stock/real_time_update_qbh_hlfx_1025.py

from jqdatasdk import *
import pandas as pd
import pymysql
from sqlalchemy import create_engine
import threading
from datetime import datetime as dt
from jqdatasdk.technical_analysis import *

auth('18616891214', 'Ea?*7f68nD.dafcW34d!')
stocks = list(get_all_securities(['stock'], date=dt.today().strftime('%Y-%m-%d')).index)
# stocks = stocks[0:200]

def hlfx(stocks, engine_stock, engine_hlfx):
    for thd.stock in stocks:
        print(thd.stock)
        if ('stk%s_%s' % (thd.stock, fre)) in table_list:
            # 有历史数据
            index_len = pd.read_sql_table('stk%s_%s' % (thd.stock, fre), con=engine_hlfx).iloc[-1, 0]
            startdate = pd.read_sql_table('stk%s_%s' % (thd.stock, fre), con=engine_hlfx).iloc[-1, 1]

            # thd.get_price = pd.read_sql_query(
            #     'select date,open,close,high,low,volume,money from `stk%s_%s`' % (thd.stock, fre), engine_stock)
            # thd.get_price = thd.get_price.loc[thd.get_price['date'] > startdate]
            thd.get_price = df.loc[thd.stock]

            thd.df_day = pd.read_sql_query(
                'select date,open,close,high,low,volume,money,HL from `stk%s_%s`' % (thd.stock, fre), engine_hlfx)

            # 先处理去包含
            for i in thd.get_price.index:
                # 不包含
                if (thd.df_day.iloc[-1, 3] > thd.get_price.loc[i, 'high']
                    and thd.df_day.iloc[-1, 4] > thd.get_price.loc[i, 'low']) \
                        or (thd.df_day.iloc[-1, 3] < thd.get_price.loc[i, 'high']
                            and thd.df_day.iloc[-1, 4] < thd.get_price.loc[i, 'low']):
                    thd.df_day = pd.concat([thd.df_day, thd.get_price.loc[[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.df_day.iloc[-2, 3] > thd.df_day.iloc[-1, 3]:
                        thd.df_day.iloc[-1, 3] = min(thd.df_day.iloc[-1, 3], thd.get_price.loc[i, 'high'])
                        thd.df_day.iloc[-1, 4] = min(thd.df_day.iloc[-1, 4], thd.get_price.loc[i, 'low'])
                    else:
                        # 右高，上升
                        thd.df_day.iloc[-1, 3] = max(thd.df_day.iloc[-1, 3], thd.get_price.loc[i, 'high'])
                        thd.df_day.iloc[-1, 4] = max(thd.df_day.iloc[-1, 4], thd.get_price.loc[i, 'low'])

            # 寻找顶底分型
            if len(thd.df_day.index) > 2:
                x = len(thd.df_day.index)-1
                m = x - 1
                # 底
                if ((thd.df_day.loc[x, 'high'] > thd.df_day.loc[x - 1, 'high']) and (
                        thd.df_day.loc[x - 2, 'high'] > thd.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)
                    thd.df_day.loc[x, 'HL'] = 'L*'
                    while m:
                        if thd.df_day.loc[m, 'HL'] == 'H':
                            if (x - m) > 3:
                                thd.df_day.loc[x, 'HL'] = 'L'
                                # 此处可以获得MACD指标
                                # pre-macd_dif, pre-macd_dea, pre-macd_macd = MACD(thd.stock,check_date=thd.df_day.loc[m, 'datetime'], SHORT = 12, LONG = 26, MID = 9)
                                    # pass
                                print(thd.stock, '$$$$$$$', '\n', thd.df_day.loc[x, 'date'], '\n',
                                      '笔形成————买买买买买！！')
                                results.append(thd.stock)
                                print('222')
                            # break
                        elif (thd.df_day.loc[m, 'HL'] == 'L'):
                            if thd.df_day.loc[x - 1, 'low'] < thd.df_day.loc[m - 1, 'low']:
                                # 前一个为底，且中间存在不包含 or 更低的底
                                thd.df_day.loc[x, 'HL'] = 'L'
                                x_macd_dif, x_macd_dea, x_macd_macd = MACD(thd.stock,
                                                                           check_date=thd.df_day.loc[x, 'date'],
                                                                           SHORT=12, LONG=26, MID=9, unit=fre)
                                m_macd_dif, m_macd_dea, m_macd_macd = MACD(thd.stock,
                                                                           check_date=thd.df_day.loc[m, 'date'],
                                                                           SHORT=12, LONG=26, MID=9, unit=fre)
                                if x_macd_dif[thd.stock] > m_macd_dif[thd.stock]:
                                    # pass
                                    # print(thd.df_day.loc[m, 'date'], thd.df_day.loc[m, 'low'],
                                    #       m_macd_dif[thd.stock])
                                    # print(thd.df_day.loc[x, 'date'], thd.df_day.loc[x, 'low'],
                                    #       x_macd_dif[thd.stock])
                                    print(thd.stock, '$$$$$$$', '\n', thd.df_day.loc[x, 'date'], 'MACD背驰————买吗？！')
                                    results.append(thd.stock)
                                    print('333')
                                break
                            else:
                                # 底更低但没有背驰
                                break
                        m = m - 1
                        if m == 0:
                            # 第一个底
                            thd.df_day.loc[x, 'HL'] = 'L'
                            results.append(thd.stock)
                            print('444')
                # 顶
                elif ((thd.df_day.loc[x, 'high'] < thd.df_day.loc[x - 1, 'high']) and (
                        thd.df_day.loc[x - 2, 'high'] < thd.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)
                    thd.df_day.loc[x, 'HL'] = 'H*'
                    while m:
                        if thd.df_day.loc[m, 'HL'] == 'L':
                            if x - m > 3:
                                thd.df_day.loc[x, 'HL'] = 'H'
                                print(thd.stock, '!!!!!!!', '\n', thd.df_day.loc[x, 'date'], '笔形成————卖卖卖卖卖卖卖!')
                                # pass
                                results_short.append(thd.stock)
                                if thd.stock in results:
                                    results.remove(thd.stock)
                            # break
                        elif (thd.df_day.loc[m, 'HL'] == 'H'):
                            if thd.df_day.loc[x - 1, 'high'] > thd.df_day.loc[m - 1, 'high']:
                                # 前一个为顶，且中间存在不包含 or 更高的顶
                                thd.df_day.loc[x, 'HL'] = 'H'
                                x_macd_dif, x_macd_dea, x_macd_macd = MACD(thd.stock,
                                                                           check_date=thd.df_day.loc[x, 'date'],
                                                                           SHORT=12, LONG=26, MID=9, unit=fre)
                                m_macd_dif, m_macd_dea, m_macd_macd = MACD(thd.stock,
                                                                           check_date=thd.df_day.loc[m, 'date'],
                                                                           SHORT=12, LONG=26, MID=9, unit=fre)
                                if x_macd_dif[thd.stock] < m_macd_dif[thd.stock]:
                                    # pass
                                    print(thd.stock, '/\/\/\/\/\/\/', '顶背离了！！！！')
                                    results_short.append(thd.stock)
                                    if thd.stock in results:
                                        results.remove(thd.stock)
                                break
                            break
                        m = m - 1
                        if m == 0:
                            thd.df_day.loc[x, 'HL'] = 'H'
                            results_short.append(thd.stock)
                            if thd.stock in results:
                                results.remove(thd.stock)
                else:
                    thd.df_day.loc[x, 'HL'] = '-'

            # 更新数据库
            # 可以使用normalize_code(code) 方法 改变代码格式
            # thd.df_day[index_len + 1:].to_sql('stk%s_%s' % (thd.stock, fre), con=engine_hlfx, index=True, if_exists='append')
        else:
            # 没有历史数据表
            thd.df_day = pd.DataFrame(columns=('date', 'open', 'close', 'high', 'low', 'volume', 'money', 'HL'))
            thd.get_price = pd.read_sql_query(
                'select date,open,close,high,low,volume,money from `stk%s_%s`' % (thd.stock, fre), engine_stock)
            # 先处理去包含
            for i in thd.get_price.index:
                if i == 0 or i == 1:
                    thd.df_day = pd.concat([thd.df_day, thd.get_price.iloc[[i]]], ignore_index=True)
                # 不包含
                elif (thd.df_day.iloc[-1, 3] > thd.get_price.loc[i, 'high']
                      and thd.df_day.iloc[-1, 4] > thd.get_price.loc[i, 'low']) \
                        or (thd.df_day.iloc[-1, 3] < thd.get_price.loc[i, 'high']
                            and thd.df_day.iloc[-1, 4] < thd.get_price.loc[i, 'low']):
                    thd.df_day = pd.concat([thd.df_day, thd.get_price.loc[[i]]], ignore_index=True)
                # 包含
                else:
                    # 左高，下降
                    if thd.df_day.iloc[-2, 3] > thd.df_day.iloc[-1, 3]:
                        thd.df_day.iloc[-1, 3] = min(thd.df_day.iloc[-1, 3], thd.get_price.loc[i, 'high'])
                        thd.df_day.iloc[-1, 4] = min(thd.df_day.iloc[-1, 4], thd.get_price.loc[i, 'low'])
                    else:
                        # 右高，上升
                        thd.df_day.iloc[-1, 3] = max(thd.df_day.iloc[-1, 3], thd.get_price.loc[i, 'high'])
                        thd.df_day.iloc[-1, 4] = max(thd.df_day.iloc[-1, 4], thd.get_price.loc[i, 'low'])
            if len(thd.df_day.index) > 2:
                # 寻找顶底分型
                x = len(thd.df_day.index)-1
                m = x - 1
                # 底
                if ((thd.df_day.loc[x, 'high'] > thd.df_day.loc[x - 1, 'high']) and (
                        thd.df_day.loc[x - 2, 'high'] > thd.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)
                    thd.df_day.loc[x, 'HL'] = 'L*'
                    while m:
                        if thd.df_day.loc[m, 'HL'] == 'H':
                            if (x - m) > 3:
                                thd.df_day.loc[x, 'HL'] = 'L'
                                # 此处可以获得MACD指标
                                # pre-macd_dif, pre-macd_dea, pre-macd_macd = MACD(thd.stock,check_date=thd.df_day.loc[m, 'datetime'], SHORT = 12, LONG = 26, MID = 9)
                                # pass
                                print(thd.stock, '$$$$$$$', '\n', thd.df_day.loc[x, 'date'], '买买买买买！！')
                                results.append(thd.stock)
                            # break
                        elif (thd.df_day.loc[m, 'HL'] == 'L'):
                            if thd.df_day.loc[x - 1, 'low'] < thd.df_day.loc[m - 1, 'low']:
                                # 前一个为底，且中间存在不包含 or 更低的底
                                thd.df_day.loc[x, 'HL'] = 'L'
                                x_macd_dif, x_macd_dea, x_macd_macd = MACD(thd.stock,
                                                                           check_date=thd.df_day.loc[x, 'date'],
                                                                           SHORT=12, LONG=26, MID=9, unit=fre)
                                m_macd_dif, m_macd_dea, m_macd_macd = MACD(thd.stock,
                                                                           check_date=thd.df_day.loc[m, 'date'],
                                                                           SHORT=12, LONG=26, MID=9, unit=fre)
                                if x_macd_dif[thd.stock] > m_macd_dif[thd.stock]:
                                    # pass
                                    # print(thd.df_day.loc[m, 'date'], thd.df_day.loc[m, 'low'],
                                    #       m_macd_dif[thd.stock])
                                    # print(thd.df_day.loc[x, 'date'], thd.df_day.loc[x, 'low'],
                                    #       x_macd_dif[thd.stock])
                                    print(thd.stock, '$$$$$$$', '\n', thd.df_day.loc[x, 'date'],
                                          'MACD背驰————买吗？！')
                                    results.append(thd.stock)

                                break
                            else:
                                break
                        m = m - 1
                        if m == 0:
                            thd.df_day.loc[x, 'HL'] = 'L'
                            results.append(thd.stock)
                # 顶
                elif ((thd.df_day.loc[x, 'high'] < thd.df_day.loc[x - 1, 'high']) and (
                        thd.df_day.loc[x - 2, 'high'] < thd.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)
                    thd.df_day.loc[x, 'HL'] = 'H*'
                    while m:
                        if thd.df_day.loc[m, 'HL'] == 'L':
                            if x - m > 3:
                                thd.df_day.loc[x, 'HL'] = 'H'
                                print(thd.stock, '!!!!!!!', '\n', '卖卖卖卖卖卖卖!')
                                # pass
                                results.remove(thd.stock)
                            # break
                        elif (thd.df_day.loc[m, 'HL'] == 'H'):
                            if thd.df_day.loc[x - 1, 'high'] > thd.df_day.loc[m - 1, 'high']:
                                # 前一个为顶，且中间存在不包含 or 更高的顶
                                thd.df_day.loc[x, 'HL'] = 'H'
                                x_macd_dif, x_macd_dea, x_macd_macd = MACD(thd.stock,
                                                                           check_date=thd.df_day.loc[x, 'date'],
                                                                           SHORT=12, LONG=26, MID=9, unit=fre)
                                m_macd_dif, m_macd_dea, m_macd_macd = MACD(thd.stock,
                                                                           check_date=thd.df_day.loc[m, 'date'],
                                                                           SHORT=12, LONG=26, MID=9, unit=fre)
                                if x_macd_dif[thd.stock] < m_macd_dif[
                                    thd.stock]:
                                    # pass
                                    print(thd.stock, '/\/\/\/\/\/\/', '顶背离了！！！！')
                                    results.remove(thd.stock)
                                break
                            break
                        m = m - 1
                        if m == 0:
                            thd.df_day.loc[x, 'HL'] = 'H'
                            results.remove(thd.stock)
                else:
                    thd.df_day.loc[x, 'HL'] = '-'
            # print(thd.df_day[-20:])
            # 更新数据库
            # thd.df_day.to_sql('stk%s_%s' % (thd.stock, fre), con=engine_hlfx, index=True, if_exists='append')

start = dt.now()
while True:
    now_date = dt.now()
    date_morning_begin = now_date.replace(hour=9, minute=25, second=0)
    date_morning_end = now_date.replace(hour=11, minute=31, second=0)
    date_afternooe_begin = now_date.replace(hour=13, minute=0, second=0)
    date_afternooe_end = now_date.replace(hour=15, minute=0, second=0)
    # print(now_date,date_morning_begin,date_morning_end,date_afternooe_begin,date_afternooe_end)
    if date_morning_begin < now_date < date_afternooe_end:
    # if True:
        for fre in ['1d']:
            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)

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

            stk = locals()
            thd = threading.local()
            # 进程准备
            step = 600
            thread_list = []
            engine_stock = []
            engine_hlfx = []
            times_engine = 0
            engine_hlfx_pool = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/hlfx_pool?charset=utf8')

            # 获得hlfx_pool池子
            # results = pd.read_sql_query(
            #                 'select value from `%s`' % fre, engine_hlfx_pool)
            # for i in range(0, len(results)):
            #     print(len(results.iloc[i, 0].split(",")))

            results = pd.read_sql_query(
                            'select value from `%s`' % fre, engine_hlfx_pool).iloc[-1, 0].split(",")
            results_short = []
            print('数据库读取', len(results))

            df = get_bars(stocks, count=20, unit=fre,
                          fields=['date', 'open', 'close', 'high', 'low', 'volume', 'money'], include_now=True, df=True)
            print(dt.now(), 'get_bars 成功')

            for i in range(0, len(stocks), step):
                engine_stock.append(create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/stocks?charset=utf8'))
                engine_hlfx.append(create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/hlfx?charset=utf8'))
                thread = threading.Thread(target=hlfx, args=(stocks[i:i + step], engine_stock[times_engine], engine_hlfx[times_engine]))
                times_engine = times_engine + 1
                thread.start()
                thread_list.append(thread)

            for thread in thread_list:
                thread.join()
            db.close()



            time = dt.now().strftime('%Y-%m-%d %H:%M:%S')
            results_list =','.join(set(results))
            print(set(results))
            sql = "INSERT INTO %s (date,value) VALUES('%s','%s')" % (fre, dt.now().strftime('%Y-%m-%d %H:%M:%S'), results_list)
            cursor_pool.execute(sql)
            db_pool.commit()
            print(fre, '\n', '做多:', len(set(results)),  set(results))
            print('做空', len(set(results_short)), set(results_short))


            end= dt.now()
            print('总时长：', (end - start).seconds)
    elif now_date>date_afternooe_end:
        pass
        # print("HLFX_收盘了",now_date)
        # break