f9b\u53f0\u80a1\uff0c\u4e0a\u5e02\u3001\u4e0a\u6ac3\u3001\u8208\u6ac3\uff0c\u7684\u80a1\u7968\u65e5\u6210\u4ea4\u8cc7\u8a0a\uff01 \u8cc7\u6599\u5340\u9593\uff1a1994-10-01 ~ now \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u661f\u671f\u4e00\u81f3\u4e94 17:30 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b 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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 } \u4e00\u6b21\u62ff\u7279\u5b9a\u65e5\u671f\uff0c\u6240\u6709\u8cc7\u6599(\u53ea\u9650 backer\u3001sponsor \u4f7f\u7528) \u00b6 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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 } \u53f0\u80a1\u9031 K \u8cc7\u6599\u8868 TaiwanStockWeekPrice (\u53ea\u9650 backer\u3001sponsor \u6703\u54e1\u4f7f\u7528) \u00b6 \u63d0\u4f9b\u53f0\u80a1\uff0c\u4e0a\u5e02\u3001\u4e0a\u6ac3\u3001\u8208\u6ac3\uff0c\u7684\u80a1\u7968\u65e5\u6210\u4ea4\u8cc7\u8a0a\uff01 \u8cc7\u6599\u5340\u9593\uff1a2000-01-01 ~ now \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u661f\u671f\u4e00\u81f3\u4e94 17:30 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b 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_weekly ( 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\" : \"TaiwanStockWeekPrice\" , \"data_id\" : \"2330\" , \"start_date\" : \"2020-04-02\" , \"end_date\" : \"2020-04-12\" , \"token\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"TaiwanStockWeekPrice\" , data_id = \"2330\" , start_date = \"2020-04-02\" , end_date = \"2020-04-08\" , token = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) data = content ( response ) df = data $ data %>% do.call ( 'rbind' , . ) %>% data.table head ( df ) Output DataFrame stock_id yweek max min trading_volume trading_money trading_turnover date close open spread 0 2330 2020W15 288 270 409564428 114799189198 188964 2020-04-06 279.5 273 8 Schema { stock_id: str, yweek: str, max: float64, min: float64, trading_volume: int64, trading_money: int64, trading_turnover: float32, date: str, close: float64, open: float64, spread: float64, } \u4e00\u6b21\u62ff\u7279\u5b9a\u65e5\u671f\uff0c\u6240\u6709\u8cc7\u6599(\u53ea\u9650 backer\u3001sponsor \u4f7f\u7528) \u00b6 Example Python-request import requests import pandas as pd url = \"https://api.finmindtrade.com/api/v4/data\" parameter = { \"dataset\" : \"TaiwanStockWeekPrice\" , \"start_date\" : \"2020-04-06\" , \"token\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"TaiwanStockWeekPrice\" , start_date = \"2020-04-06\" , token = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) data = content ( response ) df = data $ data %>% do.call ( 'rbind' , . ) %>% data.table head ( df ) Output DataFrame stock_id yweek max min trading_volume trading_money trading_turnover date close open spread 0 2330 2020W15 288 270 409564428 114799189198 188964 2020-04-06 279.5 273 8 Schema { stock_id: str, yweek: str, max: float64, min: float64, trading_volume: int64, trading_money: int64, trading_turnover: float32, date: str, close: float64, open: float64, spread: float64, } \u53f0\u80a1\u6708 K \u8cc7\u6599\u8868 TaiwanStockMonthPrice (\u53ea\u9650 backer\u3001sponsor \u6703\u54e1\u4f7f\u7528) \u00b6 \u63d0\u4f9b\u53f0\u80a1\uff0c\u4e0a\u5e02\u3001\u4e0a\u6ac3\u3001\u8208\u6ac3\uff0c\u7684\u80a1\u7968\u65e5\u6210\u4ea4\u8cc7\u8a0a\uff01 \u8cc7\u6599\u5340\u9593\uff1a2000-01-01 ~ now \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u661f\u671f\u4e00\u81f3\u4e94 17:30 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b 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_monthly ( 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\" : \"TaiwanStockMonthPrice\" , \"data_id\" : \"2330\" , \"start_date\" : \"2020-04-02\" , \"end_date\" : \"2020-04-12\" , \"token\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"TaiwanStockMonthPrice\" , data_id = \"2330\" , start_date = \"2020-04-02\" , end_date = \"2020-05-08\" , token = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) data = content ( response ) df = data $ data %>% do.call ( 'rbind' , . ) %>% data.table head ( df ) Output DataFrame stock_id ymonth max min trading_volume trading_money trading_turnover date close open spread 0 2330 2020M05 301.5 288.5 1744651784 513799591970 788158 2020-05-01 292 294.5 -12.5 Schema { stock_id: str, ymonth: str, max: float64, min: float64, trading_volume: int64, trading_money: int64, trading_turnover: float32, date: str, close: float64, open: float64, spread: float64, } \u4e00\u6b21\u62ff\u7279\u5b9a\u65e5\u671f\uff0c\u6240\u6709\u8cc7\u6599(\u53ea\u9650 backer\u3001sponsor \u4f7f\u7528) \u00b6 Example Python-request import requests import pandas as pd url = \"https://api.finmindtrade.com/api/v4/data\" parameter = { \"dataset\" : \"TaiwanStockMonthPrice\" , \"start_date\" : \"2020-04-06\" , \"token\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"TaiwanStockMonthPrice\" , start_date = \"2020-04-06\" , token = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) data = content ( response ) df = data $ data %>% do.call ( 'rbind' , . ) %>% data.table head ( df ) Output DataFrame stock_id ymonth max min trading_volume trading_money trading_turnover date close open spread 0 2330 2020M05 301.5 288.5 1744651784 513799591970 788158 2020-05-01 292 294.5 -12.5 Schema { stock_id: str, ymonth: str, max: float64, min: float64, trading_volume: int64, trading_money: int64, trading_turnover: float32, date: str, close: float64, open: float64, spread: float64, } \u53f0\u7063\u9084\u539f\u80a1\u50f9\u8cc7\u6599\u8868 TaiwanStockPriceAdj (\u53ea\u9650 backer\u3001sponsor \u6703\u54e1\u4f7f\u7528) \u00b6 \u8cc7\u6599\u5340\u9593\uff1a1994-10-01 ~ now \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u661f\u671f\u4e00\u81f3\u4e94 17:30 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b 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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 } \u4e00\u6b21\u62ff\u7279\u5b9a\u65e5\u671f\uff0c\u6240\u6709\u8cc7\u6599(\u53ea\u9650 backer\u3001sponsor \u4f7f\u7528) \u00b6 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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 } \u53f0\u7063\u80a1\u50f9\u6b77\u53f2\u9010\u7b46\u8cc7\u6599\u8868 TaiwanStockPriceTick (\u53ea\u9650 backer\u3001sponsor \u6703\u54e1\u4f7f\u7528) \u00b6 (\u7531\u65bc\u8cc7\u6599\u91cf\u904e\u5927\uff0c\u55ae\u6b21\u8acb\u6c42\u53ea\u63d0\u4f9b\u4e00\u5929\u8cc7\u6599) \u8cc7\u6599\u5340\u9593\uff1a2019-01-01 ~ now \u8f38\u5165 dataset\u3001stock_id\u3001start_date \u53c3\u6578\uff0c\u6703\u56de\u50b3 start_date \u7576\u5929\u8cc7\u6599\u3002 \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u661f\u671f\u4e00\u81f3\u4e94 15:30 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b 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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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: str # 0: \u7121\u6cd5\u5224\u65b7, 1: \u8ce3\u76e4\u6210\u4ea4, 2: \u8cb7\u76e4\u6210\u4ea4 } \u500b\u80a1PER\u3001PBR\u8cc7\u6599\u8868 TaiwanStockPER \u00b6 \u8cc7\u6599\u5340\u9593\uff1a2005-10-01 ~ now \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u661f\u671f\u4e00\u81f3\u4e94 18:00 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b 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' , end_date = '2020-04-12' , ) 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\" , \"end_date\" : \"2020-04-12\" , \"token\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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\" , end_date = \"2020-04-12\" , token = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 } \u6bcf5\u79d2\u59d4\u8a17\u6210\u4ea4\u7d71\u8a08 TaiwanStockStatisticsOfOrderBookAndTrade \u00b6 (\u7531\u65bc\u8cc7\u6599\u91cf\u904e\u5927\uff0c\u55ae\u6b21\u8acb\u6c42\u53ea\u63d0\u4f9b\u4e00\u5929\u8cc7\u6599) \u8cc7\u6599\u5340\u9593\uff1a2005-01-01 ~ 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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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, } \u52a0\u6b0a\u6307\u6578 TaiwanVariousIndicators5Seconds \u00b6 (\u7531\u65bc\u8cc7\u6599\u91cf\u904e\u5927\uff0c\u55ae\u6b21\u8acb\u6c42\u53ea\u63d0\u4f9b\u4e00\u5929\u8cc7\u6599) \u8cc7\u6599\u5340\u9593\uff1a2005-01-01 ~ 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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 } \u7576\u65e5\u6c96\u92b7\u4ea4\u6613\u6a19\u7684\u53ca\u6210\u4ea4\u91cf\u503c TaiwanStockDayTrading \u00b6 \u8cc7\u6599\u5340\u9593\uff1a2014-01-01 ~ now \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u661f\u671f\u4e00\u81f3\u4e94 21:30 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b 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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 } \u4e00\u6b21\u62ff\u7279\u5b9a\u65e5\u671f\uff0c\u6240\u6709\u8cc7\u6599(\u53ea\u9650 backer\u3001sponsor \u6703\u54e1\u4f7f\u7528) \u00b6 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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 } \u52a0\u6b0a\u3001\u6ac3\u8cb7\u5831\u916c\u6307\u6578 TaiwanStockTotalReturnIndex \u00b6 \u8cc7\u6599\u5340\u9593\uff1a2003-01-01 ~ now \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u661f\u671f\u4e00\u81f3\u4e94 16:50 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b 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\" , # \u767c\u884c\u91cf\u52a0\u6b0a\u80a1\u50f9\u5831\u916c\u6307\u6578 # \"data_id\": \"TPEx\", # \u6ac3\u8cb7\u6307\u6578\u8207\u5831\u916c\u6307\u6578 \"start_date\" : \"2020-04-02\" , \"end_date\" : \"2020-04-12\" , \"token\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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\" , # \u767c\u884c\u91cf\u52a0\u6b0a\u80a1\u50f9\u5831\u916c\u6307\u6578 # data_id= \"TPEx\", # \u6ac3\u8cb7\u6307\u6578\u8207\u5831\u916c\u6307\u6578 start_date = \"2020-04-02\" , end_date = \"2020-04-08\" , token = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 } \u53f0\u7063\u500b\u80a1\u5341\u5e74\u7dda\u8cc7\u6599\u8868 TaiwanStock10Year (\u53ea\u9650 backer\u3001sponsor \u6703\u54e1\u4f7f\u7528) \u00b6 \u8cc7\u6599\u5340\u9593\uff1a2011-01-24 ~ now \u900f\u904e2500\u500b\u4ea4\u6613\u65e5\u6240\u8a08\u7b97\u51fa\u7684\u5e73\u5747\u50f9\u683c \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u661f\u671f\u4e00\u81f3\u4e94 20:00 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b 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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 } \u4e00\u6b21\u62ff\u7279\u5b9a\u65e5\u671f\uff0c\u6240\u6709\u8cc7\u6599(\u53ea\u9650 backer\u3001sponsor \u4f7f\u7528) \u00b6 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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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, } \u53f0\u80a1\u5206 K \u8cc7\u6599\u8868 TaiwanStockKBar (\u53ea\u9650 sponsor \u6703\u54e1\u4f7f\u7528) \u00b6 (\u7531\u65bc\u8cc7\u6599\u91cf\u904e\u5927\uff0c\u55ae\u6b21\u8acb\u6c42\u53ea\u63d0\u4f9b\u4e00\u5929\u8cc7\u6599) \u8cc7\u6599\u5340\u9593\uff1a2019-01-01 ~ now \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u661f\u671f\u4e00\u81f3\u4e94 15:50 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b 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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 }","title":"\u6280\u8853\u9762"},{"location":"tutor/TaiwanMarket/Technical/#taiwanstockinfo","text":"\u9019\u5f35\u8cc7\u6599\u8868\u4e3b\u8981\u662f\u5217\u51fa\u53f0\u7063\u6240\u6709\uff0c\u4e0a\u5e02\u3001\u4e0a\u6ac3\u3001\u8208\u6ac3\uff0c\u7684\u80a1\u7968\u540d\u7a31\uff0c\u4ee3\u78bc\u548c\u7522\u696d\u985e\u5225\uff01 \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u6bcf\u5929 1:30 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b 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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 \u5143\u5927\u53f0\u706350 twse 2021-10-05 1 ETF 0051 \u5143\u5927\u4e2d\u578b100 twse 2021-10-05 2 ETF 0052 \u5bcc\u90a6\u79d1\u6280 twse 2021-10-05 3 ETF 0053 \u5143\u5927\u96fb\u5b50 twse 2021-10-05 4 ETF 0054 \u5143\u5927\u53f0\u554650 twse 2021-10-05 Schema { industry_category: str, stock_id: str, stock_name: str, type: str, date: str }","title":"\u53f0\u80a1\u7e3d\u89bd TaiwanStockInfo"},{"location":"tutor/TaiwanMarket/Technical/#taiwanstockinfowithwarrant","text":"\u9019\u5f35\u8cc7\u6599\u8868\u4e3b\u8981\u662f\u5217\u51fa\u53f0\u7063\u6240\u6709\uff0c\u4e0a\u5e02\u3001\u4e0a\u6ac3\u3001\u8208\u6ac3\uff0c\u7684\u80a1\u7968\u3001\u6b0a\u8b49\u540d\u7a31\uff0c\u4ee3\u78bc\u548c\u7522\u696d\u985e\u5225\uff01 \u8cc7\u6599\u91cf\u8d85\u904e 5 \u842c\u7b46 \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u6bcf\u5929 1:30 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b 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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 \u5143\u5927\u53f0\u706350 twse 2021-10-05 1 ETF 0051 \u5143\u5927\u4e2d\u578b100 twse 2021-10-05 2 ETF 0052 \u5bcc\u90a6\u79d1\u6280 twse 2021-10-05 3 ETF 0053 \u5143\u5927\u96fb\u5b50 twse 2021-10-05 4 ETF 0054 \u5143\u5927\u53f0\u554650 twse 2021-10-05 Schema { industry_category: str, stock_id: str, stock_name: str, type: str, date: str }","title":"\u53f0\u80a1\u7e3d\u89bd(\u542b\u6b0a\u8b49) TaiwanStockInfoWithWarrant"},{"location":"tutor/TaiwanMarket/Technical/#taiwanstockprice","text":"\u63d0\u4f9b\u53f0\u80a1\uff0c\u4e0a\u5e02\u3001\u4e0a\u6ac3\u3001\u8208\u6ac3\uff0c\u7684\u80a1\u7968\u65e5\u6210\u4ea4\u8cc7\u8a0a\uff01 \u8cc7\u6599\u5340\u9593\uff1a1994-10-01 ~ now \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u661f\u671f\u4e00\u81f3\u4e94 17:30 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b 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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 }","title":"\u80a1\u50f9\u65e5\u6210\u4ea4\u8cc7\u8a0a TaiwanStockPrice"},{"location":"tutor/TaiwanMarket/Technical/#backersponsor","text":"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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 }","title":"\u4e00\u6b21\u62ff\u7279\u5b9a\u65e5\u671f\uff0c\u6240\u6709\u8cc7\u6599(\u53ea\u9650 backer\u3001sponsor \u4f7f\u7528)"},{"location":"tutor/TaiwanMarket/Technical/#k-taiwanstockweekprice-backersponsor","text":"\u63d0\u4f9b\u53f0\u80a1\uff0c\u4e0a\u5e02\u3001\u4e0a\u6ac3\u3001\u8208\u6ac3\uff0c\u7684\u80a1\u7968\u65e5\u6210\u4ea4\u8cc7\u8a0a\uff01 \u8cc7\u6599\u5340\u9593\uff1a2000-01-01 ~ now \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u661f\u671f\u4e00\u81f3\u4e94 17:30 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b 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_weekly ( 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\" : \"TaiwanStockWeekPrice\" , \"data_id\" : \"2330\" , \"start_date\" : \"2020-04-02\" , \"end_date\" : \"2020-04-12\" , \"token\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"TaiwanStockWeekPrice\" , data_id = \"2330\" , start_date = \"2020-04-02\" , end_date = \"2020-04-08\" , token = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) data = content ( response ) df = data $ data %>% do.call ( 'rbind' , . ) %>% data.table head ( df ) Output DataFrame stock_id yweek max min trading_volume trading_money trading_turnover date close open spread 0 2330 2020W15 288 270 409564428 114799189198 188964 2020-04-06 279.5 273 8 Schema { stock_id: str, yweek: str, max: float64, min: float64, trading_volume: int64, trading_money: int64, trading_turnover: float32, date: str, close: float64, open: float64, spread: float64, }","title":"\u53f0\u80a1\u9031 K \u8cc7\u6599\u8868 TaiwanStockWeekPrice (\u53ea\u9650 backer\u3001sponsor \u6703\u54e1\u4f7f\u7528)"},{"location":"tutor/TaiwanMarket/Technical/#backersponsor_1","text":"Example Python-request import requests import pandas as pd url = \"https://api.finmindtrade.com/api/v4/data\" parameter = { \"dataset\" : \"TaiwanStockWeekPrice\" , \"start_date\" : \"2020-04-06\" , \"token\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"TaiwanStockWeekPrice\" , start_date = \"2020-04-06\" , token = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) data = content ( response ) df = data $ data %>% do.call ( 'rbind' , . ) %>% data.table head ( df ) Output DataFrame stock_id yweek max min trading_volume trading_money trading_turnover date close open spread 0 2330 2020W15 288 270 409564428 114799189198 188964 2020-04-06 279.5 273 8 Schema { stock_id: str, yweek: str, max: float64, min: float64, trading_volume: int64, trading_money: int64, trading_turnover: float32, date: str, close: float64, open: float64, spread: float64, }","title":"\u4e00\u6b21\u62ff\u7279\u5b9a\u65e5\u671f\uff0c\u6240\u6709\u8cc7\u6599(\u53ea\u9650 backer\u3001sponsor \u4f7f\u7528)"},{"location":"tutor/TaiwanMarket/Technical/#k-taiwanstockmonthprice-backersponsor","text":"\u63d0\u4f9b\u53f0\u80a1\uff0c\u4e0a\u5e02\u3001\u4e0a\u6ac3\u3001\u8208\u6ac3\uff0c\u7684\u80a1\u7968\u65e5\u6210\u4ea4\u8cc7\u8a0a\uff01 \u8cc7\u6599\u5340\u9593\uff1a2000-01-01 ~ now \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u661f\u671f\u4e00\u81f3\u4e94 17:30 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b 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_monthly ( 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\" : \"TaiwanStockMonthPrice\" , \"data_id\" : \"2330\" , \"start_date\" : \"2020-04-02\" , \"end_date\" : \"2020-04-12\" , \"token\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"TaiwanStockMonthPrice\" , data_id = \"2330\" , start_date = \"2020-04-02\" , end_date = \"2020-05-08\" , token = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) data = content ( response ) df = data $ data %>% do.call ( 'rbind' , . ) %>% data.table head ( df ) Output DataFrame stock_id ymonth max min trading_volume trading_money trading_turnover date close open spread 0 2330 2020M05 301.5 288.5 1744651784 513799591970 788158 2020-05-01 292 294.5 -12.5 Schema { stock_id: str, ymonth: str, max: float64, min: float64, trading_volume: int64, trading_money: int64, trading_turnover: float32, date: str, close: float64, open: float64, spread: float64, }","title":"\u53f0\u80a1\u6708 K \u8cc7\u6599\u8868 TaiwanStockMonthPrice (\u53ea\u9650 backer\u3001sponsor \u6703\u54e1\u4f7f\u7528)"},{"location":"tutor/TaiwanMarket/Technical/#backersponsor_2","text":"Example Python-request import requests import pandas as pd url = \"https://api.finmindtrade.com/api/v4/data\" parameter = { \"dataset\" : \"TaiwanStockMonthPrice\" , \"start_date\" : \"2020-04-06\" , \"token\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"TaiwanStockMonthPrice\" , start_date = \"2020-04-06\" , token = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) data = content ( response ) df = data $ data %>% do.call ( 'rbind' , . ) %>% data.table head ( df ) Output DataFrame stock_id ymonth max min trading_volume trading_money trading_turnover date close open spread 0 2330 2020M05 301.5 288.5 1744651784 513799591970 788158 2020-05-01 292 294.5 -12.5 Schema { stock_id: str, ymonth: str, max: float64, min: float64, trading_volume: int64, trading_money: int64, trading_turnover: float32, date: str, close: float64, open: float64, spread: float64, }","title":"\u4e00\u6b21\u62ff\u7279\u5b9a\u65e5\u671f\uff0c\u6240\u6709\u8cc7\u6599(\u53ea\u9650 backer\u3001sponsor \u4f7f\u7528)"},{"location":"tutor/TaiwanMarket/Technical/#taiwanstockpriceadj-backersponsor","text":"\u8cc7\u6599\u5340\u9593\uff1a1994-10-01 ~ now \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u661f\u671f\u4e00\u81f3\u4e94 17:30 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b 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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 }","title":"\u53f0\u7063\u9084\u539f\u80a1\u50f9\u8cc7\u6599\u8868 TaiwanStockPriceAdj (\u53ea\u9650 backer\u3001sponsor \u6703\u54e1\u4f7f\u7528)"},{"location":"tutor/TaiwanMarket/Technical/#backersponsor_3","text":"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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 }","title":"\u4e00\u6b21\u62ff\u7279\u5b9a\u65e5\u671f\uff0c\u6240\u6709\u8cc7\u6599(\u53ea\u9650 backer\u3001sponsor \u4f7f\u7528)"},{"location":"tutor/TaiwanMarket/Technical/#taiwanstockpricetick-backersponsor","text":"(\u7531\u65bc\u8cc7\u6599\u91cf\u904e\u5927\uff0c\u55ae\u6b21\u8acb\u6c42\u53ea\u63d0\u4f9b\u4e00\u5929\u8cc7\u6599) \u8cc7\u6599\u5340\u9593\uff1a2019-01-01 ~ now \u8f38\u5165 dataset\u3001stock_id\u3001start_date \u53c3\u6578\uff0c\u6703\u56de\u50b3 start_date \u7576\u5929\u8cc7\u6599\u3002 \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u661f\u671f\u4e00\u81f3\u4e94 15:30 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b 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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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: str # 0: \u7121\u6cd5\u5224\u65b7, 1: \u8ce3\u76e4\u6210\u4ea4, 2: \u8cb7\u76e4\u6210\u4ea4 }","title":"\u53f0\u7063\u80a1\u50f9\u6b77\u53f2\u9010\u7b46\u8cc7\u6599\u8868 TaiwanStockPriceTick (\u53ea\u9650 backer\u3001sponsor \u6703\u54e1\u4f7f\u7528)"},{"location":"tutor/TaiwanMarket/Technical/#perpbr-taiwanstockper","text":"\u8cc7\u6599\u5340\u9593\uff1a2005-10-01 ~ now \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u661f\u671f\u4e00\u81f3\u4e94 18:00 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b 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' , end_date = '2020-04-12' , ) 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\" , \"end_date\" : \"2020-04-12\" , \"token\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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\" , end_date = \"2020-04-12\" , token = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 }","title":"\u500b\u80a1PER\u3001PBR\u8cc7\u6599\u8868 TaiwanStockPER"},{"location":"tutor/TaiwanMarket/Technical/#5-taiwanstockstatisticsoforderbookandtrade","text":"(\u7531\u65bc\u8cc7\u6599\u91cf\u904e\u5927\uff0c\u55ae\u6b21\u8acb\u6c42\u53ea\u63d0\u4f9b\u4e00\u5929\u8cc7\u6599) \u8cc7\u6599\u5340\u9593\uff1a2005-01-01 ~ 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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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, }","title":"\u6bcf5\u79d2\u59d4\u8a17\u6210\u4ea4\u7d71\u8a08 TaiwanStockStatisticsOfOrderBookAndTrade"},{"location":"tutor/TaiwanMarket/Technical/#taiwanvariousindicators5seconds","text":"(\u7531\u65bc\u8cc7\u6599\u91cf\u904e\u5927\uff0c\u55ae\u6b21\u8acb\u6c42\u53ea\u63d0\u4f9b\u4e00\u5929\u8cc7\u6599) \u8cc7\u6599\u5340\u9593\uff1a2005-01-01 ~ 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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 }","title":"\u52a0\u6b0a\u6307\u6578 TaiwanVariousIndicators5Seconds"},{"location":"tutor/TaiwanMarket/Technical/#taiwanstockdaytrading","text":"\u8cc7\u6599\u5340\u9593\uff1a2014-01-01 ~ now \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u661f\u671f\u4e00\u81f3\u4e94 21:30 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b 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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 }","title":"\u7576\u65e5\u6c96\u92b7\u4ea4\u6613\u6a19\u7684\u53ca\u6210\u4ea4\u91cf\u503c TaiwanStockDayTrading"},{"location":"tutor/TaiwanMarket/Technical/#backersponsor_4","text":"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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 }","title":"\u4e00\u6b21\u62ff\u7279\u5b9a\u65e5\u671f\uff0c\u6240\u6709\u8cc7\u6599(\u53ea\u9650 backer\u3001sponsor \u6703\u54e1\u4f7f\u7528)"},{"location":"tutor/TaiwanMarket/Technical/#taiwanstocktotalreturnindex","text":"\u8cc7\u6599\u5340\u9593\uff1a2003-01-01 ~ now \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u661f\u671f\u4e00\u81f3\u4e94 16:50 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b 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\" , # \u767c\u884c\u91cf\u52a0\u6b0a\u80a1\u50f9\u5831\u916c\u6307\u6578 # \"data_id\": \"TPEx\", # \u6ac3\u8cb7\u6307\u6578\u8207\u5831\u916c\u6307\u6578 \"start_date\" : \"2020-04-02\" , \"end_date\" : \"2020-04-12\" , \"token\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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\" , # \u767c\u884c\u91cf\u52a0\u6b0a\u80a1\u50f9\u5831\u916c\u6307\u6578 # data_id= \"TPEx\", # \u6ac3\u8cb7\u6307\u6578\u8207\u5831\u916c\u6307\u6578 start_date = \"2020-04-02\" , end_date = \"2020-04-08\" , token = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 }","title":"\u52a0\u6b0a\u3001\u6ac3\u8cb7\u5831\u916c\u6307\u6578 TaiwanStockTotalReturnIndex"},{"location":"tutor/TaiwanMarket/Technical/#taiwanstock10year-backersponsor","text":"\u8cc7\u6599\u5340\u9593\uff1a2011-01-24 ~ now \u900f\u904e2500\u500b\u4ea4\u6613\u65e5\u6240\u8a08\u7b97\u51fa\u7684\u5e73\u5747\u50f9\u683c \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u661f\u671f\u4e00\u81f3\u4e94 20:00 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b 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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 }","title":"\u53f0\u7063\u500b\u80a1\u5341\u5e74\u7dda\u8cc7\u6599\u8868 TaiwanStock10Year (\u53ea\u9650 backer\u3001sponsor \u6703\u54e1\u4f7f\u7528)"},{"location":"tutor/TaiwanMarket/Technical/#backersponsor_5","text":"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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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, }","title":"\u4e00\u6b21\u62ff\u7279\u5b9a\u65e5\u671f\uff0c\u6240\u6709\u8cc7\u6599(\u53ea\u9650 backer\u3001sponsor \u4f7f\u7528)"},{"location":"tutor/TaiwanMarket/Technical/#k-taiwanstockkbar-sponsor","text":"(\u7531\u65bc\u8cc7\u6599\u91cf\u904e\u5927\uff0c\u55ae\u6b21\u8acb\u6c42\u53ea\u63d0\u4f9b\u4e00\u5929\u8cc7\u6599) \u8cc7\u6599\u5340\u9593\uff1a2019-01-01 ~ now \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u661f\u671f\u4e00\u81f3\u4e94 15:50 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b 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\" : \"\" , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } 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 = \"\" # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 ) ) 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 }","title":"\u53f0\u80a1\u5206 K \u8cc7\u6599\u8868 TaiwanStockKBar (\u53ea\u9650 sponsor \u6703\u54e1\u4f7f\u7528)"},{"location":"tutor/UnitedKingdomMarket/DataList/","text":"\u5728\u82f1\u570b\u91d1\u878d\u5e02\u5834\uff0c\u6211\u5011\u64c1\u6709 1 \u7a2e\u8cc7\u6599\u96c6\uff0c\u5982\u4e0b: \u82f1\u570b\u80a1\u7968\u6e05\u55ae\u5217\u8868 UKStockInfo \u80a1\u50f9 TaiwanStock \u00b6 \u82f1\u570b\u80a1\u50f9\u8cc7\u6599\u8868 UKStockPrice \u5177\u9ad4 schemas \u8acb\u53c3\u8003 finmindapi","title":"\u7e3d\u89bd"},{"location":"tutor/UnitedKingdomMarket/DataList/#taiwanstock","text":"\u82f1\u570b\u80a1\u50f9\u8cc7\u6599\u8868 UKStockPrice \u5177\u9ad4 schemas \u8acb\u53c3\u8003 finmindapi","title":"\u80a1\u50f9 TaiwanStock"},{"location":"tutor/UnitedKingdomMarket/Technical/","text":"\u5728\u82f1\u570b\u80a1\u7968\u8cc7\u6599\uff0c\u6211\u5011\u64c1\u6709 1 \u7a2e\u8cc7\u6599\u96c6\uff0c\u5982\u4e0b: \u82f1\u570b\u80a1\u50f9\u8cc7\u6599\u8868 UKStockPrice \u53e6\u5916\u91dd\u5c0d\u8cc7\u6599\u96c6\u6574\u7406\u51fa\u7684\u5217\u8868\u5982\u4e0b: \u82f1\u570b\u80a1\u7968\u6e05\u55ae\u5217\u8868 UKStockInfo \u6839\u64da\u4e0a\u8ff0\u8cc7\u6599\u8868\u9010\u4e00\u8aaa\u660e\u600e\u9ebc\u4f7f\u7528\uff0c\u53e6\u5916\u5177\u9ad4\u8cc7\u6599\u8868 schemas \u8acb\u53c3\u8003 finmindapi \u82f1\u570b\u80a1\u7968\u6e05\u55ae\u5217\u8868 UKStockInfo \u00b6 \u9019\u5f35\u8cc7\u6599\u8868\u4e3b\u8981\u662f\u5217\u51fa\u82f1\u570b\u6240\u6709\u4e0a\u5e02\u4e0a\u6ac3\u7684\u80a1\u7968\u540d\u7a31\uff0c\u4ee3\u78bc\u548c\u7522\u696d\u985e\u5225 Example import requests import pandas as pd url = 'https://api.finmindtrade.com/api/v4/data' parameter = { \"dataset\" : \"UKStockInfo\" } data = requests . get ( url , params = parameter ) data = data . json () data = pd . DataFrame ( data [ 'data' ]) print ( data . head ()) Output DataFrame date stock_id Country stock_name 0 2019-01-31 01IC.L USD QUEBEC FRN20 1 2019-01-31 01IE.L USD ROY.BK.SCOTSERB 2 2019-01-31 01KG.L GBP SCOT.AMER.8%DB. 3 2019-01-31 01LQ.L GBP SCOT.PWR.UK6T23 4 2019-01-31 01LT.L GBP RES.MORT.4CTBRA Schema { date: str, stock_id: str, Country: str, stock_name: str } \u82f1\u570b\u80a1\u50f9\u8cc7\u6599\u8868 UKStockPrice \u00b6 Example import requests import pandas as pd url = 'https://api.finmindtrade.com/api/v4/data' parameter = { \"dataset\" : \"UKStockPrice\" , \"data_id\" : \"BP.L\" , \"start_date\" : \"2020-06-16\" , \"end_date\" : \"2021-06-16\" , \"token\" : token , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } data = requests . get ( url , params = parameter ) data = data . json () data = pd . DataFrame ( data [ 'data' ]) print ( data . head ()) Output DataFrame date stock_id Adj_Close Close High Low Open Volume 0 2020-06-16 BP.L 290.26 321.65 331.8 318.65 322.15 55470516 1 2020-06-17 BP.L 285.84 316.75 328.35 315.1 322.85 38005133 2 2020-06-18 BP.L 284.13 314.85 316.7 308.9 313.9 33988764 3 2020-06-19 BP.L 290.08 321.45 325.95 315.7 316.8 95409968 4 2020-06-22 BP.L 284.67 315.45 318.9 311.1 318.15 58369094 Schema { date: str, stock_id: str, Adj_Close: float32, Close: float32, High: float32, Low: float32, Open: float32, Volume: int64 }","title":"\u6280\u8853\u9762"},{"location":"tutor/UnitedKingdomMarket/Technical/#ukstockinfo","text":"\u9019\u5f35\u8cc7\u6599\u8868\u4e3b\u8981\u662f\u5217\u51fa\u82f1\u570b\u6240\u6709\u4e0a\u5e02\u4e0a\u6ac3\u7684\u80a1\u7968\u540d\u7a31\uff0c\u4ee3\u78bc\u548c\u7522\u696d\u985e\u5225 Example import requests import pandas as pd url = 'https://api.finmindtrade.com/api/v4/data' parameter = { \"dataset\" : \"UKStockInfo\" } data = requests . get ( url , params = parameter ) data = data . json () data = pd . DataFrame ( data [ 'data' ]) print ( data . head ()) Output DataFrame date stock_id Country stock_name 0 2019-01-31 01IC.L USD QUEBEC FRN20 1 2019-01-31 01IE.L USD ROY.BK.SCOTSERB 2 2019-01-31 01KG.L GBP SCOT.AMER.8%DB. 3 2019-01-31 01LQ.L GBP SCOT.PWR.UK6T23 4 2019-01-31 01LT.L GBP RES.MORT.4CTBRA Schema { date: str, stock_id: str, Country: str, stock_name: str }","title":"\u82f1\u570b\u80a1\u7968\u6e05\u55ae\u5217\u8868 UKStockInfo"},{"location":"tutor/UnitedKingdomMarket/Technical/#ukstockprice","text":"Example import requests import pandas as pd url = 'https://api.finmindtrade.com/api/v4/data' parameter = { \"dataset\" : \"UKStockPrice\" , \"data_id\" : \"BP.L\" , \"start_date\" : \"2020-06-16\" , \"end_date\" : \"2021-06-16\" , \"token\" : token , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } data = requests . get ( url , params = parameter ) data = data . json () data = pd . DataFrame ( data [ 'data' ]) print ( data . head ()) Output DataFrame date stock_id Adj_Close Close High Low Open Volume 0 2020-06-16 BP.L 290.26 321.65 331.8 318.65 322.15 55470516 1 2020-06-17 BP.L 285.84 316.75 328.35 315.1 322.85 38005133 2 2020-06-18 BP.L 284.13 314.85 316.7 308.9 313.9 33988764 3 2020-06-19 BP.L 290.08 321.45 325.95 315.7 316.8 95409968 4 2020-06-22 BP.L 284.67 315.45 318.9 311.1 318.15 58369094 Schema { date: str, stock_id: str, Adj_Close: float32, Close: float32, High: float32, Low: float32, Open: float32, Volume: int64 }","title":"\u82f1\u570b\u80a1\u50f9\u8cc7\u6599\u8868 UKStockPrice"},{"location":"tutor/UnitedStatesMarket/DataList/","text":"\u5728\u7f8e\u570b\u91d1\u878d\u5e02\u5834\uff0c\u6211\u5011\u64c1\u6709 1 \u7a2e\u8cc7\u6599\u96c6\uff0c\u5982\u4e0b: \u7f8e\u570b\u80a1\u7968\u6e05\u55ae\u5217\u8868 USStockInfo \u80a1\u50f9 TaiwanStock \u00b6 \u7f8e\u570b\u80a1\u50f9 minute \u8cc7\u6599\u8868 USStockPriceMinute \u7f8e\u570b\u80a1\u50f9 daily \u8cc7\u6599\u8868 USStockPrice \u5177\u9ad4 schemas \u8acb\u53c3\u8003 finmindapi","title":"\u7e3d\u89bd"},{"location":"tutor/UnitedStatesMarket/DataList/#taiwanstock","text":"\u7f8e\u570b\u80a1\u50f9 minute \u8cc7\u6599\u8868 USStockPriceMinute \u7f8e\u570b\u80a1\u50f9 daily \u8cc7\u6599\u8868 USStockPrice \u5177\u9ad4 schemas \u8acb\u53c3\u8003 finmindapi","title":"\u80a1\u50f9 TaiwanStock"},{"location":"tutor/UnitedStatesMarket/Technical/","text":"\u5728\u7f8e\u570b\u80a1\u7968\u8cc7\u6599\uff0c\u6211\u5011\u64c1\u6709 2 \u7a2e\u8cc7\u6599\u96c6\uff0c\u5982\u4e0b: \u7f8e\u570b\u80a1\u50f9 minute \u8cc7\u6599\u8868 USStockPriceMinute \u7f8e\u570b\u80a1\u50f9 daily \u8cc7\u6599\u8868 USStockPrice \u53e6\u5916\u91dd\u5c0d\u8cc7\u6599\u96c6\u6574\u7406\u51fa\u7684\u5217\u8868\u5982\u4e0b: \u7f8e\u570b\u80a1\u7968\u6e05\u55ae\u5217\u8868 USStockInfo \u6839\u64da\u4e0a\u8ff0\u8cc7\u6599\u8868\u9010\u4e00\u8aaa\u660e\u600e\u9ebc\u4f7f\u7528\uff0c\u53e6\u5916\u5177\u9ad4\u8cc7\u6599\u8868 schemas \u8acb\u53c3\u8003 finmindapi \u7f8e\u570b\u80a1\u7968\u6e05\u55ae\u5217\u8868 USStockInfo \u00b6 \u9019\u5f35\u8cc7\u6599\u8868\u4e3b\u8981\u662f\u5217\u51fa\u7f8e\u570b\u6240\u6709\u4e0a\u5e02\u4e0a\u6ac3\u7684\u80a1\u7968\u540d\u7a31\uff0c\u4ee3\u78bc\u548c\u7522\u696d\u985e\u5225 Example import requests import pandas as pd url = 'https://api.finmindtrade.com/api/v4/data' parameter = { \"dataset\" : \"USStockInfo\" } data = requests . get ( url , params = parameter ) data = data . json () data = pd . DataFrame ( data [ 'data' ]) print ( data . head ()) Output DataFrame date stock_id Country IPOYear MarketCap Subsector stock_name 0 2019-01-10 A United States 1999 21610000000 Biotechnology:LaboratoryAnalyticalInstruments Agilent Technologies, Inc. 1 2019-01-10 AA n/a 2016 5270000000 Aluminum Alcoa Corporation 2 2019-01-10 AABA United States 0 36780000000 EDPServices Altaba Inc. 3 2019-01-10 AAC United States 2014 55360000 MedicalSpecialities AAC Holdings, Inc. 4 2019-01-10 AAL United States 0 14930000000 AirFreight/DeliveryServices American Airlines Group, Inc. Schema { date: str, stock_id: str, Country: str, IPOYear: str, MarketCap: str, Subsector: str, stock_name: str } \u7f8e\u570b\u80a1\u50f9 minute \u8cc7\u6599\u8868 USStockPriceMinute \u00b6 \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u6bcf\u5929 08:00 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b Example import requests import pandas as pd url = 'https://api.finmindtrade.com/api/v4/data' parameter = { \"dataset\" : \"USStockPriceMinute\" , \"data_id\" : \"^DJI\" , \"start_date\" : \"2022-06-16\" , \"token\" : token , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } data = requests . get ( url , params = parameter ) data = data . json () data = pd . DataFrame ( data [ 'data' ]) print ( data . head ()) Output DataFrame date stock_id close high low open volume 0 2022-06-16 13:31:00 ^DJI 30082.5 30094.9 30076.6 30091.4 1686944 1 2022-06-16 13:32:00 ^DJI 30075.6 30087.9 30069.2 30081.7 1939377 2 2022-06-16 13:33:00 ^DJI 30039.5 30085.8 30036.4 30078.1 1589351 3 2022-06-16 13:34:00 ^DJI 30001.6 30036.5 29995.9 30036.5 2127155 4 2022-06-16 13:35:00 ^DJI 29996.7 30008 29984.3 30002.2 1917277 Schema { date: str, stock_id: str, close: float64, high: float64, low: float64, open: float64, volume: int64 } \u7f8e\u570b\u80a1\u50f9 daily \u8cc7\u6599\u8868 USStockPrice \u00b6 \u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u6bcf\u5929 08:00 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b Example import requests import pandas as pd url = 'https://api.finmindtrade.com/api/v4/data' parameter = { \"dataset\" : \"USStockPrice\" , \"data_id\" : \"^DJI\" , \"start_date\" : \"2020-06-16\" , \"end_date\" : \"2021-06-16\" , \"token\" : token , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } data = requests . get ( url , params = parameter ) data = data . json () data = pd . DataFrame ( data [ 'data' ]) print ( data . head ()) Output DataFrame date stock_id Adj_Close Close High Low Open Volume 0 2020-06-16 ^DJI 26290 26290 26611 25811.7 26326.7 489500000 1 2020-06-17 ^DJI 26119.6 26119.6 26400.1 26068.4 26330.5 328830000 2 2020-06-18 ^DJI 26080.1 26080.1 26154.2 25848.5 26016.5 328390000 3 2020-06-19 ^DJI 25871.5 25871.5 26451.4 25759.7 26213.1 669390000 4 2020-06-22 ^DJI 26025 26025 26059.8 25667.7 25865.1 351780000 Schema { date: str, stock_id: str, close: float64, high: float64, low: float64, open: float64, volume: int64 }","title":"\u6280\u8853\u9762"},{"location":"tutor/UnitedStatesMarket/Technical/#usstockinfo","text":"\u9019\u5f35\u8cc7\u6599\u8868\u4e3b\u8981\u662f\u5217\u51fa\u7f8e\u570b\u6240\u6709\u4e0a\u5e02\u4e0a\u6ac3\u7684\u80a1\u7968\u540d\u7a31\uff0c\u4ee3\u78bc\u548c\u7522\u696d\u985e\u5225 Example import requests import pandas as pd url = 'https://api.finmindtrade.com/api/v4/data' parameter = { \"dataset\" : \"USStockInfo\" } data = requests . get ( url , params = parameter ) data = data . json () data = pd . DataFrame ( data [ 'data' ]) print ( data . head ()) Output DataFrame date stock_id Country IPOYear MarketCap Subsector stock_name 0 2019-01-10 A United States 1999 21610000000 Biotechnology:LaboratoryAnalyticalInstruments Agilent Technologies, Inc. 1 2019-01-10 AA n/a 2016 5270000000 Aluminum Alcoa Corporation 2 2019-01-10 AABA United States 0 36780000000 EDPServices Altaba Inc. 3 2019-01-10 AAC United States 2014 55360000 MedicalSpecialities AAC Holdings, Inc. 4 2019-01-10 AAL United States 0 14930000000 AirFreight/DeliveryServices American Airlines Group, Inc. Schema { date: str, stock_id: str, Country: str, IPOYear: str, MarketCap: str, Subsector: str, stock_name: str }","title":"\u7f8e\u570b\u80a1\u7968\u6e05\u55ae\u5217\u8868 USStockInfo"},{"location":"tutor/UnitedStatesMarket/Technical/#minute-usstockpriceminute","text":"\u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u6bcf\u5929 08:00 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b Example import requests import pandas as pd url = 'https://api.finmindtrade.com/api/v4/data' parameter = { \"dataset\" : \"USStockPriceMinute\" , \"data_id\" : \"^DJI\" , \"start_date\" : \"2022-06-16\" , \"token\" : token , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } data = requests . get ( url , params = parameter ) data = data . json () data = pd . DataFrame ( data [ 'data' ]) print ( data . head ()) Output DataFrame date stock_id close high low open volume 0 2022-06-16 13:31:00 ^DJI 30082.5 30094.9 30076.6 30091.4 1686944 1 2022-06-16 13:32:00 ^DJI 30075.6 30087.9 30069.2 30081.7 1939377 2 2022-06-16 13:33:00 ^DJI 30039.5 30085.8 30036.4 30078.1 1589351 3 2022-06-16 13:34:00 ^DJI 30001.6 30036.5 29995.9 30036.5 2127155 4 2022-06-16 13:35:00 ^DJI 29996.7 30008 29984.3 30002.2 1917277 Schema { date: str, stock_id: str, close: float64, high: float64, low: float64, open: float64, volume: int64 }","title":"\u7f8e\u570b\u80a1\u50f9 minute \u8cc7\u6599\u8868 USStockPriceMinute"},{"location":"tutor/UnitedStatesMarket/Technical/#daily-usstockprice","text":"\u8cc7\u6599\u66f4\u65b0\u6642\u9593 \u6bcf\u5929 08:00 \uff0c\u5be6\u969b\u66f4\u65b0\u6642\u9593\u4ee5 API \u8cc7\u6599\u70ba\u4e3b Example import requests import pandas as pd url = 'https://api.finmindtrade.com/api/v4/data' parameter = { \"dataset\" : \"USStockPrice\" , \"data_id\" : \"^DJI\" , \"start_date\" : \"2020-06-16\" , \"end_date\" : \"2021-06-16\" , \"token\" : token , # \u53c3\u8003\u767b\u5165\uff0c\u7372\u53d6\u91d1\u9470 } data = requests . get ( url , params = parameter ) data = data . json () data = pd . DataFrame ( data [ 'data' ]) print ( data . head ()) Output DataFrame date stock_id Adj_Close Close High Low Open Volume 0 2020-06-16 ^DJI 26290 26290 26611 25811.7 26326.7 489500000 1 2020-06-17 ^DJI 26119.6 26119.6 26400.1 26068.4 26330.5 328830000 2 2020-06-18 ^DJI 26080.1 26080.1 26154.2 25848.5 26016.5 328390000 3 2020-06-19 ^DJI 25871.5 25871.5 26451.4 25759.7 26213.1 669390000 4 2020-06-22 ^DJI 26025 26025 26059.8 25667.7 25865.1 351780000 Schema { date: str, stock_id: str, close: float64, high: float64, low: float64, open: float64, volume: int64 }","title":"\u7f8e\u570b\u80a1\u50f9 daily \u8cc7\u6599\u8868 USStockPrice"},{"location":"tutor/analysis/Backtesting/","text":"\u7dda\u4e0a\u9023\u7d50 \u00b6 \u5982\u4e0b\u7a0b\u5f0f\u8aaa\u660e\u5982\u4f55\u4f7f\u7528 FinMind \u9032\u884c\u7b56\u7565\u56de\u6e2c\uff0c\u4e3b\u8981\u900f\u904e strategies \u4f86\u9032\u884c\u56de\u6e2c\uff0c DataLoader \u8b80\u53d6 FinMind \u63d0\u4f9b\u7684\u8cc7\u6599\u3002 \u5728\u9032\u884c\u56de\u6e2c\u7684\u904e\u7a0b\u4e2d\uff0c\u4e3b\u8981\u8981\u5148\u6c7a\u5b9a\u56de\u6e2c\u6a19\u7684\u3001\u56de\u6e2c\u5340\u9593\u3001\u8cc7\u91d1\u90e8\u4f4d\u3001\u4ea4\u6613\u7a05\u4ee5\u53ca\u7b56\u7565\u7684\u8a2d\u8a08\u3002 \u56de\u6e2c\u908f\u8f2f\u4e3b\u8981\u662f\u53bb\u6c7a\u5b9a \u9032\u5834\u3001\u7dad\u6301\u548c\u51fa\u5834 \u7684\u8a0a\u865f (signal)\uff0c\u4f8b\u5982: - \u4eca\u5929\u8a08\u7b97\u51fa\u4f86\u7684\u8a0a\u865f\u70ba -1\uff0c\u4ee3\u8868\u660e\u5929\u6703\u4ee5\u958b\u76e4\u50f9**\u8ce3\u6389** 1 \u5f35\u80a1\u7968 - \u4eca\u5929\u8a08\u7b97\u51fa\u4f86\u7684\u8a0a\u865f\u70ba 0\uff0c\u4ee3\u8868\u660e\u5929\u4ec0\u9ebc\u4e8b\u90fd\u4e0d\u505a - \u4eca\u5929\u8a08\u7b97\u51fa\u4f86\u7684\u8a0a\u865f\u70ba 1\uff0c\u4ee3\u8868\u660e\u5929\u6703\u4ee5\u958b\u76e4\u50f9**\u8cb7\u9032** 1 \u5f35\u80a1\u7968 \u56de\u6e2c\u7d50\u679c\u63d0\u4f9b\u8cc7\u8a0a\u5982\u4e0b: - trade_detail: \u56de\u6e2c\u8a73\u7d30\u8cc7\u6599 - compare_market_detail: \u5927\u76e4\u7d2f\u7a4d\u5831\u916c\u548c\u56de\u6e2c\u7d2f\u7a4d\u5831\u916c\u8d70\u52e2 - final_stats: \u56de\u6e2c\u7d50\u679c - compare_market_stats: \u5927\u76e4\u5e74\u5316\u5831\u916c\u7387\u548c\u7b56\u7565\u5e74\u5316\u5831\u916c\u7387 \u5728\u7bc4\u4f8b\u7a0b\u5f0f\u4e2d\uff0c\u4e3b\u8981\u5206\u6210\u4f7f\u7528 FinMind \u63d0\u4f9b\u7684\u7b56\u7565\u548c\u5ba2\u88fd\u5316\u7b56\u7565\u3002 \u521d\u59cb\u5316\uff0c\u8a2d\u5b9a\u56de\u6e2c\u80a1\u7968\u4ee3\u78bc\u3001\u6642\u9593\u5340\u9593 import numpy as np import pandas as pd from FinMind import strategies from FinMind.data import DataLoader from FinMind.strategies.base import Strategy from ta.momentum import StochasticOscillator data_loader = DataLoader () # data_loader.login(user_id, password) # \u53ef\u9078 obj = strategies . BackTest ( stock_id = \"0056\" , start_date = \"2018-01-01\" , end_date = \"2019-01-01\" , trader_fund = 500000.0 , fee = 0.001425 , data_loader = data_loader , ) obj . stock_price \u5c07\u6703\u7528\u4ee5\u4e0b data \u8a08\u7b97\u56de\u6e2c date stock_id Trading_Volume Trading_money open max min close spread Trading_turnover CashEarningsDistribution StockEarningsDistribution 0 2018 - 01 - 02 0056 1868451 46856990 25.00 25.14 25.00 25.13 0.13 733.0 0.0 0.0 1 2018 - 01 - 03 0056 3846820 97179269 25.15 25.31 25.15 25.31 0.18 1202.0 0.0 0.0 2 2018 - 01 - 04 0056 2736229 69496928 25.31 25.45 25.31 25.41 0.10 957.0 0.0 0.0 3 2018 - 01 - 05 0056 2383585 60620826 25.41 25.48 25.39 25.44 0.03 782.0 0.0 0.0 4 2018 - 01 - 08 0056 3048596 77817525 25.45 25.57 25.45 25.57 0.13 1236.0 0.0 0.0 .. ... ... ... ... ... ... ... ... ... ... ... ... 242 2018 - 12 - 24 0056 1501284 36271556 24.20 24.21 24.09 24.18 0.01 773.0 0.0 0.0 243 2018 - 12 - 25 0056 13908880 332473777 24.01 24.06 23.84 23.97 - 0.21 6359.0 0.0 0.0 244 2018 - 12 - 26 0056 12600245 300239138 24.09 24.09 23.67 23.72 - 0.25 6413.0 0.0 0.0 245 2018 - 12 - 27 0056 2986526 71566004 24.00 24.02 23.90 23.91 0.19 1651.0 0.0 0.0 246 2018 - 12 - 28 0056 2657586 63571334 23.93 23.96 23.89 23.94 0.03 1075.0 0.0 0.0 \u8a2d\u8a08\u7b56\u7565 class Kd ( Strategy ): \"\"\" summary: \u65e5KD 80 20 \u65e5K\u7dda <= 20 \u9032\u5834 \u65e5K\u7dda >= 80 \u51fa\u5834 \"\"\" kdays = 9 kd_upper = 80 kd_lower = 20 def create_trade_sign ( self , stock_price : pd . DataFrame ) -> pd . DataFrame : stock_price = stock_price . sort_values ( \"date\" ) kd = StochasticOscillator ( high = stock_price [ \"max\" ], low = stock_price [ \"min\" ], close = stock_price [ \"close\" ], n = self . kdays , ) rsv_ = kd . stoch () . fillna ( 50 ) _k = np . zeros ( stock_price . shape [ 0 ]) _d = np . zeros ( stock_price . shape [ 0 ]) for i , r in enumerate ( rsv_ ): if i == 0 : _k [ i ] = 50 _d [ i ] = 50 else : _k [ i ] = _k [ i - 1 ] * 2 / 3 + r / 3 _d [ i ] = _d [ i - 1 ] * 2 / 3 + _k [ i ] / 3 stock_price [ \"K\" ] = _k stock_price [ \"D\" ] = _d stock_price . index = range ( len ( stock_price )) stock_price [ \"signal\" ] = 0 stock_price . loc [ stock_price [ \"K\" ] <= self . kd_lower , \"signal\" ] = 1 stock_price . loc [ stock_price [ \"K\" ] >= self . kd_upper , \"signal\" ] = - 1 return stock_price \u56de\u6e2c\u6a21\u64ec\u4ea4\u6613 obj . add_strategy ( Kd ) obj . simulate () obj . final_stats output MeanProfit 2366.450976 MaxLoss - 1425.510000 FinalProfit 6196.970000 MeanProfitPer 0.470000 FinalProfitPer 1.240000 MaxLossPer - 0.290000 AnnualReturnPer 1.250000 AnnualSharpRatio 0.860000 dtype : float64 \u4ea4\u6613\u660e\u7d30 obj . trade_detail output stock_id date EverytimeProfit RealizedProfit UnrealizedProfit board_lot hold_cost hold_volume signal tax fee trade_price trader_fund 0 0056 2018 - 01 - 03 0.00 0.00 0.00 1000 0.000000 0 0 0.001 0.001425 25.15 500000.00000 1 0056 2018 - 01 - 04 0.00 0.00 0.00 1000 0.000000 0 0 0.001 0.001425 25.31 500000.00000 2 0056 2018 - 01 - 05 0.00 0.00 0.00 1000 0.000000 0 0 0.001 0.001425 25.41 500000.00000 3 0056 2018 - 01 - 08 0.00 0.00 0.00 1000 0.000000 0 0 0.001 0.001425 25.45 500000.00000 4 0056 2018 - 01 - 09 0.00 0.00 0.00 1000 0.000000 0 0 0.001 0.001425 25.55 500000.00000 .. ... ... ... ... ... ... ... ... ... ... ... ... ... 241 0056 2018 - 12 - 24 7274.35 5677.56 1596.79 1000 23.742118 4000 0 0.001 0.001425 24.20 410709.09425 242 0056 2018 - 12 - 25 6516.19 5677.56 838.63 1000 23.742118 4000 0 0.001 0.001425 24.01 410709.09425 243 0056 2018 - 12 - 26 6835.42 5677.56 1157.86 1000 23.742118 4000 0 0.001 0.001425 24.09 410709.09425 244 0056 2018 - 12 - 27 6476.29 5677.56 798.73 1000 23.742118 4000 0 0.001 0.001425 24.00 410709.09425 245 0056 2018 - 12 - 28 6196.97 5677.56 519.41 1000 23.742118 4000 0 0.001 0.001425 23.93 410709.09425 \u8996\u89ba\u5316 obj . plot ()","title":"\u56de\u6e2c"},{"location":"tutor/analysis/Backtesting/#_1","text":"\u5982\u4e0b\u7a0b\u5f0f\u8aaa\u660e\u5982\u4f55\u4f7f\u7528 FinMind \u9032\u884c\u7b56\u7565\u56de\u6e2c\uff0c\u4e3b\u8981\u900f\u904e strategies \u4f86\u9032\u884c\u56de\u6e2c\uff0c DataLoader \u8b80\u53d6 FinMind \u63d0\u4f9b\u7684\u8cc7\u6599\u3002 \u5728\u9032\u884c\u56de\u6e2c\u7684\u904e\u7a0b\u4e2d\uff0c\u4e3b\u8981\u8981\u5148\u6c7a\u5b9a\u56de\u6e2c\u6a19\u7684\u3001\u56de\u6e2c\u5340\u9593\u3001\u8cc7\u91d1\u90e8\u4f4d\u3001\u4ea4\u6613\u7a05\u4ee5\u53ca\u7b56\u7565\u7684\u8a2d\u8a08\u3002 \u56de\u6e2c\u908f\u8f2f\u4e3b\u8981\u662f\u53bb\u6c7a\u5b9a \u9032\u5834\u3001\u7dad\u6301\u548c\u51fa\u5834 \u7684\u8a0a\u865f (signal)\uff0c\u4f8b\u5982: - \u4eca\u5929\u8a08\u7b97\u51fa\u4f86\u7684\u8a0a\u865f\u70ba -1\uff0c\u4ee3\u8868\u660e\u5929\u6703\u4ee5\u958b\u76e4\u50f9**\u8ce3\u6389** 1 \u5f35\u80a1\u7968 - \u4eca\u5929\u8a08\u7b97\u51fa\u4f86\u7684\u8a0a\u865f\u70ba 0\uff0c\u4ee3\u8868\u660e\u5929\u4ec0\u9ebc\u4e8b\u90fd\u4e0d\u505a - \u4eca\u5929\u8a08\u7b97\u51fa\u4f86\u7684\u8a0a\u865f\u70ba 1\uff0c\u4ee3\u8868\u660e\u5929\u6703\u4ee5\u958b\u76e4\u50f9**\u8cb7\u9032** 1 \u5f35\u80a1\u7968 \u56de\u6e2c\u7d50\u679c\u63d0\u4f9b\u8cc7\u8a0a\u5982\u4e0b: - trade_detail: \u56de\u6e2c\u8a73\u7d30\u8cc7\u6599 - compare_market_detail: \u5927\u76e4\u7d2f\u7a4d\u5831\u916c\u548c\u56de\u6e2c\u7d2f\u7a4d\u5831\u916c\u8d70\u52e2 - final_stats: \u56de\u6e2c\u7d50\u679c - compare_market_stats: \u5927\u76e4\u5e74\u5316\u5831\u916c\u7387\u548c\u7b56\u7565\u5e74\u5316\u5831\u916c\u7387 \u5728\u7bc4\u4f8b\u7a0b\u5f0f\u4e2d\uff0c\u4e3b\u8981\u5206\u6210\u4f7f\u7528 FinMind \u63d0\u4f9b\u7684\u7b56\u7565\u548c\u5ba2\u88fd\u5316\u7b56\u7565\u3002 \u521d\u59cb\u5316\uff0c\u8a2d\u5b9a\u56de\u6e2c\u80a1\u7968\u4ee3\u78bc\u3001\u6642\u9593\u5340\u9593 import numpy as np import pandas as pd from FinMind import strategies from FinMind.data import DataLoader from FinMind.strategies.base import Strategy from ta.momentum import StochasticOscillator data_loader = DataLoader () # data_loader.login(user_id, password) # \u53ef\u9078 obj = strategies . BackTest ( stock_id = \"0056\" , start_date = \"2018-01-01\" , end_date = \"2019-01-01\" , trader_fund = 500000.0 , fee = 0.001425 , data_loader = data_loader , ) obj . stock_price \u5c07\u6703\u7528\u4ee5\u4e0b data \u8a08\u7b97\u56de\u6e2c date stock_id Trading_Volume Trading_money open max min close spread Trading_turnover CashEarningsDistribution StockEarningsDistribution 0 2018 - 01 - 02 0056 1868451 46856990 25.00 25.14 25.00 25.13 0.13 733.0 0.0 0.0 1 2018 - 01 - 03 0056 3846820 97179269 25.15 25.31 25.15 25.31 0.18 1202.0 0.0 0.0 2 2018 - 01 - 04 0056 2736229 69496928 25.31 25.45 25.31 25.41 0.10 957.0 0.0 0.0 3 2018 - 01 - 05 0056 2383585 60620826 25.41 25.48 25.39 25.44 0.03 782.0 0.0 0.0 4 2018 - 01 - 08 0056 3048596 77817525 25.45 25.57 25.45 25.57 0.13 1236.0 0.0 0.0 .. ... ... ... ... ... ... ... ... ... ... ... ... 242 2018 - 12 - 24 0056 1501284 36271556 24.20 24.21 24.09 24.18 0.01 773.0 0.0 0.0 243 2018 - 12 - 25 0056 13908880 332473777 24.01 24.06 23.84 23.97 - 0.21 6359.0 0.0 0.0 244 2018 - 12 - 26 0056 12600245 300239138 24.09 24.09 23.67 23.72 - 0.25 6413.0 0.0 0.0 245 2018 - 12 - 27 0056 2986526 71566004 24.00 24.02 23.90 23.91 0.19 1651.0 0.0 0.0 246 2018 - 12 - 28 0056 2657586 63571334 23.93 23.96 23.89 23.94 0.03 1075.0 0.0 0.0 \u8a2d\u8a08\u7b56\u7565 class Kd ( Strategy ): \"\"\" summary: \u65e5KD 80 20 \u65e5K\u7dda <= 20 \u9032\u5834 \u65e5K\u7dda >= 80 \u51fa\u5834 \"\"\" kdays = 9 kd_upper = 80 kd_lower = 20 def create_trade_sign ( self , stock_price : pd . DataFrame ) -> pd . DataFrame : stock_price = stock_price . sort_values ( \"date\" ) kd = StochasticOscillator ( high = stock_price [ \"max\" ], low = stock_price [ \"min\" ], close = stock_price [ \"close\" ], n = self . kdays , ) rsv_ = kd . stoch () . fillna ( 50 ) _k = np . zeros ( stock_price . shape [ 0 ]) _d = np . zeros ( stock_price . shape [ 0 ]) for i , r in enumerate ( rsv_ ): if i == 0 : _k [ i ] = 50 _d [ i ] = 50 else : _k [ i ] = _k [ i - 1 ] * 2 / 3 + r / 3 _d [ i ] = _d [ i - 1 ] * 2 / 3 + _k [ i ] / 3 stock_price [ \"K\" ] = _k stock_price [ \"D\" ] = _d stock_price . index = range ( len ( stock_price )) stock_price [ \"signal\" ] = 0 stock_price . loc [ stock_price [ \"K\" ] <= self . kd_lower , \"signal\" ] = 1 stock_price . loc [ stock_price [ \"K\" ] >= self . kd_upper , \"signal\" ] = - 1 return stock_price \u56de\u6e2c\u6a21\u64ec\u4ea4\u6613 obj . add_strategy ( Kd ) obj . simulate () obj . final_stats output MeanProfit 2366.450976 MaxLoss - 1425.510000 FinalProfit 6196.970000 MeanProfitPer 0.470000 FinalProfitPer 1.240000 MaxLossPer - 0.290000 AnnualReturnPer 1.250000 AnnualSharpRatio 0.860000 dtype : float64 \u4ea4\u6613\u660e\u7d30 obj . trade_detail output stock_id date EverytimeProfit RealizedProfit UnrealizedProfit board_lot hold_cost hold_volume signal tax fee trade_price trader_fund 0 0056 2018 - 01 - 03 0.00 0.00 0.00 1000 0.000000 0 0 0.001 0.001425 25.15 500000.00000 1 0056 2018 - 01 - 04 0.00 0.00 0.00 1000 0.000000 0 0 0.001 0.001425 25.31 500000.00000 2 0056 2018 - 01 - 05 0.00 0.00 0.00 1000 0.000000 0 0 0.001 0.001425 25.41 500000.00000 3 0056 2018 - 01 - 08 0.00 0.00 0.00 1000 0.000000 0 0 0.001 0.001425 25.45 500000.00000 4 0056 2018 - 01 - 09 0.00 0.00 0.00 1000 0.000000 0 0 0.001 0.001425 25.55 500000.00000 .. ... ... ... ... ... ... ... ... ... ... ... ... ... 241 0056 2018 - 12 - 24 7274.35 5677.56 1596.79 1000 23.742118 4000 0 0.001 0.001425 24.20 410709.09425 242 0056 2018 - 12 - 25 6516.19 5677.56 838.63 1000 23.742118 4000 0 0.001 0.001425 24.01 410709.09425 243 0056 2018 - 12 - 26 6835.42 5677.56 1157.86 1000 23.742118 4000 0 0.001 0.001425 24.09 410709.09425 244 0056 2018 - 12 - 27 6476.29 5677.56 798.73 1000 23.742118 4000 0 0.001 0.001425 24.00 410709.09425 245 0056 2018 - 12 - 28 6196.97 5677.56 519.41 1000 23.742118 4000 0 0.001 0.001425 23.93 410709.09425 \u8996\u89ba\u5316 obj . plot ()","title":"\u7dda\u4e0a\u9023\u7d50"},{"location":"tutor/analysis/BacktestingOtherData/","text":"\u7dda\u4e0a\u9023\u7d50 \u00b6 pip install FinMind \u521d\u59cb\u5316\uff0c\u8a2d\u5b9a\u56de\u6e2c\u80a1\u7968\u4ee3\u78bc\u3001\u6642\u9593\u5340\u9593 import numpy as np import pandas as pd from FinMind import strategies from FinMind.data import DataLoader from FinMind.strategies.base import Strategy from ta.momentum import StochasticOscillator data_loader = DataLoader () # data_loader.login(user_id, password) # \u53ef\u9078 obj = strategies . BackTest ( stock_id = \"0056\" , start_date = \"2018-01-01\" , end_date = \"2019-01-01\" , trader_fund = 500000.0 , fee = 0.001425 , data_loader = data_loader , ) obj . stock_price \u5c07\u6703\u7528\u4ee5\u4e0b data \u8a08\u7b97\u56de\u6e2c date stock_id Trading_Volume Trading_money open max min close spread Trading_turnover CashEarningsDistribution StockEarningsDistribution 0 2018 - 01 - 02 2330 18055269 4188555408 231.5 232.5 231.0 232.5 3.0 9954.0 0.0 0.0 1 2018 - 01 - 03 2330 31706091 7504382512 236.0 238.0 235.5 237.0 4.5 13633.0 0.0 0.0 2 2018 - 01 - 04 2330 29179613 6963192636 240.0 240.0 236.5 239.5 2.5 10953.0 0.0 0.0 3 2018 - 01 - 05 2330 23721255 5681934695 240.0 240.0 238.0 240.0 0.5 8659.0 0.0 0.0 4 2018 - 01 - 08 2330 21846692 5281823362 242.0 242.5 240.5 242.0 2.0 10251.0 0.0 0.0 .. ... ... ... ... ... ... ... ... ... ... ... ... 729 2020 - 12 - 25 2330 12581145 6449612552 514.0 515.0 510.0 511.0 1.0 14988.0 0.0 0.0 730 2020 - 12 - 28 2330 19262886 9890545245 512.0 515.0 509.0 515.0 4.0 16673.0 0.0 0.0 731 2020 - 12 - 29 2330 20151736 10370562545 515.0 517.0 513.0 515.0 0.0 17186.0 0.0 0.0 732 2020 - 12 - 30 2330 46705107 24306881615 516.0 525.0 514.0 525.0 10.0 33173.0 0.0 0.0 733 2020 - 12 - 31 2330 30326332 15989936054 526.0 530.0 524.0 530.0 5.0 25134.0 0.0 0.0 \u8a2d\u8a08\u7b56\u7565 class ShortSaleMarginPurchaseRatio ( Strategy ): \"\"\" summary: \u7b56\u7565\u6982\u5ff5: \u5238\u8cc7\u6bd4\u8d8a\u9ad8\u4ee3\u8868\u6563\u6236\u770b\u7a7a\uff0c\u6cd5\u4eba\u8cb7\u8d85\u80a1\u7968\u6703\u4e0a\u6f32\uff0c\u9019\u6642\u5019\u8ce3\u53ef\u4ee5\u8ddf\u5927\u90e8\u5206\u6563\u6236\u9032\u884c\u76f8\u53cd\u7684\u64cd\u4f5c\uff0c\u53cd\u4e4b\u4ea6\u7136 \u7b56\u7565\u898f\u5247: \u5238\u8cc7\u6bd4>=30% \u4e14\u6cd5\u4eba\u8cb7\u8d85\u80a1\u7968, \u8ce3 \u5238\u8cc7\u6bd4<30% \u4e14\u6cd5\u4eba\u8ce3\u8d85\u80a1\u7968 \u8cb7 \"\"\" ShortSaleMarginPurchaseTodayRatioThreshold = 0.3 def load_taiwan_stock_margin_purchase_short_sale ( self ): self . TaiwanStockMarginPurchaseShortSale = ( self . data_loader . taiwan_stock_margin_purchase_short_sale ( stock_id = self . stock_id , start_date = self . start_date , end_date = self . end_date , ) ) self . TaiwanStockMarginPurchaseShortSale [ [ \"ShortSaleTodayBalance\" , \"MarginPurchaseTodayBalance\" ] ] = self . TaiwanStockMarginPurchaseShortSale [ [ \"ShortSaleTodayBalance\" , \"MarginPurchaseTodayBalance\" ] ] . astype ( int ) self . TaiwanStockMarginPurchaseShortSale [ \"ShortSaleMarginPurchaseTodayRatio\" ] = ( self . TaiwanStockMarginPurchaseShortSale [ \"ShortSaleTodayBalance\" ] / self . TaiwanStockMarginPurchaseShortSale [ \"MarginPurchaseTodayBalance\" ] ) def load_institutional_investors_buy_sell ( self ): self . InstitutionalInvestorsBuySell = ( self . data_loader . taiwan_stock_institutional_investors ( stock_id = self . stock_id , start_date = self . start_date , end_date = self . end_date , ) ) self . InstitutionalInvestorsBuySell [[ \"sell\" , \"buy\" ]] = ( self . InstitutionalInvestorsBuySell [[ \"sell\" , \"buy\" ]] . fillna ( 0 ) . astype ( int ) ) self . InstitutionalInvestorsBuySell = ( self . InstitutionalInvestorsBuySell . groupby ( [ \"date\" , \"stock_id\" ], as_index = False ) . agg ({ \"buy\" : np . sum , \"sell\" : np . sum }) ) self . InstitutionalInvestorsBuySell [ \"diff\" ] = ( self . InstitutionalInvestorsBuySell [ \"buy\" ] - self . InstitutionalInvestorsBuySell [ \"sell\" ] ) def create_trade_sign ( self , stock_price : pd . DataFrame ) -> pd . DataFrame : stock_price = stock_price . sort_values ( \"date\" ) self . load_taiwan_stock_margin_purchase_short_sale () self . load_institutional_investors_buy_sell () stock_price = pd . merge ( stock_price , self . InstitutionalInvestorsBuySell [[ \"stock_id\" , \"date\" , \"diff\" ]], on = [ \"stock_id\" , \"date\" ], how = \"left\" , ) . fillna ( 0 ) stock_price = pd . merge ( stock_price , self . TaiwanStockMarginPurchaseShortSale [ [ \"stock_id\" , \"date\" , \"ShortSaleMarginPurchaseTodayRatio\" ] ], on = [ \"stock_id\" , \"date\" ], how = \"left\" , ) . fillna ( 0 ) stock_price . index = range ( len ( stock_price )) stock_price [ \"signal\" ] = 0 sell_mask = ( stock_price [ \"ShortSaleMarginPurchaseTodayRatio\" ] >= self . ShortSaleMarginPurchaseTodayRatioThreshold ) & ( stock_price [ \"diff\" ] > 0 ) stock_price . loc [ sell_mask , \"signal\" ] = - 1 buy_mask = ( stock_price [ \"ShortSaleMarginPurchaseTodayRatio\" ] < self . ShortSaleMarginPurchaseTodayRatioThreshold ) & ( stock_price [ \"diff\" ] < 0 ) stock_price . loc [ buy_mask , \"signal\" ] = 1 return stock_price \u56de\u6e2c\u6a21\u64ec\u4ea4\u6613 obj . add_strategy ( ShortSaleMarginPurchaseRatio ) obj . simulate () obj . final_stats output MeanProfit 187013.454352 MaxLoss - 17592.160000 FinalProfit 716596.810000 MeanProfitPer 37.400000 FinalProfitPer 143.320000 MaxLossPer - 3.520000 AnnualReturnPer 34.500000 AnnualSharpRatio 1.430000 dtype : float64 \u4ea4\u6613\u660e\u7d30 obj . trade_detail output stock_id date EverytimeProfit RealizedProfit UnrealizedProfit board_lot hold_cost hold_volume signal tax fee trade_price trader_fund 0 2330 2018 - 01 - 03 0.00 0.00 0.00 1000 0.0000 0 0 0.003 0.001425 236.0 500000.000 1 2330 2018 - 01 - 04 0.00 0.00 0.00 1000 0.0000 0 0 0.003 0.001425 240.0 500000.000 2 2330 2018 - 01 - 05 0.00 0.00 0.00 1000 0.0000 0 0 0.003 0.001425 240.0 500000.000 3 2330 2018 - 01 - 08 0.00 0.00 0.00 1000 0.0000 0 - 1 0.003 0.001425 242.0 500000.000 4 2330 2018 - 01 - 09 0.00 0.00 0.00 1000 0.0000 0 - 1 0.003 0.001425 242.0 500000.000 .. ... ... ... ... ... ... ... ... ... ... ... ... ... 728 2330 2020 - 12 - 25 692703.01 160992.91 531710.10 1000 245.8705 2000 0 0.003 0.001425 514.0 47251.925 729 2330 2020 - 12 - 28 688720.71 160992.91 527727.80 1000 245.8705 2000 0 0.003 0.001425 512.0 47251.925 730 2330 2020 - 12 - 29 694694.16 160992.91 533701.25 1000 245.8705 2000 0 0.003 0.001425 515.0 47251.925 731 2330 2020 - 12 - 30 696685.31 160992.91 535692.40 1000 245.8705 2000 0 0.003 0.001425 516.0 47251.925 732 2330 2020 - 12 - 31 716596.81 160992.91 555603.90 1000 245.8705 2000 0 0.003 0.001425 526.0 47251.925 \u8996\u89ba\u5316 obj . plot ()","title":"\u56de\u6e2c(\u5f15\u7528\u5916\u90e8 data)"},{"location":"tutor/analysis/BacktestingOtherData/#_1","text":"pip install FinMind \u521d\u59cb\u5316\uff0c\u8a2d\u5b9a\u56de\u6e2c\u80a1\u7968\u4ee3\u78bc\u3001\u6642\u9593\u5340\u9593 import numpy as np import pandas as pd from FinMind import strategies from FinMind.data import DataLoader from FinMind.strategies.base import Strategy from ta.momentum import StochasticOscillator data_loader = DataLoader () # data_loader.login(user_id, password) # \u53ef\u9078 obj = strategies . BackTest ( stock_id = \"0056\" , start_date = \"2018-01-01\" , end_date = \"2019-01-01\" , trader_fund = 500000.0 , fee = 0.001425 , data_loader = data_loader , ) obj . stock_price \u5c07\u6703\u7528\u4ee5\u4e0b data \u8a08\u7b97\u56de\u6e2c date stock_id Trading_Volume Trading_money open max min close spread Trading_turnover CashEarningsDistribution StockEarningsDistribution 0 2018 - 01 - 02 2330 18055269 4188555408 231.5 232.5 231.0 232.5 3.0 9954.0 0.0 0.0 1 2018 - 01 - 03 2330 31706091 7504382512 236.0 238.0 235.5 237.0 4.5 13633.0 0.0 0.0 2 2018 - 01 - 04 2330 29179613 6963192636 240.0 240.0 236.5 239.5 2.5 10953.0 0.0 0.0 3 2018 - 01 - 05 2330 23721255 5681934695 240.0 240.0 238.0 240.0 0.5 8659.0 0.0 0.0 4 2018 - 01 - 08 2330 21846692 5281823362 242.0 242.5 240.5 242.0 2.0 10251.0 0.0 0.0 .. ... ... ... ... ... ... ... ... ... ... ... ... 729 2020 - 12 - 25 2330 12581145 6449612552 514.0 515.0 510.0 511.0 1.0 14988.0 0.0 0.0 730 2020 - 12 - 28 2330 19262886 9890545245 512.0 515.0 509.0 515.0 4.0 16673.0 0.0 0.0 731 2020 - 12 - 29 2330 20151736 10370562545 515.0 517.0 513.0 515.0 0.0 17186.0 0.0 0.0 732 2020 - 12 - 30 2330 46705107 24306881615 516.0 525.0 514.0 525.0 10.0 33173.0 0.0 0.0 733 2020 - 12 - 31 2330 30326332 15989936054 526.0 530.0 524.0 530.0 5.0 25134.0 0.0 0.0 \u8a2d\u8a08\u7b56\u7565 class ShortSaleMarginPurchaseRatio ( Strategy ): \"\"\" summary: \u7b56\u7565\u6982\u5ff5: \u5238\u8cc7\u6bd4\u8d8a\u9ad8\u4ee3\u8868\u6563\u6236\u770b\u7a7a\uff0c\u6cd5\u4eba\u8cb7\u8d85\u80a1\u7968\u6703\u4e0a\u6f32\uff0c\u9019\u6642\u5019\u8ce3\u53ef\u4ee5\u8ddf\u5927\u90e8\u5206\u6563\u6236\u9032\u884c\u76f8\u53cd\u7684\u64cd\u4f5c\uff0c\u53cd\u4e4b\u4ea6\u7136 \u7b56\u7565\u898f\u5247: \u5238\u8cc7\u6bd4>=30% \u4e14\u6cd5\u4eba\u8cb7\u8d85\u80a1\u7968, \u8ce3 \u5238\u8cc7\u6bd4<30% \u4e14\u6cd5\u4eba\u8ce3\u8d85\u80a1\u7968 \u8cb7 \"\"\" ShortSaleMarginPurchaseTodayRatioThreshold = 0.3 def load_taiwan_stock_margin_purchase_short_sale ( self ): self . TaiwanStockMarginPurchaseShortSale = ( self . data_loader . taiwan_stock_margin_purchase_short_sale ( stock_id = self . stock_id , start_date = self . start_date , end_date = self . end_date , ) ) self . TaiwanStockMarginPurchaseShortSale [ [ \"ShortSaleTodayBalance\" , \"MarginPurchaseTodayBalance\" ] ] = self . TaiwanStockMarginPurchaseShortSale [ [ \"ShortSaleTodayBalance\" , \"MarginPurchaseTodayBalance\" ] ] . astype ( int ) self . TaiwanStockMarginPurchaseShortSale [ \"ShortSaleMarginPurchaseTodayRatio\" ] = ( self . TaiwanStockMarginPurchaseShortSale [ \"ShortSaleTodayBalance\" ] / self . TaiwanStockMarginPurchaseShortSale [ \"MarginPurchaseTodayBalance\" ] ) def load_institutional_investors_buy_sell ( self ): self . InstitutionalInvestorsBuySell = ( self . data_loader . taiwan_stock_institutional_investors ( stock_id = self . stock_id , start_date = self . start_date , end_date = self . end_date , ) ) self . InstitutionalInvestorsBuySell [[ \"sell\" , \"buy\" ]] = ( self . InstitutionalInvestorsBuySell [[ \"sell\" , \"buy\" ]] . fillna ( 0 ) . astype ( int ) ) self . InstitutionalInvestorsBuySell = ( self . InstitutionalInvestorsBuySell . groupby ( [ \"date\" , \"stock_id\" ], as_index = False ) . agg ({ \"buy\" : np . sum , \"sell\" : np . sum }) ) self . InstitutionalInvestorsBuySell [ \"diff\" ] = ( self . InstitutionalInvestorsBuySell [ \"buy\" ] - self . InstitutionalInvestorsBuySell [ \"sell\" ] ) def create_trade_sign ( self , stock_price : pd . DataFrame ) -> pd . DataFrame : stock_price = stock_price . sort_values ( \"date\" ) self . load_taiwan_stock_margin_purchase_short_sale () self . load_institutional_investors_buy_sell () stock_price = pd . merge ( stock_price , self . InstitutionalInvestorsBuySell [[ \"stock_id\" , \"date\" , \"diff\" ]], on = [ \"stock_id\" , \"date\" ], how = \"left\" , ) . fillna ( 0 ) stock_price = pd . merge ( stock_price , self . TaiwanStockMarginPurchaseShortSale [ [ \"stock_id\" , \"date\" , \"ShortSaleMarginPurchaseTodayRatio\" ] ], on = [ \"stock_id\" , \"date\" ], how = \"left\" , ) . fillna ( 0 ) stock_price . index = range ( len ( stock_price )) stock_price [ \"signal\" ] = 0 sell_mask = ( stock_price [ \"ShortSaleMarginPurchaseTodayRatio\" ] >= self . ShortSaleMarginPurchaseTodayRatioThreshold ) & ( stock_price [ \"diff\" ] > 0 ) stock_price . loc [ sell_mask , \"signal\" ] = - 1 buy_mask = ( stock_price [ \"ShortSaleMarginPurchaseTodayRatio\" ] < self . ShortSaleMarginPurchaseTodayRatioThreshold ) & ( stock_price [ \"diff\" ] < 0 ) stock_price . loc [ buy_mask , \"signal\" ] = 1 return stock_price \u56de\u6e2c\u6a21\u64ec\u4ea4\u6613 obj . add_strategy ( ShortSaleMarginPurchaseRatio ) obj . simulate () obj . final_stats output MeanProfit 187013.454352 MaxLoss - 17592.160000 FinalProfit 716596.810000 MeanProfitPer 37.400000 FinalProfitPer 143.320000 MaxLossPer - 3.520000 AnnualReturnPer 34.500000 AnnualSharpRatio 1.430000 dtype : float64 \u4ea4\u6613\u660e\u7d30 obj . trade_detail output stock_id date EverytimeProfit RealizedProfit UnrealizedProfit board_lot hold_cost hold_volume signal tax fee trade_price trader_fund 0 2330 2018 - 01 - 03 0.00 0.00 0.00 1000 0.0000 0 0 0.003 0.001425 236.0 500000.000 1 2330 2018 - 01 - 04 0.00 0.00 0.00 1000 0.0000 0 0 0.003 0.001425 240.0 500000.000 2 2330 2018 - 01 - 05 0.00 0.00 0.00 1000 0.0000 0 0 0.003 0.001425 240.0 500000.000 3 2330 2018 - 01 - 08 0.00 0.00 0.00 1000 0.0000 0 - 1 0.003 0.001425 242.0 500000.000 4 2330 2018 - 01 - 09 0.00 0.00 0.00 1000 0.0000 0 - 1 0.003 0.001425 242.0 500000.000 .. ... ... ... ... ... ... ... ... ... ... ... ... ... 728 2330 2020 - 12 - 25 692703.01 160992.91 531710.10 1000 245.8705 2000 0 0.003 0.001425 514.0 47251.925 729 2330 2020 - 12 - 28 688720.71 160992.91 527727.80 1000 245.8705 2000 0 0.003 0.001425 512.0 47251.925 730 2330 2020 - 12 - 29 694694.16 160992.91 533701.25 1000 245.8705 2000 0 0.003 0.001425 515.0 47251.925 731 2330 2020 - 12 - 30 696685.31 160992.91 535692.40 1000 245.8705 2000 0 0.003 0.001425 516.0 47251.925 732 2330 2020 - 12 - 31 716596.81 160992.91 555603.90 1000 245.8705 2000 0 0.003 0.001425 526.0 47251.925 \u8996\u89ba\u5316 obj . plot ()","title":"\u7dda\u4e0a\u9023\u7d50"},{"location":"tutor/analysis/CustomerDashboardWebServer/","text":"\u5ba2\u88fd\u5316\u770b\u76e4\u5100\u8868\u677f\u3001\u4e26\u5728 local \u67b6\u8a2d web \u00b6 K \u7dda from pathlib import Path from flask import Flask , render_template , request from loguru import logger from pyecharts.charts import Page import FinMind from FinMind import plotting from FinMind.data import DataLoader def get_path (): path = Path ( FinMind . __file__ ) path = path . parent . joinpath ( \"templates\" ) return path PATH = get_path () def kline ( data_loader : DataLoader , stock_id : str , start_date : str , end_date : str ): stock_data = data_loader . taiwan_stock_daily ( stock_id , start_date , end_date ) stock_data = data_loader . feature . add_kline_institutional_investors ( stock_data ) stock_data = data_loader . feature . add_kline_margin_purchase_short_sale ( stock_data ) # \u7e6a\u88fdk\u7dda\u5716 kline_plot = plotting . kline ( stock_data ) return kline_plot \u6708\u71df\u6536\u3001\u9577\u689d\u5716 def bar ( data_loader : DataLoader , stock_id : str , start_date : str , end_date : str ): df = data_loader . taiwan_stock_month_revenue ( stock_id = stock_id , start_date = start_date , end_date = end_date ) df [ \"labels\" ] = ( df [[ \"revenue_year\" , \"revenue_month\" ]] . astype ( str ) . apply ( lambda date : f \" { date [ 0 ] } - { date [ 1 ] } M\" , axis = 1 ) ) df [ \"series\" ] = df [ \"revenue\" ] . map ( lambda value : round ( value * 1e-8 , 2 )) bar_plot = plotting . bar ( labels = df [ \"labels\" ], series = df [ \"series\" ], title = \"\u6708\u71df\u6536\" , yaxis_color = \"orange\" , y_axis_name = \"\u5104\" , ) return bar_plot \u5916\u8cc7\u6301\u80a1\u6bd4\u4f8b\u3001\u7dda\u5716 def line ( data_loader : DataLoader , stock_id : str , start_date : str , end_date : str ): df = data_loader . taiwan_stock_shareholding ( stock_id = stock_id , start_date = start_date , end_date = end_date ) df [ \"series\" ] = df [ \"ForeignInvestmentSharesRatio\" ] . map ( lambda value : round ( value * 1e-2 , 2 ) ) df [ \"labels\" ] = df [ \"date\" ] line_plot = plotting . line ( labels = df [ \"labels\" ], series = df [ \"series\" ], title = \"\u5916\u8cc7\u6301\u80a1\u6bd4\u4f8b\" , yaxis_color = \"blue\" , y_axis_name = \"\" , ) return line_plot \u80a1\u6b0a\u5206\u6563\u8868\u3001\u5713\u9905\u5716 def pie ( data_loader : DataLoader , stock_id : str , start_date : str , end_date : str ): df = data_loader . taiwan_stock_holding_shares_per ( stock_id = stock_id , start_date = start_date , end_date = end_date ) df = df [ df [ \"date\" ] == max ( df [ \"date\" ])] df = df [ df [ \"HoldingSharesLevel\" ] != \"total\" ] df [ \"labels\" ] = df [ \"HoldingSharesLevel\" ] df [ \"series\" ] = df [ \"percent\" ] pie_plot = plotting . pie ( labels = df [ \"labels\" ], series = df [ \"series\" ], title = \"\u80a1\u6b0a\u5206\u6563\u8868\" ) return pie_plot \u5408\u4f75\u6210 dashboard def dashboard ( stock_id : str , start_date : str , end_date : str ): data_loader = DataLoader () page = Page ( layout = Page . SimplePageLayout ) page . add ( kline ( data_loader , stock_id , start_date , end_date ), bar ( data_loader , stock_id , start_date , end_date ), line ( data_loader , stock_id , start_date , end_date ), pie ( data_loader , stock_id , start_date , end_date ), ) dashboard_html_path = str ( PATH . joinpath ( \"dashboard.html\" )) post_html_path = str ( PATH . joinpath ( \"post.html\" )) page . render ( dashboard_html_path ) post_html = open ( post_html_path , \"r\" , encoding = \"utf-8\" ) . read () dashboard_html = open ( dashboard_html_path , \"r\" , encoding = \"utf-8\" ) . read () html = post_html . replace ( \"DASHBOARD\" , dashboard_html ) with open ( dashboard_html_path , \"w\" , encoding = \"utf-8\" ) as e : e . write ( html ) \u7528 flask run web app = Flask ( __name__ , template_folder = str ( PATH )) @app . route ( \"/\" , methods = [ \"GET\" , \"POST\" ]) def submit (): if request . method == \"POST\" : stock_id = request . form . get ( \"stock_id\" ) start_date = request . form . get ( \"start_date\" ) end_date = request . form . get ( \"end_date\" ) logger . info ( stock_id ) dashboard ( stock_id = stock_id , start_date = start_date , end_date = end_date ) return render_template ( \"dashboard.html\" ) return render_template ( \"post.html\" ) app . run ( host = \"0.0.0.0\" , debug = True )","title":"\u5ba2\u88fd\u5316\u770b\u76e4\u5100\u8868\u677f"},{"location":"tutor/analysis/CustomerDashboardWebServer/#local-web","text":"K \u7dda from pathlib import Path from flask import Flask , render_template , request from loguru import logger from pyecharts.charts import Page import FinMind from FinMind import plotting from FinMind.data import DataLoader def get_path (): path = Path ( FinMind . __file__ ) path = path . parent . joinpath ( \"templates\" ) return path PATH = get_path () def kline ( data_loader : DataLoader , stock_id : str , start_date : str , end_date : str ): stock_data = data_loader . taiwan_stock_daily ( stock_id , start_date , end_date ) stock_data = data_loader . feature . add_kline_institutional_investors ( stock_data ) stock_data = data_loader . feature . add_kline_margin_purchase_short_sale ( stock_data ) # \u7e6a\u88fdk\u7dda\u5716 kline_plot = plotting . kline ( stock_data ) return kline_plot \u6708\u71df\u6536\u3001\u9577\u689d\u5716 def bar ( data_loader : DataLoader , stock_id : str , start_date : str , end_date : str ): df = data_loader . taiwan_stock_month_revenue ( stock_id = stock_id , start_date = start_date , end_date = end_date ) df [ \"labels\" ] = ( df [[ \"revenue_year\" , \"revenue_month\" ]] . astype ( str ) . apply ( lambda date : f \" { date [ 0 ] } - { date [ 1 ] } M\" , axis = 1 ) ) df [ \"series\" ] = df [ \"revenue\" ] . map ( lambda value : round ( value * 1e-8 , 2 )) bar_plot = plotting . bar ( labels = df [ \"labels\" ], series = df [ \"series\" ], title = \"\u6708\u71df\u6536\" , yaxis_color = \"orange\" , y_axis_name = \"\u5104\" , ) return bar_plot \u5916\u8cc7\u6301\u80a1\u6bd4\u4f8b\u3001\u7dda\u5716 def line ( data_loader : DataLoader , stock_id : str , start_date : str , end_date : str ): df = data_loader . taiwan_stock_shareholding ( stock_id = stock_id , start_date = start_date , end_date = end_date ) df [ \"series\" ] = df [ \"ForeignInvestmentSharesRatio\" ] . map ( lambda value : round ( value * 1e-2 , 2 ) ) df [ \"labels\" ] = df [ \"date\" ] line_plot = plotting . line ( labels = df [ \"labels\" ], series = df [ \"series\" ], title = \"\u5916\u8cc7\u6301\u80a1\u6bd4\u4f8b\" , yaxis_color = \"blue\" , y_axis_name = \"\" , ) return line_plot \u80a1\u6b0a\u5206\u6563\u8868\u3001\u5713\u9905\u5716 def pie ( data_loader : DataLoader , stock_id : str , start_date : str , end_date : str ): df = data_loader . taiwan_stock_holding_shares_per ( stock_id = stock_id , start_date = start_date , end_date = end_date ) df = df [ df [ \"date\" ] == max ( df [ \"date\" ])] df = df [ df [ \"HoldingSharesLevel\" ] != \"total\" ] df [ \"labels\" ] = df [ \"HoldingSharesLevel\" ] df [ \"series\" ] = df [ \"percent\" ] pie_plot = plotting . pie ( labels = df [ \"labels\" ], series = df [ \"series\" ], title = \"\u80a1\u6b0a\u5206\u6563\u8868\" ) return pie_plot \u5408\u4f75\u6210 dashboard def dashboard ( stock_id : str , start_date : str , end_date : str ): data_loader = DataLoader () page = Page ( layout = Page . SimplePageLayout ) page . add ( kline ( data_loader , stock_id , start_date , end_date ), bar ( data_loader , stock_id , start_date , end_date ), line ( data_loader , stock_id , start_date , end_date ), pie ( data_loader , stock_id , start_date , end_date ), ) dashboard_html_path = str ( PATH . joinpath ( \"dashboard.html\" )) post_html_path = str ( PATH . joinpath ( \"post.html\" )) page . render ( dashboard_html_path ) post_html = open ( post_html_path , \"r\" , encoding = \"utf-8\" ) . read () dashboard_html = open ( dashboard_html_path , \"r\" , encoding = \"utf-8\" ) . read () html = post_html . replace ( \"DASHBOARD\" , dashboard_html ) with open ( dashboard_html_path , \"w\" , encoding = \"utf-8\" ) as e : e . write ( html ) \u7528 flask run web app = Flask ( __name__ , template_folder = str ( PATH )) @app . route ( \"/\" , methods = [ \"GET\" , \"POST\" ]) def submit (): if request . method == \"POST\" : stock_id = request . form . get ( \"stock_id\" ) start_date = request . form . get ( \"start_date\" ) end_date = request . form . get ( \"end_date\" ) logger . info ( stock_id ) dashboard ( stock_id = stock_id , start_date = start_date , end_date = end_date ) return render_template ( \"dashboard.html\" ) return render_template ( \"post.html\" ) app . run ( host = \"0.0.0.0\" , debug = True )","title":"\u5ba2\u88fd\u5316\u770b\u76e4\u5100\u8868\u677f\u3001\u4e26\u5728 local \u67b6\u8a2d web"},{"location":"tutor/analysis/Kline/","text":"kbar_plotting.ipynb \u00b6 \u7e6a\u88fd K \u7dda # \u53d6\u5f97\u80a1\u50f9 from FinMind.data import DataLoader dl = DataLoader () # \u4e0b\u8f09\u53f0\u80a1\u80a1\u50f9\u8cc7\u6599 stock_data = dl . taiwan_stock_daily ( stock_id = '2609' , start_date = '2018-01-01' , end_date = '2021-06-26' ) # \u4e0b\u8f09\u4e09\u5927\u6cd5\u4eba\u8cc7\u6599 stock_data = dl . feature . add_kline_institutional_investors ( stock_data ) # \u4e0b\u8f09\u878d\u8cc7\u5238\u8cc7\u6599 stock_data = dl . feature . add_kline_margin_purchase_short_sale ( stock_data ) # \u7e6a\u88fdk\u7dda\u5716 from FinMind import plotting plotting . kline ( stock_data )","title":"K \u7dda"},{"location":"tutor/analysis/Kline/#kbar_plottingipynb","text":"\u7e6a\u88fd K \u7dda # \u53d6\u5f97\u80a1\u50f9 from FinMind.data import DataLoader dl = DataLoader () # \u4e0b\u8f09\u53f0\u80a1\u80a1\u50f9\u8cc7\u6599 stock_data = dl . taiwan_stock_daily ( stock_id = '2609' , start_date = '2018-01-01' , end_date = '2021-06-26' ) # \u4e0b\u8f09\u4e09\u5927\u6cd5\u4eba\u8cc7\u6599 stock_data = dl . feature . add_kline_institutional_investors ( stock_data ) # \u4e0b\u8f09\u878d\u8cc7\u5238\u8cc7\u6599 stock_data = dl . feature . add_kline_margin_purchase_short_sale ( stock_data ) # \u7e6a\u88fdk\u7dda\u5716 from FinMind import plotting plotting . kline ( stock_data )","title":"kbar_plotting.ipynb"},{"location":"tutor/analysis/RealTimeDataSubscription/","text":"\u5373\u6642\u5831\u50f9 \u00b6 \u5982\u4e0b\u7a0b\u5f0f\u8aaa\u660e\u5982\u4f55\u5728 \u76e4\u4e2d \u7372\u5f97\u5373\u6642\u5831\u50f9\uff0c\u4e3b\u8981\u900f\u904e DataSubscriber \u4f86\u9032\u884c\u8a02\u95b1\u548c\u9000\u8a02\u3002 DataSubscriber \u4e3b\u8981\u6709\u5206\u6e2c\u8a66\u6a21\u5f0f\u8ddf\u6b63\u5f0f\u6a21\u5f0f\uff0c\u900f\u904e testing \u8b8a\u6578\u4f86\u505a\u5207\u63db\u3002 - testing=True: \u6e2c\u8a66\u6a21\u5f0f - testing=False: \u6b63\u5f0f\u6a21\u5f0f \u6b63\u5f0f\u6a21\u5f0f\u4e2d\uff0c\u53ea\u6709\u5728\u958b\u76e4\u7684\u60c5\u6cc1\u4e0b\u624d\u6709\u8fa6\u6cd5\u63d0\u4f9b\u8cc7\u6599\u3002 \u5728\u7bc4\u4f8b\u7a0b\u5f0f\u4e2d\uff0c\u524d\u90e8\u5206\u4e3b\u8981\u8aaa\u660e\u8a02\u95b1\uff0c\u5f8c\u90e8\u5206\u4e3b\u8981\u8aaa\u660e\u9000\u8a02\u7684\u65b9\u5f0f\u3002 Demo code \u00b6 \u8a02\u95b1\u7bc4\u4f8b from FinMind.data import DataSubscriber from FinMind.data import Stock from FinMind.data import FutureAndOption ds = DataSubscriber ( testing = True ) # \u8a02\u95b1 2330 \u80a1\u7968 Tick \u8cc7\u6599 ds . subscribe ( \"2330\" , Stock . Tick ) # \u8a02\u95b1 2330 \u80a1\u7968\u4e94\u6a94\u5831\u50f9\u8cc7\u6599 ds . subscribe ( \"2330\" , Stock . BidAsk ) # \u8a02\u95b1 TXFF1 \u671f\u6b0a\u5831\u50f9 ds . subscribe ( \"TXFF1\" , FutureAndOption . Tick ) \u8a02\u95b1\u7bc4\u4f8b + \u81ea\u5b9a callback # \u81ea\u5b9a\u56de\u8abf\u51fd\u6578 def cb ( message ): stock_id = message . get ( \"stock_id\" , \"\" ) deal_price = message . get ( \"deal_price\" , \"\" ) volume = message . get ( \"volume\" , \"\" ) time = message . get ( \"Time\" , \"\" ) tick_type = message . get ( \"TickType\" , \"\" ) print ( f \"stock_id: { stock_id } , deal_price: { deal_price } , volume: { volume } , time: { time } , tick_type: { tick_type } \" ) # \u8a02\u95b1 2330 \u80a1\u7968 Tick \u8cc7\u6599\uff0c\u4f7f\u7528\u5ba2\u88fd\u5316\u56de\u8abf\u51fd\u6578 ds . subscribe ( \"2330\" , Stock . Tick , cb ) # \u8a02\u95b1 2330 \u80a1\u7968\u4e94\u6a94\u5831\u50f9\u8cc7\u6599\uff0c\u4f7f\u7528\u5ba2\u88fd\u5316\u56de\u8abf\u51fd\u6578 ds . subscribe ( \"2330\" , Stock . BidAsk , cb ) # \u8a02\u95b1 TXFF1 \u671f\u6b0a\u5831\u50f9\uff0c\u4f7f\u7528\u5ba2\u88fd\u5316\u56de\u8abf\u51fd\u6578 ds . subscribe ( \"TXFF1\" , FutureAndOption . Tick , cb ) \u9000\u8a02\u7bc4\u4f8b # \u53d6\u6d88 2330 \u80a1\u7968 Tick \u8cc7\u6599\u8a02\u95b1 ds . unsubscribe ( \"2330\" , Stock . Tick ) # \u95dc\u9589\u6240\u6709\u8a02\u95b1 ds . close ()","title":"\u5373\u6642\u5831\u50f9"},{"location":"tutor/analysis/RealTimeDataSubscription/#_1","text":"\u5982\u4e0b\u7a0b\u5f0f\u8aaa\u660e\u5982\u4f55\u5728 \u76e4\u4e2d \u7372\u5f97\u5373\u6642\u5831\u50f9\uff0c\u4e3b\u8981\u900f\u904e DataSubscriber \u4f86\u9032\u884c\u8a02\u95b1\u548c\u9000\u8a02\u3002 DataSubscriber \u4e3b\u8981\u6709\u5206\u6e2c\u8a66\u6a21\u5f0f\u8ddf\u6b63\u5f0f\u6a21\u5f0f\uff0c\u900f\u904e testing \u8b8a\u6578\u4f86\u505a\u5207\u63db\u3002 - testing=True: \u6e2c\u8a66\u6a21\u5f0f - testing=False: \u6b63\u5f0f\u6a21\u5f0f \u6b63\u5f0f\u6a21\u5f0f\u4e2d\uff0c\u53ea\u6709\u5728\u958b\u76e4\u7684\u60c5\u6cc1\u4e0b\u624d\u6709\u8fa6\u6cd5\u63d0\u4f9b\u8cc7\u6599\u3002 \u5728\u7bc4\u4f8b\u7a0b\u5f0f\u4e2d\uff0c\u524d\u90e8\u5206\u4e3b\u8981\u8aaa\u660e\u8a02\u95b1\uff0c\u5f8c\u90e8\u5206\u4e3b\u8981\u8aaa\u660e\u9000\u8a02\u7684\u65b9\u5f0f\u3002","title":"\u5373\u6642\u5831\u50f9"},{"location":"tutor/analysis/RealTimeDataSubscription/#demo-code","text":"\u8a02\u95b1\u7bc4\u4f8b from FinMind.data import DataSubscriber from FinMind.data import Stock from FinMind.data import FutureAndOption ds = DataSubscriber ( testing = True ) # \u8a02\u95b1 2330 \u80a1\u7968 Tick \u8cc7\u6599 ds . subscribe ( \"2330\" , Stock . Tick ) # \u8a02\u95b1 2330 \u80a1\u7968\u4e94\u6a94\u5831\u50f9\u8cc7\u6599 ds . subscribe ( \"2330\" , Stock . BidAsk ) # \u8a02\u95b1 TXFF1 \u671f\u6b0a\u5831\u50f9 ds . subscribe ( \"TXFF1\" , FutureAndOption . Tick ) \u8a02\u95b1\u7bc4\u4f8b + \u81ea\u5b9a callback # \u81ea\u5b9a\u56de\u8abf\u51fd\u6578 def cb ( message ): stock_id = message . get ( \"stock_id\" , \"\" ) deal_price = message . get ( \"deal_price\" , \"\" ) volume = message . get ( \"volume\" , \"\" ) time = message . get ( \"Time\" , \"\" ) tick_type = message . get ( \"TickType\" , \"\" ) print ( f \"stock_id: { stock_id } , deal_price: { deal_price } , volume: { volume } , time: { time } , tick_type: { tick_type } \" ) # \u8a02\u95b1 2330 \u80a1\u7968 Tick \u8cc7\u6599\uff0c\u4f7f\u7528\u5ba2\u88fd\u5316\u56de\u8abf\u51fd\u6578 ds . subscribe ( \"2330\" , Stock . Tick , cb ) # \u8a02\u95b1 2330 \u80a1\u7968\u4e94\u6a94\u5831\u50f9\u8cc7\u6599\uff0c\u4f7f\u7528\u5ba2\u88fd\u5316\u56de\u8abf\u51fd\u6578 ds . subscribe ( \"2330\" , Stock . BidAsk , cb ) # \u8a02\u95b1 TXFF1 \u671f\u6b0a\u5831\u50f9\uff0c\u4f7f\u7528\u5ba2\u88fd\u5316\u56de\u8abf\u51fd\u6578 ds . subscribe ( \"TXFF1\" , FutureAndOption . Tick , cb ) \u9000\u8a02\u7bc4\u4f8b # \u53d6\u6d88 2330 \u80a1\u7968 Tick \u8cc7\u6599\u8a02\u95b1 ds . unsubscribe ( \"2330\" , Stock . Tick ) # \u95dc\u9589\u6240\u6709\u8a02\u95b1 ds . close ()","title":"Demo code"},{"location":"tutor/analysis/SnapshotTreemap/","text":"\u9810\u671f\u57f7\u884c\u7d50\u679c\u5982\u4e0b\uff08\u5df2\u5728\u7a0b\u5f0f\u78bc 136 \u884c\uff0c\u52a0\u5165 html \u8a9e\u6cd5\uff0c\u7db2\u9801\u6703\u81ea\u52d5\u66f4\u65b0\uff09 \u00b6 \u5982\u4f55\u57f7\u884c\uff1f \u00b6 \u5c07\u4ee5\u4e0b\u7a0b\u5f0f\u78bc\uff0c\u5132\u5b58\u70ba\uff0c FinMind - \u53f0\u80a1\u200a-\u200a\u5373\u6642\u8cc7\u8a0a X \u7248\u584a\u5716 X flask.py \u57f7\u884c\u4ee5\u4e0b\u6307\u4ee4 FINMIND_API_TOKEN = your_token python FinMind - \u53f0\u80a1 \u200a - \u200a \u5373\u6642\u8cc7\u8a0a X \u7248\u584a\u5716 X flask . py \u7a0b\u5f0f\u78bc\u5982\u4e0b \u00b6 REF \u00b6 medium","title":"\u5373\u6642\u80a1\u5e02\u8cc7\u8a0a X \u677f\u584a\u5716"},{"location":"tutor/analysis/SnapshotTreemap/#136-html","text":"","title":"\u9810\u671f\u57f7\u884c\u7d50\u679c\u5982\u4e0b\uff08\u5df2\u5728\u7a0b\u5f0f\u78bc 136 \u884c\uff0c\u52a0\u5165 html \u8a9e\u6cd5\uff0c\u7db2\u9801\u6703\u81ea\u52d5\u66f4\u65b0\uff09"},{"location":"tutor/analysis/SnapshotTreemap/#_1","text":"\u5c07\u4ee5\u4e0b\u7a0b\u5f0f\u78bc\uff0c\u5132\u5b58\u70ba\uff0c FinMind - \u53f0\u80a1\u200a-\u200a\u5373\u6642\u8cc7\u8a0a X \u7248\u584a\u5716 X flask.py \u57f7\u884c\u4ee5\u4e0b\u6307\u4ee4 FINMIND_API_TOKEN = your_token python FinMind - \u53f0\u80a1 \u200a - \u200a \u5373\u6642\u8cc7\u8a0a X \u7248\u584a\u5716 X flask . py","title":"\u5982\u4f55\u57f7\u884c\uff1f"},{"location":"tutor/analysis/SnapshotTreemap/#_2","text":"","title":"\u7a0b\u5f0f\u78bc\u5982\u4e0b"},{"location":"tutor/analysis/SnapshotTreemap/#ref","text":"medium","title":"REF"}]}
\ No newline at end of file
diff --git a/sitemap.xml b/sitemap.xml
index 4e5fe0d..ba12418 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -2,192 +2,192 @@
https://finmind.github.io/FinMindDoc/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/Contact/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/Donate/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/License/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/StressTest/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/WhatIsNew/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/api_usage_count/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/login/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/quickstart/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/release/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/ExchangeRate/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/GovernmentBondsYield/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/InterestRate/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/Macroeconomy/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/Materials/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/Others/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/EuropeMarket/DataList/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/EuropeMarket/Technical/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/JapanMarket/DataList/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/JapanMarket/Technical/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/TaiwanMarket/Chip/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/TaiwanMarket/ConvertibleBond/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/TaiwanMarket/DataList/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/TaiwanMarket/Derivative/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/TaiwanMarket/Fundamental/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/TaiwanMarket/Others/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/TaiwanMarket/RealTime/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/TaiwanMarket/Technical/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/UnitedKingdomMarket/DataList/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/UnitedKingdomMarket/Technical/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/UnitedStatesMarket/DataList/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/UnitedStatesMarket/Technical/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/analysis/Backtesting/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/analysis/BacktestingOtherData/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/analysis/CustomerDashboardWebServer/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/analysis/Kline/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/analysis/RealTimeDataSubscription/
- 2024-11-30
+ 2024-12-02
daily
https://finmind.github.io/FinMindDoc/tutor/analysis/SnapshotTreemap/
- 2024-11-30
+ 2024-12-02
daily
\ No newline at end of file
diff --git a/sitemap.xml.gz b/sitemap.xml.gz
index 6459a88..2f895b9 100644
Binary files a/sitemap.xml.gz and b/sitemap.xml.gz differ
diff --git a/tutor/TaiwanMarket/DataList/index.html b/tutor/TaiwanMarket/DataList/index.html
index 25a7b56..e97165c 100644
--- a/tutor/TaiwanMarket/DataList/index.html
+++ b/tutor/TaiwanMarket/DataList/index.html
@@ -1101,7 +1101,7 @@
總覽
- 在台灣金融市場，我們擁有 61 種資料集，如下:
+ 在台灣金融市場，我們擁有 62 種資料集，如下:
技術面 Technical
+
+
+