stock/z_jq_his/updata_qbh_hlfx_1114.py

import multiprocessing as mp
import pandas as pd
import pymysql
from sqlalchemy import create_engine
from datetime import datetime as dt


import datetime

# auth('18616891214', 'Ea?*7f68nD.dafcW34d!')
def hlfx(stocks,fre,table_list):
    engine = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/stocks?charset=utf8')
    engine2 = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/hlfx?charset=utf8')
    for stock in stocks:
        # print(stock)
        if ('stk%s_%s' % (stock, fre)) in table_list:
            # 有历史数据
            index_len = pd.read_sql_table('stk%s_%s' % (stock, fre), con=engine2).iloc[-1, 0]

            # 注意修改time delta
            startdate = pd.read_sql_table('stk%s_%s' % (stock, fre), con=engine2).iloc[-1, 1]
            # startdate = pd.read_sql_table('stk%s_%s' % (stock, fre), con=engine2).iloc[-1, 1] + datetime.timedelta(minutes= 5)
            get_price = pd.read_sql_query(
                'select date,open,close,high,low,volume,money from `stk%s_%s`' % (stock, fre), engine)
            get_price = get_price.loc[get_price['date'] > startdate]
            df_day = pd.read_sql_query(
                'select date,open,close,high,low,volume,money,HL from `stk%s_%s`' % (stock, fre), engine2)
            if index_len > 2:
                # 先处理去包含
                for i in get_price.index:
                    # 不包含
                    if (df_day.iloc[-1, 3] > get_price.loc[i, 'high']
                        and df_day.iloc[-1, 4] > get_price.loc[i, 'low']) \
                            or (df_day.iloc[-1, 3] < get_price.loc[i, 'high']
                                and df_day.iloc[-1, 4] < get_price.loc[i, 'low']):
                        df_day = pd.concat([df_day, get_price.loc[[i]]], ignore_index=True)
                        # print(df_day)
                    # 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 df_day.iloc[-2, 3] > df_day.iloc[-1, 3]:
                            df_day.iloc[-1, 3] = min(df_day.iloc[-1, 3], get_price.loc[i, 'high'])
                            df_day.iloc[-1, 4] = min(df_day.iloc[-1, 4], get_price.loc[i, 'low'])
                        else:
                            # 右高，上升
                            df_day.iloc[-1, 3] = max(df_day.iloc[-1, 3], get_price.loc[i, 'high'])
                            df_day.iloc[-1, 4] = max(df_day.iloc[-1, 4], get_price.loc[i, 'low'])
                            # 寻找顶底分型
                if len(df_day.index) > 2:
                    # 寻找顶底分型
                    for x in range(index_len, len(df_day.index)):
                        m = x - 1
                        # 底
                        if ((df_day.loc[x, 'high'] > df_day.loc[x - 1, 'high']) and (
                                df_day.loc[x - 2, 'high'] > df_day.loc[x - 1, 'high'])):
                            # if ((stk.df_day.loc[i-2, 'date'] != stk.fxdf.iloc[-1,0]) and (stk.df_day.loc[i-3,'date'] != stk.fxdf.iloc[-1,0]) and (stk.df_day.loc[i-1,'date'] != stk.fxdf.iloc[-1,0])):
                            # stk.fxdf = pd.concat([stk.fxdf, stk.df_day.iloc[[i]]], ignore_index=True)
                            df_day.loc[x, 'HL'] = 'L*'
                            while m:
                                if df_day.loc[m, 'HL'] == 'H':
                                    if (x - m) > 3:
                                        df_day.loc[x, 'HL'] = 'L'
                                        if x == len(df_day.index) - 1:
                                            # print(stock, '$$$$$$$', '\n', df_day.loc[x, 'date'], '买买买买买！！')
                                            pass
                                    break
                                elif (df_day.loc[m, 'HL'] == 'L'):
                                    if df_day.loc[x - 1, 'low'] < df_day.loc[m - 1, 'low']:
                                        # 前一个为底，且中间存在不包含 or 更低的底
                                        df_day.loc[x, 'HL'] = 'L'
                                        if x == len(df_day.index) - 1:
                                            pass
                                            # print(stock, '$$$$$$$', '\n', df_day.loc[x, 'date'],
                                            #       '中继后的底————买吗？！')
                                        break
                                    else:
                                        break
                                m = m - 1
                                if m == 0:
                                    df_day.loc[x, 'HL'] = 'L'
                        # 顶
                        elif ((df_day.loc[x, 'high'] < df_day.loc[x - 1, 'high']) and (
                                df_day.loc[x - 2, 'high'] < df_day.loc[x - 1, 'high'])):
                            # if ((stk.df_day.loc[i-2, 'date'] != stk.fxdf.iloc[-1,0]) and (stk.df_day.loc[i-3,'date'] != stk.fxdf.iloc[-1,0]) and (stk.df_day.loc[i-1,'date'] != stk.fxdf.iloc[-1,0])):
                            #     stk.fxdf = pd.concat([stk.fxdf, stk.df_day.iloc[[i]]], ignore_index=True)
                            df_day.loc[x, 'HL'] = 'H*'
                            while m:
                                if df_day.loc[m, 'HL'] == 'L':
                                    if x - m > 3:
                                        df_day.loc[x, 'HL'] = 'H'
                                        if x == len(df_day.index) - 1:
                                            # print(stock, '!!!!!!!', '\n', '卖卖卖卖卖卖卖!')
                                            pass
                                    break
                                elif (df_day.loc[m, 'HL'] == 'H'):
                                    if df_day.loc[x - 1, 'high'] > df_day.loc[m - 1, 'high']:
                                        # 前一个为顶，且中间存在不包含 or 更高的顶
                                        df_day.loc[x, 'HL'] = 'H'
                                        if x == len(df_day.index) - 1:
                                            pass
                                            # print(stock, '/\/\/\/\/\/\/', '一顶更有一顶高！')
                                        break
                                    break
                                m = m - 1
                                if m == 0:
                                    df_day.loc[x, 'HL'] = 'H'
                        else:
                            df_day.loc[x, 'HL'] = '-'
                # 更新数据库
                df_day[index_len + 1:].to_sql('stk%s_%s' % (stock, fre), con=engine2, index=True,
                                              if_exists='append')
            else:
                df_day = pd.concat([df_day, get_price], ignore_index=True)
                df_day[index_len + 1:].to_sql('stk%s_%s' % (stock, fre), con=engine2, index=True,
                                              if_exists='append')
        else:
            # 没有历史数据表
            df_day = pd.DataFrame(columns=('date', 'open', 'close', 'high', 'low', 'volume', 'money', 'HL'))
            get_price = pd.read_sql_query(
                'select date,open,close,high,low,volume,money from `stk%s_%s`' % (stock, fre), engine)
            # 先处理去包含
            for i in get_price.index:
                if i == 0 or i == 1:
                    df_day = pd.concat([df_day, get_price.iloc[[i]]], ignore_index=True)
                # 不包含
                elif (df_day.iloc[-1, 3] > get_price.loc[i, 'high']
                      and df_day.iloc[-1, 4] > get_price.loc[i, 'low']) \
                        or (df_day.iloc[-1, 3] < get_price.loc[i, 'high']
                            and df_day.iloc[-1, 4] < get_price.loc[i, 'low']):
                    df_day = pd.concat([df_day, get_price.loc[[i]]], ignore_index=True)
                # 包含
                else:
                    # 左高，下降
                    if df_day.iloc[-2, 3] > df_day.iloc[-1, 3]:
                        df_day.iloc[-1, 3] = min(df_day.iloc[-1, 3], get_price.loc[i, 'high'])
                        df_day.iloc[-1, 4] = min(df_day.iloc[-1, 4], get_price.loc[i, 'low'])
                    else:
                        # 右高，上升
                        df_day.iloc[-1, 3] = max(df_day.iloc[-1, 3], get_price.loc[i, 'high'])
                        df_day.iloc[-1, 4] = max(df_day.iloc[-1, 4], get_price.loc[i, 'low'])
            if len(df_day.index) > 2:
                # 寻找顶底分型
                for x in range(2, len(df_day.index)):
                    m = x - 1
                    # 底
                    if ((df_day.loc[x, 'high'] > df_day.loc[x - 1, 'high']) and (
                            df_day.loc[x - 2, 'high'] > df_day.loc[x - 1, 'high'])):
                        # if ((stk.df_day.loc[i-2, 'date'] != stk.fxdf.iloc[-1,0]) and (stk.df_day.loc[i-3,'date'] != stk.fxdf.iloc[-1,0]) and (stk.df_day.loc[i-1,'date'] != stk.fxdf.iloc[-1,0])):
                        # stk.fxdf = pd.concat([stk.fxdf, stk.df_day.iloc[[i]]], ignore_index=True)
                        df_day.loc[x, 'HL'] = 'L*'
                        while m:
                            if df_day.loc[m, 'HL'] == 'H':
                                if (x - m) > 3:
                                    df_day.loc[x, 'HL'] = 'L'
                                    if x == len(df_day.index) - 1:
                                        pass
                                        # print(stock, '$$$$$$$', '\n', df_day.loc[x, 'date'], '买买买买买！！')
                                break
                            elif (df_day.loc[m, 'HL'] == 'L'):
                                if df_day.loc[x - 1, 'low'] < df_day.loc[m - 1, 'low']:
                                    # 前一个为底，且中间存在不包含 or 更低的底
                                    df_day.loc[x, 'HL'] = 'L'
                                    if x == len(df_day.index) - 1:
                                        pass
                                        # print(stock, '$$$$$$$', '\n', df_day.loc[x, 'date'], '中继后的底————买吗？！')
                                    break
                                else:
                                    break
                            m = m - 1
                            if m == 0:
                                df_day.loc[x, 'HL'] = 'L'
                    # 顶
                    elif ((df_day.loc[x, 'high'] < df_day.loc[x - 1, 'high']) and (
                            df_day.loc[x - 2, 'high'] < df_day.loc[x - 1, 'high'])):
                        # if ((stk.df_day.loc[i-2, 'date'] != stk.fxdf.iloc[-1,0]) and (stk.df_day.loc[i-3,'date'] != stk.fxdf.iloc[-1,0]) and (stk.df_day.loc[i-1,'date'] != stk.fxdf.iloc[-1,0])):
                        #     stk.fxdf = pd.concat([stk.fxdf, stk.df_day.iloc[[i]]], ignore_index=True)
                        df_day.loc[x, 'HL'] = 'H*'
                        while m:
                            if df_day.loc[m, 'HL'] == 'L':
                                if x - m > 3:
                                    df_day.loc[x, 'HL'] = 'H'
                                    if x == len(df_day.index) - 1:
                                        # print(stock, '!!!!!!!', '\n', '卖卖卖卖卖卖卖!')
                                        pass
                                break
                            elif (df_day.loc[m, 'HL'] == 'H'):
                                if df_day.loc[x - 1, 'high'] > df_day.loc[m - 1, 'high']:
                                    # 前一个为顶，且中间存在不包含 or 更高的顶
                                    df_day.loc[x, 'HL'] = 'H'
                                    if x == len(df_day.index) - 1:
                                        pass
                                        # print(stock, '/\/\/\/\/\/\/', '一顶更有一顶高！')
                                    break
                                break
                            m = m - 1
                            if m == 0:
                                df_day.loc[x, 'HL'] = 'H'
                    else:
                        df_day.loc[x, 'HL'] = '-'
            # 更新数据库
            df_day.to_sql('stk%s_%s' % (stock, fre), con=engine2, index=True, if_exists='append')


if __name__ == '__main__':
    engine_stocks_list = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/hlfx_pool?charset=utf8')
    # stocks = list(get_all_securities(['stock'], date=dt.today().strftime('%Y-%m-%d')).index)


    stocks = pd.read_sql_query(
        'select securities from stocks_list', engine_stocks_list)
    stocks = stocks.iloc[-1, 0]
    stocks = stocks.split(",")
    print(len(stocks), type(stocks), stocks)
    # stocks = stocks[0:1000]

    start = dt.now()
    # 确定级别
    # 注意修改time delta
    # fre = '30m'

    for fre in ['1d', '30m']:
        start = dt.now()
        print(fre)
        # 连接数据库
        db = pymysql.connect(host='localhost',
                             user='root',
                             port=3307,
                             password='r6kEwqWU9!v3',
                             database='hlfx')
        cursor = db.cursor()
        cursor.execute("show tables like '%%%s%%' " % fre)
        table_list = [tuple[0] for tuple in cursor.fetchall()]
        print('取得 table_list %s' % fre)

        step = 800
        mp_list = []
        print(len(stocks))

        for i in range(0, len(stocks), step):
            p = mp.Process(target=hlfx, args=(stocks[i:i + step], fre, table_list, ))
            mp_list.append(p)
            p.start()

        for processing in mp_list:
            processing.join()
        # db.close()

        end = dt.now()
        print('总时长：', (end - start).seconds)