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@@ -0,0 +1,345 @@
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+from jqdatasdk import *
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+import pandas as pd
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+import pymysql
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+from sqlalchemy import create_engine
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+import threading
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+from datetime import datetime as dt
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+from jqdatasdk.technical_analysis import *
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+
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+auth('18616891214', 'Ea?*7f68nD.dafcW34d!')
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+stocks = list(get_all_securities(['stock'], date=dt.today().strftime('%Y-%m-%d')).index)
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+# stocks = stocks[0:200]
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+
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+def hlfx(stocks, engine_stock, engine_hlfx):
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+ for thd.stock in stocks:
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+ print(thd.stock)
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+ if ('stk%s_%s' % (thd.stock, fre)) in table_list:
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+ # 有历史数据
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+ index_len = pd.read_sql_table('stk%s_%s' % (thd.stock, fre), con=engine_hlfx).iloc[-1, 0]
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+ startdate = pd.read_sql_table('stk%s_%s' % (thd.stock, fre), con=engine_hlfx).iloc[-1, 1]
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+
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+ # thd.get_price = pd.read_sql_query(
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+ # 'select date,open,close,high,low,volume,money from `stk%s_%s`' % (thd.stock, fre), engine_stock)
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+ # thd.get_price = thd.get_price.loc[thd.get_price['date'] > startdate]
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+ thd.get_price = df.loc[thd.stock]
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+
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+ thd.df_day = pd.read_sql_query(
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+ 'select date,open,close,high,low,volume,money,HL from `stk%s_%s`' % (thd.stock, fre), engine_hlfx)
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+
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+ # 先处理去包含
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+ for i in thd.get_price.index:
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+ # 不包含
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+ if (thd.df_day.iloc[-1, 3] > thd.get_price.loc[i, 'high']
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+ and thd.df_day.iloc[-1, 4] > thd.get_price.loc[i, 'low']) \
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+ or (thd.df_day.iloc[-1, 3] < thd.get_price.loc[i, 'high']
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+ and thd.df_day.iloc[-1, 4] < thd.get_price.loc[i, 'low']):
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+ thd.df_day = pd.concat([thd.df_day, thd.get_price.loc[[i]]], ignore_index=True)
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+
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+ # 包含
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+ else:
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+ # (new_df.iloc[-1,3]>=df_day.loc[i,'high'] and new_df.iloc[-1,4]<= df_day.loc[i,'low']):
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+ # 左高，下降
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+ if thd.df_day.iloc[-2, 3] > thd.df_day.iloc[-1, 3]:
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+ thd.df_day.iloc[-1, 3] = min(thd.df_day.iloc[-1, 3], thd.get_price.loc[i, 'high'])
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+ thd.df_day.iloc[-1, 4] = min(thd.df_day.iloc[-1, 4], thd.get_price.loc[i, 'low'])
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+ else:
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+ # 右高，上升
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+ thd.df_day.iloc[-1, 3] = max(thd.df_day.iloc[-1, 3], thd.get_price.loc[i, 'high'])
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+ thd.df_day.iloc[-1, 4] = max(thd.df_day.iloc[-1, 4], thd.get_price.loc[i, 'low'])
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+
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+ # 寻找顶底分型
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+ if len(thd.df_day.index) > 2:
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+ x = len(thd.df_day.index)-1
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+ m = x - 1
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+ # 底
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+ if ((thd.df_day.loc[x, 'high'] > thd.df_day.loc[x - 1, 'high']) and (
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+ thd.df_day.loc[x - 2, 'high'] > thd.df_day.loc[x - 1, 'high'])):
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+ # 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])):
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+ # stk.fxdf = pd.concat([stk.fxdf, stk.df_day.iloc[[i]]], ignore_index=True)
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+ thd.df_day.loc[x, 'HL'] = 'L*'
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+ while m:
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+ if thd.df_day.loc[m, 'HL'] == 'H':
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+ if (x - m) > 3:
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+ thd.df_day.loc[x, 'HL'] = 'L'
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+ # 此处可以获得MACD指标
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+ # 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)
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+ # pass
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+ print(thd.stock, '$$$$$$$', '\n', thd.df_day.loc[x, 'date'], '\n',
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+ '笔形成————买买买买买！！')
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+ results.append(thd.stock)
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+ print('222')
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+ # break
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+ elif (thd.df_day.loc[m, 'HL'] == 'L'):
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+ if thd.df_day.loc[x - 1, 'low'] < thd.df_day.loc[m - 1, 'low']:
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+ # 前一个为底，且中间存在不包含 or 更低的底
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+ thd.df_day.loc[x, 'HL'] = 'L'
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+ x_macd_dif, x_macd_dea, x_macd_macd = MACD(thd.stock,
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+ check_date=thd.df_day.loc[x, 'date'],
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+ SHORT=12, LONG=26, MID=9, unit=fre)
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+ m_macd_dif, m_macd_dea, m_macd_macd = MACD(thd.stock,
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+ check_date=thd.df_day.loc[m, 'date'],
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+ SHORT=12, LONG=26, MID=9, unit=fre)
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+ if x_macd_dif[thd.stock] > m_macd_dif[thd.stock]:
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+ # pass
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+ # print(thd.df_day.loc[m, 'date'], thd.df_day.loc[m, 'low'],
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+ # m_macd_dif[thd.stock])
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+ # print(thd.df_day.loc[x, 'date'], thd.df_day.loc[x, 'low'],
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+ # x_macd_dif[thd.stock])
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+ print(thd.stock, '$$$$$$$', '\n', thd.df_day.loc[x, 'date'], 'MACD背驰————买吗？！')
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+ results.append(thd.stock)
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+ print('333')
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+ break
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+ else:
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+ # 底更低但没有背驰
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+ break
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+ m = m - 1
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+ if m == 0:
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+ # 第一个底
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+ thd.df_day.loc[x, 'HL'] = 'L'
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+ results.append(thd.stock)
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+ print('444')
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+ # 顶
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+ elif ((thd.df_day.loc[x, 'high'] < thd.df_day.loc[x - 1, 'high']) and (
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+ thd.df_day.loc[x - 2, 'high'] < thd.df_day.loc[x - 1, 'high'])):
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+ # 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])):
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+ # stk.fxdf = pd.concat([stk.fxdf, stk.df_day.iloc[[i]]], ignore_index=True)
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+ thd.df_day.loc[x, 'HL'] = 'H*'
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+ while m:
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+ if thd.df_day.loc[m, 'HL'] == 'L':
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+ if x - m > 3:
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+ thd.df_day.loc[x, 'HL'] = 'H'
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+ print(thd.stock, '!!!!!!!', '\n', thd.df_day.loc[x, 'date'], '笔形成————卖卖卖卖卖卖卖!')
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+ # pass
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+ results_short.append(thd.stock)
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+ if thd.stock in results:
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+ results.remove(thd.stock)
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+ # break
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+ elif (thd.df_day.loc[m, 'HL'] == 'H'):
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+ if thd.df_day.loc[x - 1, 'high'] > thd.df_day.loc[m - 1, 'high']:
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+ # 前一个为顶，且中间存在不包含 or 更高的顶
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+ thd.df_day.loc[x, 'HL'] = 'H'
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+ x_macd_dif, x_macd_dea, x_macd_macd = MACD(thd.stock,
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+ check_date=thd.df_day.loc[x, 'date'],
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+ SHORT=12, LONG=26, MID=9, unit=fre)
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+ m_macd_dif, m_macd_dea, m_macd_macd = MACD(thd.stock,
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+ check_date=thd.df_day.loc[m, 'date'],
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+ SHORT=12, LONG=26, MID=9, unit=fre)
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+ if x_macd_dif[thd.stock] < m_macd_dif[thd.stock]:
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+ # pass
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+ print(thd.stock, '/\/\/\/\/\/\/', '顶背离了！！！！')
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+ results_short.append(thd.stock)
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+ if thd.stock in results:
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+ results.remove(thd.stock)
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+ break
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+ break
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+ m = m - 1
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+ if m == 0:
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+ thd.df_day.loc[x, 'HL'] = 'H'
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+ results_short.append(thd.stock)
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+ if thd.stock in results:
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+ results.remove(thd.stock)
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+ else:
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+ thd.df_day.loc[x, 'HL'] = '-'
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+
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+ # 更新数据库
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+ # 可以使用normalize_code(code) 方法 改变代码格式
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+ # thd.df_day[index_len + 1:].to_sql('stk%s_%s' % (thd.stock, fre), con=engine_hlfx, index=True, if_exists='append')
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+ else:
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+ # 没有历史数据表
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+ thd.df_day = pd.DataFrame(columns=('date', 'open', 'close', 'high', 'low', 'volume', 'money', 'HL'))
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+ thd.get_price = pd.read_sql_query(
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+ 'select date,open,close,high,low,volume,money from `stk%s_%s`' % (thd.stock, fre), engine_stock)
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+ # 先处理去包含
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+ for i in thd.get_price.index:
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+ if i == 0 or i == 1:
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+ thd.df_day = pd.concat([thd.df_day, thd.get_price.iloc[[i]]], ignore_index=True)
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+ # 不包含
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+ elif (thd.df_day.iloc[-1, 3] > thd.get_price.loc[i, 'high']
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+ and thd.df_day.iloc[-1, 4] > thd.get_price.loc[i, 'low']) \
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+ or (thd.df_day.iloc[-1, 3] < thd.get_price.loc[i, 'high']
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+ and thd.df_day.iloc[-1, 4] < thd.get_price.loc[i, 'low']):
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+ thd.df_day = pd.concat([thd.df_day, thd.get_price.loc[[i]]], ignore_index=True)
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+ # 包含
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+ else:
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+ # 左高，下降
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+ if thd.df_day.iloc[-2, 3] > thd.df_day.iloc[-1, 3]:
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+ thd.df_day.iloc[-1, 3] = min(thd.df_day.iloc[-1, 3], thd.get_price.loc[i, 'high'])
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+ thd.df_day.iloc[-1, 4] = min(thd.df_day.iloc[-1, 4], thd.get_price.loc[i, 'low'])
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+ else:
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+ # 右高，上升
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+ thd.df_day.iloc[-1, 3] = max(thd.df_day.iloc[-1, 3], thd.get_price.loc[i, 'high'])
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+ thd.df_day.iloc[-1, 4] = max(thd.df_day.iloc[-1, 4], thd.get_price.loc[i, 'low'])
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+ if len(thd.df_day.index) > 2:
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+ # 寻找顶底分型
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+ x = len(thd.df_day.index)-1
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+ m = x - 1
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+ # 底
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+ if ((thd.df_day.loc[x, 'high'] > thd.df_day.loc[x - 1, 'high']) and (
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+ thd.df_day.loc[x - 2, 'high'] > thd.df_day.loc[x - 1, 'high'])):
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+ # 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])):
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+ # stk.fxdf = pd.concat([stk.fxdf, stk.df_day.iloc[[i]]], ignore_index=True)
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+ thd.df_day.loc[x, 'HL'] = 'L*'
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+ while m:
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+ if thd.df_day.loc[m, 'HL'] == 'H':
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+ if (x - m) > 3:
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+ thd.df_day.loc[x, 'HL'] = 'L'
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+ # 此处可以获得MACD指标
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+ # 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)
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+ # pass
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+ print(thd.stock, '$$$$$$$', '\n', thd.df_day.loc[x, 'date'], '买买买买买！！')
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+ results.append(thd.stock)
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+ # break
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+ elif (thd.df_day.loc[m, 'HL'] == 'L'):
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+ if thd.df_day.loc[x - 1, 'low'] < thd.df_day.loc[m - 1, 'low']:
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+ # 前一个为底，且中间存在不包含 or 更低的底
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+ thd.df_day.loc[x, 'HL'] = 'L'
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+ x_macd_dif, x_macd_dea, x_macd_macd = MACD(thd.stock,
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+ check_date=thd.df_day.loc[x, 'date'],
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+ SHORT=12, LONG=26, MID=9, unit=fre)
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+ m_macd_dif, m_macd_dea, m_macd_macd = MACD(thd.stock,
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+ check_date=thd.df_day.loc[m, 'date'],
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+ SHORT=12, LONG=26, MID=9, unit=fre)
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+ if x_macd_dif[thd.stock] > m_macd_dif[thd.stock]:
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+ # pass
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+ # print(thd.df_day.loc[m, 'date'], thd.df_day.loc[m, 'low'],
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+ # m_macd_dif[thd.stock])
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+ # print(thd.df_day.loc[x, 'date'], thd.df_day.loc[x, 'low'],
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+ # x_macd_dif[thd.stock])
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+ print(thd.stock, '$$$$$$$', '\n', thd.df_day.loc[x, 'date'],
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+ 'MACD背驰————买吗？！')
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+ results.append(thd.stock)
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+
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+ break
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+ else:
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+ break
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+ m = m - 1
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+ if m == 0:
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+ thd.df_day.loc[x, 'HL'] = 'L'
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+ results.append(thd.stock)
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+ # 顶
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+ elif ((thd.df_day.loc[x, 'high'] < thd.df_day.loc[x - 1, 'high']) and (
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+ thd.df_day.loc[x - 2, 'high'] < thd.df_day.loc[x - 1, 'high'])):
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+ # 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])):
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+ # stk.fxdf = pd.concat([stk.fxdf, stk.df_day.iloc[[i]]], ignore_index=True)
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+ thd.df_day.loc[x, 'HL'] = 'H*'
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+ while m:
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+ if thd.df_day.loc[m, 'HL'] == 'L':
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+ if x - m > 3:
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+ thd.df_day.loc[x, 'HL'] = 'H'
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+ print(thd.stock, '!!!!!!!', '\n', '卖卖卖卖卖卖卖!')
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+ # pass
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+ results.remove(thd.stock)
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+ # break
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+ elif (thd.df_day.loc[m, 'HL'] == 'H'):
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+ if thd.df_day.loc[x - 1, 'high'] > thd.df_day.loc[m - 1, 'high']:
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+ # 前一个为顶，且中间存在不包含 or 更高的顶
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+ thd.df_day.loc[x, 'HL'] = 'H'
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+ x_macd_dif, x_macd_dea, x_macd_macd = MACD(thd.stock,
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+ check_date=thd.df_day.loc[x, 'date'],
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+ SHORT=12, LONG=26, MID=9, unit=fre)
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+ m_macd_dif, m_macd_dea, m_macd_macd = MACD(thd.stock,
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+ check_date=thd.df_day.loc[m, 'date'],
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+ SHORT=12, LONG=26, MID=9, unit=fre)
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+ if x_macd_dif[thd.stock] < m_macd_dif[
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+ thd.stock]:
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+ # pass
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+ print(thd.stock, '/\/\/\/\/\/\/', '顶背离了！！！！')
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+ results.remove(thd.stock)
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+ break
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+ break
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+ m = m - 1
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+ if m == 0:
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+ thd.df_day.loc[x, 'HL'] = 'H'
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+ results.remove(thd.stock)
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+ else:
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+ thd.df_day.loc[x, 'HL'] = '-'
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+ # print(thd.df_day[-20:])
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+ # 更新数据库
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+ # thd.df_day.to_sql('stk%s_%s' % (thd.stock, fre), con=engine_hlfx, index=True, if_exists='append')
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+
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+start = dt.now()
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+while True:
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+ now_date = dt.now()
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+ date_morning_begin = now_date.replace(hour=9, minute=25, second=0)
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+ date_morning_end = now_date.replace(hour=11, minute=31, second=0)
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+ date_afternooe_begin = now_date.replace(hour=13, minute=0, second=0)
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|
+ 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
|
