stock/QMT/test_fundamentals.py

import os

import pandas as pd
import xtquant.xtdata
from xtquant.xttrader import XtQuantTrader, XtQuantTraderCallback
from xtquant.xttype import StockAccount
from xtquant import xtdata, xtconstant
from functools import reduce
from datetime import datetime as dt
import pymysql
import math
import multiprocessing as mp
import traceback


db_pool = pymysql.connect(host='localhost',
                          user='root',
                          port=3307,
                          password='r6kEwqWU9!v3',
                          database='hlfx_pool')
cursor_pool = db_pool.cursor()

def err_call_back(err):
    print(f'问题在这里~ error：{str(err)}')
    traceback.print_exc()

def to_sql(stocks, sector_list):
    print(f'my pid is {os.getpid()}')
    for stock in stocks:
        sec_lt = []
        for i in sector_list:
            sector = xtdata.get_stock_list_in_sector(i)
            if stock in sector:
                sec_lt.append(i)
        print(f'{stock}属于：')
        print(f'{sec_lt}板块')
        results_list = ','.join(set(sec_lt))
        print(results_list)
        sql = "INSERT INTO sector_data (stock_code, sector) VALUES('%s', '%s')" \
              % (stock, results_list)
        cursor_pool.execute(sql)
        db_pool.commit()

if __name__ == '__main__':
    sttime = dt.now()
    stock_list = xtdata.get_stock_list_in_sector('沪深A股')
    sector_list = xtdata.get_sector_list()
    step = math.ceil(len(stock_list) / mp.cpu_count())
    pool = mp.Pool(processes=mp.cpu_count())
    for i in range(0, len(stock_list), step):
        pool.apply_async(func=to_sql, args=(stock_list[i:i+step], sector_list,), error_callback=err_call_back)
    pool.close()
    pool.join()

    print(dt.now()-sttime)



# exit()

# pd.set_option('display.unicode.ambiguous_as_wide', True)
# pd.set_option('display.unicode.east_asian_width', True)
# pd.set_option('display.width', 1000)
#
# stocks = xtdata.get_stock_list_in_sector('沪深A股')
# # xtdata.download_financial_data(stocks, ['Balance', 'Income', 'CashFlow'])
# stocks.sort()
# funda = xtdata.get_financial_data(stocks[0:10], ['Balance', 'Income', 'CashFlow'], start_time='20220101', end_time='20230220')
# for stock in stocks[0:10]:
#     bal = funda[stock]['Balance'][['m_timetag', 'goodwill']]
#     profit = funda[stock]['Income'][['m_timetag', 'tot_profit', 'net_profit_incl_min_int_inc_after', 's_fa_eps_basic']]
#     cflow = funda[stock]['CashFlow'][['m_timetag', 'net_profit']]
#     dfs = [bal, profit, cflow]
#     df = reduce(lambda x, y: pd.merge(x, y, on='m_timetag', how='inner'), dfs)
#     df.columns = ['披露时间', '商誉', '利润总额', '净利润', '每股收益', '净利润']
#
#     print(stock, '\n', df)