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):
    for stock in stocks:
        print(stock)
        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']):
                if thd.new_df.iloc[-2, 3] > thd.new_df.iloc[-1, 3]: #左高，下降
                    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))
        print(stock)
        print("**************")

        # new_df.to_csv('new_df.csv')

        #return new_df

stk = locals()
engine = locals()

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)

for m in range(0, len(stocks), 50):
    engine[m] = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/qbh_hlfx?charset=utf8', pool_recycle=3600)
    threading.Thread(target=qbh, args=(stocks[m:m + 50], )).start()