汇总或更新历史财报数据

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from pytdx.crawler.history_financial_crawler import HistoryFinancialCrawler,HistoryFinancialListCrawler
from pytdx.hq import TdxHq_API
from pytdx.reader import HistoryFinancialReader
from pytdx.crawler.base_crawler import demo_reporthook

import pandas as pd
from datetime import date, datetime, timedelta
import os,re,random
from chinese_calendar import is_workday

api = TdxHq_API(multithread=True, heartbeat=True, auto_retry=True)

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def updateallfns():
    '''更新汇总的财务报表数据''' 
    # 汇总的财报文件中的最新日期
    file = 'D:\\stock_data\\allfns.csv'
    allfns = pd.read_csv(file,index_col='date',dtype={'code':str},parse_dates=['date'])
    newdate_allfns = max(allfns.index)
    # 获取已经下载的最新的文件的财报日期
    fnsfold = r"D:\stock_data\fdata"
    localfnsfiles  = [i for i in os.listdir(fnsfold) if re.search('\.zip$',i)]
    fnsfilesdates = [re.sub(r'\D','',i) for i in os.listdir(fnsfold) if re.search('\.zip$',i)]
    newdate_fnsfiles = pd.to_datetime(max(fnsfilesdates))
    # 判断是否需要更新allfns文件
    updatefiles = [i for i in localfnsfiles if re.findall(r'\d+',i)[0] > f"{newdate_allfns:%Y%m%d}"]
    if not updatefiles:
        pass
    else:
        for filename in updatefiles:
            zipfile = os.path.join(fnsfold,filename)
            cols = ['report_date', 'col1', 'col4',  'col6']
            single_fns = HistoryFinancialReader().get_df(zipfile).filter(cols)
            single_fns.reset_index().to_csv(file,mode='a',header=0,index=False,encoding='utf-8-sig')
            
            
def total_fnsfile():
    '''汇总所有的财报数据文件'''
    # 列出已经下载的所有财报数据文件,2013年后的
    fnsfold = r"D:\stock_data\fdata"
    filenames = [i for i in os.listdir(fnsfold) if re.search(r'\.zip$',i) and (re.findall('\d+',i)[0]>'20131231')]
    random.choices(filenames,k=3)
    # 需要筛选的列名称
    cols = ['report_date', 'col1', 'col4',  'col6']
    total_fns = pd.DataFrame()
    for filename in filenames:
        file = os.path.join(fnsfold,filename)
        # 读取历史财报数据文件
        single_fns = HistoryFinancialReader().get_df(file).filter(cols)
        # 合并fns_sigle
        total_fns = pd.concat([total_fns,single_fns])
    # 重新命名列名称
    total_fns.columns = ["date", "EPS", "BPS", "ROE"]
    # 输出到csv文件
    total_fns.to_csv(os.path.join("D:\stock_data",'allfns.csv'),encoding='utf-8-sig')
    
if __name__ == '__main__':
    file = 'D:\\stock_data\\allfns.csv'
    if os.path.exists(file):
        updateallfns()
    else:
        total_fnsfile()