stock/z_jq_his/updata_qbh_hlfx.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]

start = dt.now()

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()

    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.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:
                    for x in range(index_len, len(thd.df_day.index)):
                        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)
                                        if x == len(thd.df_day.index) - 1 :
                                            # pass
                                            print(thd.stock, '$$$$$$$', '\n', thd.df_day.loc[x, 'date'], '\n', thd.df_day.loc[m, 'date'], '买买买买买！！')
                                            results.append(thd.stock)
                                            print('222')
                                    # break
                                elif (thd.df_day.loc[m, 'HL'] == 'L'):
                                    if thd.df_day.loc[x - 1, 'low'] < thd.df_day.loc[m - 1, 'low']:
                                        # 前一个为底，且中间存在不包含 or 更低的底
                                        thd.df_day.loc[x, 'HL'] = 'L'
                                        x_macd_dif, x_macd_dea, x_macd_macd = MACD(thd.stock,
                                                                                   check_date=thd.df_day.loc[x, 'date'],
                                                                                   SHORT=12, LONG=26, MID=9, unit=fre)
                                        m_macd_dif, m_macd_dea, m_macd_macd = MACD(thd.stock,
                                                                                   check_date=thd.df_day.loc[m, 'date'],
                                                                                   SHORT=12, LONG=26, MID=9, unit=fre)
                                        if x == len(thd.df_day.index) - 1 and x_macd_dif[thd.stock] > m_macd_dif[thd.stock]:
                                            # pass
                                            # print(thd.df_day.loc[m, 'date'], thd.df_day.loc[m, 'low'],
                                            #       m_macd_dif[thd.stock])
                                            # print(thd.df_day.loc[x, 'date'], thd.df_day.loc[x, 'low'],
                                            #       x_macd_dif[thd.stock])
                                            print(thd.stock, '$$$$$$$', '\n', thd.df_day.loc[x, 'date'], 'MACD背驰————买吗？！')
                                            results.append(thd.stock)
                                            print('333')
                                        break
                                    else:
                                        break
                                m = m - 1
                                if m == 0:
                                    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'
                                        if x == len(thd.df_day.index) - 1:
                                            print(thd.stock, '!!!!!!!', '\n', '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 == len(thd.df_day.index) - 1 and 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
                    for x in range(2, len(thd.df_day.index)):
                        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)

                                        if x == len(thd.df_day.index) - 1:
                                            # pass
                                            print(thd.stock, '$$$$$$$', '\n', thd.df_day.loc[x, 'date'], '买买买买买！！')
                                            results.append(thd.stock)
                                    # break
                                elif (thd.df_day.loc[m, 'HL'] == 'L'):
                                    if thd.df_day.loc[x - 1, 'low'] < thd.df_day.loc[m - 1, 'low']:
                                        # 前一个为底，且中间存在不包含 or 更低的底
                                        thd.df_day.loc[x, 'HL'] = 'L'
                                        x_macd_dif, x_macd_dea, x_macd_macd = MACD(thd.stock,
                                                                                   check_date=thd.df_day.loc[x, 'date'],
                                                                                   SHORT=12, LONG=26, MID=9, unit=fre)
                                        m_macd_dif, m_macd_dea, m_macd_macd = MACD(thd.stock,
                                                                                   check_date=thd.df_day.loc[m, 'date'],
                                                                                   SHORT=12, LONG=26, MID=9, unit=fre)
                                        if x == len(thd.df_day.index) - 1 and x_macd_dif[thd.stock] > m_macd_dif[
                                            thd.stock]:
                                            # pass
                                            # print(thd.df_day.loc[m, 'date'], thd.df_day.loc[m, 'low'],
                                            #       m_macd_dif[thd.stock])
                                            # print(thd.df_day.loc[x, 'date'], thd.df_day.loc[x, 'low'],
                                            #       x_macd_dif[thd.stock])
                                            print(thd.stock, '$$$$$$$', '\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'
                                        if x == len(thd.df_day.index) - 1:
                                            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 == len(thd.df_day.index) - 1 and 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')

    step = 500
    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')

    results= pd.read_sql_query(
                    'select value from `%s`' % fre, engine_hlfx_pool)

    results = results.iloc[-1, 0]
    results = results.split(",")
    results_short=[]

    print('数据库读取', results, type(results))
    # 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('做多', set(results))
    print('做空', set(results_short))
    print(len(set(results)))
    print(len(set(results_short)))

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