stock/qbh.py

from jqdatasdk import *
auth('18019403367', 'Qwer4321')
import pandas as pd
import pymysql
from sqlalchemy import create_engine
import threading



# 连接数据库
# db_stk_sql = pymysql.connect(host='localhost',
#                              user='root',
#                              port=3307,
#                              password='r6kEwqWU9!v3',
#                              database='stocks',
#                              connect_timeout=600)
#
#
# db_qbh = pymysql.connect(host='localhost',
#                          user='root',
#                          port=3307,
#                          password='r6kEwqWU9!v3',
#                          database='qbh',
#                          charset='utf8')
#
#
# cursor = db_qbh.cursor()
# engine = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/qbh_hlfx?charset=utf8')
engine2 = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/stocks?charset=utf8')

stocks = list(get_all_securities(['stock'], date='2021-12-31').index)
stocks =stocks[0:40]

thd = threading.local()


def qbh(stocks, engine, engine_backup):
    for stock in stocks:
        print(engine)
        print(engine_backup)
        # thd.new_df = pd.DataFrame(columns=('date', 'open', 'close', 'high', 'low', 'volume', 'money', 'HL'))
        # # print(new_df.head())
        # thd.df_day = stk['stk' + stock[:6]]
        # for i in thd.df_day.index:
        #     if i == 0 or i == 1:
        #         thd.new_df = pd.concat([thd.new_df, thd.df_day.iloc[[i]]], ignore_index=True)
        #     # 不包含
        #     elif (thd.new_df.iloc[-1, 3] > thd.df_day.loc[i, 'high']
        #           and thd.new_df.iloc[-1, 4] > thd.df_day.loc[i, 'low']) \
        #             or (thd.new_df.iloc[-1, 3] < thd.df_day.loc[i, 'high']
        #                 and thd.new_df.iloc[-1, 4] < thd.df_day.loc[i, 'low']):
        #         thd.new_df = pd.concat([thd.new_df, thd.df_day.iloc[[i]]], ignore_index=True)
        #     # 包含
        #     else:
        #         # (new_df.iloc[-1,3]>=df_day.loc[i,'high'] and new_df.iloc[-1,4]<= df_day.loc[i,'low']):
        #         # 左高，下降
        #         if thd.new_df.iloc[-2, 3] > thd.new_df.iloc[-1, 3]:
        #             thd.new_df.iloc[-1, 3] = min(thd.new_df.iloc[-1, 3], thd.df_day.loc[i, 'high'])
        #             thd.new_df.iloc[-1, 4] = min(thd.new_df.iloc[-1, 4], thd.df_day.loc[i, 'low'])
        #         else:
        #             # 右高，上升
        #             thd.new_df.iloc[-1, 3] = max(thd.new_df.iloc[-1, 3], thd.df_day.loc[i, 'high'])
        #             thd.new_df.iloc[-1, 4] = max(thd.new_df.iloc[-1, 4], thd.df_day.loc[i, 'low'])
        # thd.new_df.to_sql('stk%s_%s' % (stock[:6], u), con=engine, index=True, if_exists='replace')
        # with engine.connect() as con:
        #     con.execute('ALTER TABLE stk%s_%s ADD PRIMARY KEY (`date`);' % (stock[:6], u))
        # thd.new_df.to_sql('stk%s_%s' % (stock[:6], u), con=engine_backup, index=True, if_exists='replace')
        # with engine_backup.connect() as con_backup:
        #     con_backup.execute('ALTER TABLE stk%s_%s ADD PRIMARY KEY (`date`);' % (stock[:6], u))
        # thd.new_df.to_csv(
        #     '/Users/daniel/Library/CloudStorage/OneDrive-个人/个人/python_stocks/20220211qbh/qbh%s.csv' % stock[:6])
        # print(stock)
        # print("**************")
        #
        # # new_df.to_csv('new_df.csv')
        #
        # #return new_df


stk = locals()
engine = []
engine_backup = []

u = '1d'
# 获取数据存入DataFrame

for stock in stocks:
    stk['stk'+stock[:6]] = pd.read_sql_query('select date,open,close,high,low,volume,money from stk%s_%s' % (stock[:6], u), engine2)
    # print(stock, stk['stk'+stock[:6]])
print("###############################################################################################################"
      "###############################################################################################################"
      "###############################################################################################################"
      "###############################################################################################################"
      "###############################################################################################################"
      "###############################################################################################################"
      "###############################################################################################################")

# 开始去包含
# qbh(stocks)
step = 100
for m in range(0, len(stocks), step):
    engine.append(create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/qbh_hlfx?charset=utf8', pool_recycle= 3600))
    engine_backup.append(create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/qbh_hlfx_backup?charset=utf8', pool_recycle= 3600))
    threading.Thread(target=qbh, args=(stocks[m:m + step], engine[m], engine_backup[m])).start()