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get_history.py
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get_history.py
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from datetime import datetime, timedelta, date
import numpy as np
import requests, json
import pandas as pd
# 13年(民99-民112)台股資料需時9小時，
# 若讀者未來想加速: 可改採用cuDF，目前cuDF只支援Linux, Windows用戶可使用WSL開再安裝cuDF。
# 印出本程式介紹
def Intro():
print("*********************************************************************************************")
print("| TW-stock v1.0 (Dec.24,2023) |")
print("| 作者: k66(Lana Chen) |")
print("| 描述: 根據台股代碼 (k66提供的twse_stocks_id.h5)從證交所(twse)下載台灣上市股8檔資料。 |")
print("| |")
print("| Log: |")
print("| v1.0: 共1022支上市股，產業共32類。(Dec.24,2023) |")
print("| |")
print("| 贊助我Buy me a coffee: |")
print("| 贊助連結: https://www.buymeacoffee.com/k66inthesky |")
print("| 為感謝支持者，我會額外提供2支程式: |")
print("| backtest.py(回測程式，且能完美銜接此程式)及get_ids.py(能因應新上市股)。 |")
print("*********************************************************************************************")
print()
print()
# 前處理: 日期資料
def PreProcess():
# TODO: 請依需求自行修改日期，2010年以前也適用
start_date = datetime(2024, 1, 1)
end_date = datetime(2024, 2, 1)# datetime(2024, 2, 1)
# Initialize the list to store dates
weekdays = []
# Iterate over the date range
current_date = start_date
while current_date <= end_date:
# Check if the current date is a weekday (Monday=0, Sunday=6)
if current_date.weekday() < 5: # Monday to Friday
# Add the date to the list in the specified format
weekdays.append(current_date.strftime("%Y%m%d"))
# Move to the next day
current_date += timedelta(days=1)
# print(weekdays[-10:],len(weekdays)) # Display the first 10 dates for verification
# 證交所按月抓取股票資料
df=pd.read_hdf('twse_stocks_id.h5',mode="r",index=False)
print('產業共有幾類: ', df['industry'].nunique())
unique_industry_categories = df['industry'].unique()
# print(unique_industry_categories) # 將所有產業別印出
df = df.sort_values(by='industry') # 讀來的時候按理已經按產業排序，此處多做一次防呆
sorted_df=df.sort_values(by=['industry'])
# 因為現今證交所歷史資料從民99年1月4日開始，https://www.twse.com.tw/zh/trading/historical/stock-day.html
# 市場開休市日期查詢:https://www.twse.com.tw/pcversion/zh/holidaySchedule/holidaySchedule
# 證交所公布之補行交易日
# TODO: 提醒! 隨著日期區間更動，記得也要更動comtradedays及holidays!
comtradedays=[]
holidays=['20230102']
# 證交所公布之休市日
'''
comtradedays=[
'20170110','20180117','20170606'
]
holidays=[
'20100101','20100211','20100212','20100215','20100216','20100217','20100218','20100219','20100405','20100616','20100922',
'20110103','20110131','20110201','20110202','20110203','20110204','20110207','20110228','20110404','20110405','20110502','20110606','20110912','20111010',
'20120102','20120119','20120120','20120123','20120124','20120125','20120126','20120127','20120227','20120228','20120404','20120501','20120930','20121010','20121231',
'20130101','20130207','20130208','20130211','20130212','20130213','20130214','20130215','20130228','20130404','20130405','20130501','20130612','20130919','20130920','20131010',
'20140101','20140128','20140129','20140130','20140131','20140203','20140204','20140228','20140404','20140501','20140602','20140908','20141010',
'20150101','20150102','20150216','20150217','20150218','20150219','20150220','20150223','20150224','20150227','20150403','20150406','20150501','20150619','20150928','20151009',
'20160101','20160104','20160204','20160205','20160208','20160209','20160210','20160211','20160212','20160215','20160229','20160404','20160405','20160502','20160609','20160915','20160916','20161010',
'20170102','20170103','20170125','20170126','20170127','20170130','20170131','20170201','20170202','20170227','20170228','20170403','20170404','20170501','20170529','20170530','20171004','20171009','20171010',
'20180101','20180213','20180214','20180215','20180216','20180219','20180220','20180221','20180228','20180404','20180405','20180406','20180501','20180618','20180924','20181010','20181231',
'20190101','20190102','20190130','20190204','20190205','20190206','20190207','20190208','20190211','20190228','20190301','20190404','20190405','20190501','20190607','20190913','20191010','20191011',
'20200101','20200102','20200121','20200122','20200123','20200124','20200127','20200128','20200129','20200228','20200402','20200403','20200501','20200625','20200626','20201001','20201002','20201009',
'20210101','20210208','20210209','20210210','20210211','20210212','20210215','20210216','20210217','20210301','20210402','20210405','20210430','20210614','20210920','20210921','20211011','20211231',
'20220127','20220128','20220131','20220201','20220202','20220203','20220204','20220228','20220404','20220405','20220502','20220603','20220909','20221010',
'20230102','20230103','20230118','20230119','20230120','20230123','20230124','20230125','20230126','20230127','20230227','20230228','20230403','20230404','20230501','20230622','20230623','20230929','20231009','20231010'
]
'''
# marketdays = weekdays & !holidays
# 工作日 = 一般平日+補交易日
# 交易日 = 工作日和非假日的交集
weekdays = weekdays+comtradedays
marketdays = RemoveCommonElements(weekdays,holidays)
# 日期，每一單位為一個月
# TODO: request以下url，請依需求自行修改日期
# months = pd.date_range('2010-01-01','2023-12-31', freq='MS').strftime("%Y%m%d").tolist()
months = pd.date_range(start_date,end_date, freq='MS').strftime("%Y%m%d").tolist()
return df,sorted_df, months
# 前處理用到的演算法，利用日期的嚴格遞增特性優化。
def RemoveCommonElements(list1,list2):
# 從List1從刪與list2共同元素，利用list1,list2已知的日期屬於嚴格遞增特性。
# marketdays = weekdays & !holidays
# 優化後，時間複雜度由原本O(MN)變成O(M+N)。
# M: 從自行設定的開始日至截止日，共M天，N: 共N隻上市股(根據k66提供之twse_stocks_id.h5)。
p1,p2,res=0,0,[]#pointer1,pointer2,result
while p1 < len(list1) and p2 < len(list2):
if list1[p1]<list2[p2]:
res.append(list1[p1])
p1+=1
elif list1[p1]>list2[p2]:
p2+=1
else:
p1+=1
p2+=1
if p1<len(list1):
res.extend(list1[p1:])
return res
def Crawl(df,sorted_df,months):
# 下載所有台股上市股
# for i in range(len(sorted_df)):
for i in range(100): # Demo抓前100支個股就好
# 每一隻股票皆獨立存為一檔案
id = sorted_df['stocks_id'][i]
print('開始爬蟲台股，代碼: ',id)
print('進度: ', i/100*100, "%(",i,"/",100,")")
# print('進度: ', i/len(sorted_df)*100, "%(",i,"/",len(sorted_df),")")
# 下載證交所資料時，RESTful參數日期單位為一個月，故此處才用月，但其實行為會抓整個月內的日k。
df=pd.DataFrame()
for month in months:
try:
# TODO: 1.改為request(selenium)抓各月csv檔，2.try except 3.存至專屬資料夾 4.根據證券代碼整理這些csv檔
url='https://www.twse.com.tw/exchangeReport/STOCK_DAY?response=json&date='+month+'&stockNo='+id
html = requests.get(url)
content = json.loads(html.text)
stock_data = content['data']
col_name = content['fields']
df_onemonth = pd.DataFrame(data=stock_data, columns=col_name)
df = pd.concat([df,df_onemonth], ignore_index = True)
except:
print("The stock in this month has no data: "+month,". Please change the date range.")
# TODO: [使用者須留意] 若只做一個月，某些個股(如代號1435的2023/1月因無資料)會跳except，只要略過此範圍即可。
df.columns = ['日期', '成交股數', '成交金額', '開盤價', '最高價', '最低價', '收盤價', '漲跌價差', '成交筆數']
# 把日期改為西元
for row in range(df.shape[0]):
l = df.iloc[row,0].split('/')
df.iloc[row, 0] = date( int(l[0].lstrip())+1911, int(l[1]), int(l[2]) )
# 把股數或價格等帶有逗號的string轉換成float
for col in [1, 2, 3, 4, 5, 6, 8]: #第0行是日期所以不用轉換
for row in range(df.shape[0]):
tmp=df.iloc[row,col].replace(',', '')
try:
df.iloc[row, col] = float(tmp) # 證交所將缺用--表示
except ValueError:
#證交所將缺用--表示，故我們將其改為NaN
df.iloc[row, col] = np.nan
print(tmp)
# print(df[-10:-5]) # 螢幕上印出其中5筆，如不想要請註解之。
df.to_csv("stocks/"+id+".csv",mode="w",index=False, encoding="UTF-8")
# 此資料不建議存成hd5，此資料存成csv反而有優勢
# df.to_hdf("stocks/"+id+".h5",mode="w",format = 'fixed', key='df',index=False, encoding="UTF-8")
print('[完成]台股代碼: ',id)
print('[完成]下載所有台股歷年資料')
# 聯絡&贊助資訊
def Contact():
print("*********************************************************************************************")
print("| Hi我是k66(Lana), 感謝您使用TW-stock v1.0，為支持我後續更多創作，希望獲得您的寶貴贊助! |")
print("| |")
print("| |")
print("| 贊助我Buy me a coffee: |")
print("| 贊助連結: https://www.buymeacoffee.com/k66inthesky/e/216935 |")
print("| 為感謝支持者，我會額外提供2支程式: |")
print("| backtest.py(回測程式，且能完美銜接此程式)及get_ids.py(能因應新上市股)。 |")
print("| |")
print("| |")
print("| 若使用上遇到問題，歡迎Github開issue聯繫我: |")
print("| https://github.com/k66inthesky/TW-stock/issues |")
print("*********************************************************************************************")
print()
print()
def main():
Intro()
df,sorted_df,months = PreProcess()
print("前處理完成!")
Crawl(df,sorted_df,months)
print("整隻程式運行完成!")
Contact()
if __name__ == '__main__':
main()