新增30min实时

get_hisbars_tosql.py

@@ -32,14 +32,14 @@ engine = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/stocks?
 
 print('ready to write to mysql')
 
-fre = '1d'
+fre = '30m'
 
 for stock in stocks:
     # df_stock =df.loc[stock]
     print(stock)
     df_stock = get_price(stock, start_date='2010-01-01 00:00:00', end_date=dt.today().strftime('%Y-%m-%d %H:%M:%S'),
                          frequency=fre, fields=['open', 'close', 'high', 'low', 'volume', 'money'], skip_paused=False,
-                         fq='pre', count=None)
+                         fq='pre', count=None, panel=False)
     df_stock.reset_index(inplace=True)
     df_stock.rename(columns={'index': 'date'}, inplace=True)
     df_stock.to_sql('stk%s_%s' % (stock[:6], fre), con=engine, index=True, if_exists='replace')

hlfx.py

@@ -13,9 +13,9 @@ db = pymysql.connect(host='localhost',
                      user='root',
                      port=3307,
                      password='r6kEwqWU9!v3',
-                     database='qbh_hlfx_backup')
+                     database='qbh_hlfx')
 
-fre = '1d'
+fre = '30m'
 
 cursor = db.cursor()
 # cursor.execute("select table_name from information_schema.tables where table_schema='qbh_hlfx_backup' and table_name like {}".format('\'%{}\''.format(fre)))
@@ -26,7 +26,6 @@ table_list = [tuple[0] for tuple in cursor.fetchall()]
 stk = threading.local()
 
 
-
 # 主程序
 # 找顶底（hdx lfx）分型
 def hlfx(table_list, engine, tosql):
@@ -82,7 +81,7 @@ def hlfx(table_list, engine, tosql):
                     m = m-1
             else:
                 stk.df_day.loc[i, 'HL'] = '-'
-        stk.df_day.to_csv('/Users/daniel/Library/CloudStorage/OneDrive-个人/个人/python_stocks/20220212hlfx2/hlfx%s.csv' % table)
+        # stk.df_day.to_csv('/Users/daniel/Library/CloudStorage/OneDrive-个人/个人/python_stocks/20220212hlfx2/hlfx%s.csv' % table)
         stk.df_day.to_sql('%s' % table, con=tosql, index=True, if_exists='replace')
         with tosql.connect() as con_backup:
             con_backup.execute('ALTER TABLE %s ADD PRIMARY KEY (`date`);' % table)
@@ -93,7 +92,7 @@ def hlfx(table_list, engine, tosql):
 # tosql = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/bb22?charset=utf8')
 # hlfx(table_list, engine, tosql)
 
-step = 100
+step = 500
 thread_list = []
 engine = []
 tosql = []

qbh.py

@@ -60,12 +60,12 @@ def qbh(stocks, engine, engine_backup):
                     # 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))
-        thd.new_df.to_sql('stk%s_%s' % (stock[:6], u), con=engine_backup, index=True, if_exists='replace')
-        with engine_backup.connect() as con_backup:
-            con_backup.execute('ALTER TABLE stk%s_%s ADD PRIMARY KEY (`date`);' % (stock[:6], u))
+        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))
+        # thd.new_df.to_sql('stk%s_%s' % (stock[:6], u), con=engine_backup, index=True, if_exists='replace')
+        # with engine_backup.connect() as con_backup:
+        #     con_backup.execute('ALTER TABLE stk%s_%s ADD PRIMARY KEY (`date`);' % (stock[:6], u))
         # thd.new_df.to_csv(
         #     '/Users/daniel/Library/CloudStorage/OneDrive-个人/个人/python_stocks/20220211qbh/qbh%s.csv' % stock[:6])
         print(stock)
@@ -80,7 +80,7 @@ stk = locals()
 engine = []
 engine_backup = []
 
-u = '1d'
+u = '30m'
 # 获取数据存入DataFrame
 
 for stock in stocks:

real_time_signal.py

@@ -78,7 +78,8 @@ def qbh_hlfx(stocks, df):
                         # stk.fxdf = pd.concat([stk.fxdf, stk.df_day.iloc[[i]]], ignore_index=True)
                     thd.df_day.loc[x, 'HL'] = 'L*'
                     if x == len(thd.df_day.index) -1:
-                        print(stock, '$$$$$$$', '\n', thd.df_day.loc[x, 'date'], '考虑————买入！！')
+                        pass
+                        # print(stock, '$$$$$$$', '\n', thd.df_day.loc[x, 'date'], '考虑————买入！！')
                     while m:
                         if thd.df_day.loc[m, 'HL'] == 'H':
                             if(i-m) > 3:
@@ -134,9 +135,10 @@ while True:
     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')
-    strattime = dt.now()
+    # strattime = dt.now()
     qbh_hlfx(stocks, df)
-    endtime = dt.now()
-    break
-end = dt.now()
-print('单次时长为：', (endtime - strattime).seconds, '\n', '全时长为：', (end - start).seconds)
+    # endtime = dt.now()
+
+
+# end = dt.now()
+# print('单次时长为：', (endtime - strattime).seconds, '\n', '全时长为：', (end - start).seconds)

real_time_signal_30m.py

@@ -0,0 +1,143 @@
+import pandas as pd
+from jqdatasdk import *
+auth('18616891214', 'Ea?*7f68nD.dafcW34d!')
+
+import threading
+from sqlalchemy import create_engine
+import pymysql
+from datetime import datetime as dt
+
+start = dt.now()
+# 确定级别
+fre = '30m'
+# 连接数据库
+db = pymysql.connect(host='localhost',
+                     user='root',
+                     port=3307,
+                     password='r6kEwqWU9!v3',
+                     database='qbh_hlfx')
+# engine = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/stocks?charset=utf8')
+engine2 = create_engine('mysql+pymysql://root:r6kEwqWU9!v3@localhost:3307/hlfx_backup?charset=utf8')
+
+# 获取所有表名——确定计算范围
+cursor = db.cursor()
+cursor.execute('show tables like {}'.format('\'%{}\''.format(fre)))
+# stocks = [tuple[0] for tuple in cursor.fetchall()]
+stocks = list(get_all_securities(['stock'], date='2021-12-31').index)
+# stocks = stocks[0:500]
+print(dt.now(), 'stocks范围已获取！')
+
+# 获取各stock的去包含dataframe
+stk = locals()
+for stock in stocks:
+    try:
+        stk[stock[:6]] = pd.read_sql_query('select date,open,close,high,low,volume,money,HL from stk%s_%s' % (stock[:6], fre),
+                                        engine2)
+    except BaseException:
+        continue
+
+print(dt.now(), '数据库数据已赋值！')
+
+thd = threading.local()
+
+
+def qbh_hlfx(stocks, df):
+    for stock in stocks:
+        try:
+            # thd.new_df = pd.DataFrame(columns=('date', 'open', 'close', 'high', 'low', 'volume', 'money', 'HL'))
+            thd.df_day = stk[stock[:6]]
+            thd.get_bars = df.loc[stock]
+            stk_len = len(thd.df_day)
+            # 先处理去包含
+            for i in thd.get_bars.index:
+                # 不包含
+                if (thd.df_day.iloc[-1, 3] > thd.get_bars.loc[i, 'high']
+                    and thd.df_day.iloc[-1, 4] > thd.get_bars.loc[i, 'low']) \
+                        or (thd.df_day.iloc[-1, 3] < thd.get_bars.loc[i, 'high']
+                            and thd.df_day.iloc[-1, 4] < thd.get_bars.loc[i, 'low']):
+                    thd.df_day = pd.concat([thd.df_day, thd.get_bars.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']):
+                    # 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_bars.loc[i, 'high'])
+                        thd.df_day.iloc[-1, 4] = min(thd.df_day.iloc[-1, 4], thd.get_bars.loc[i, 'low'])
+                    else:
+                        # 右高，上升
+                        thd.df_day.iloc[-1, 3] = max(thd.df_day.iloc[-1, 3], thd.get_bars.loc[i, 'high'])
+                        thd.df_day.iloc[-1, 4] = max(thd.df_day.iloc[-1, 4], thd.get_bars.loc[i, 'low'])
+        # return thd.df_day
+
+            # 寻找顶底分型
+            for x in range(stk_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*'
+                    if x == len(thd.df_day.index) -1:
+                        pass
+                        # print(stock, '$$$$$$$', '\n', thd.df_day.loc[x, 'date'], '考虑————买入！！')
+                    while m:
+                        if thd.df_day.loc[m, 'HL'] == 'H':
+                            if(i-m) > 3:
+                                thd.df_day.loc[x, 'HL'] = 'L'
+                                if x == len(thd.df_day.index) - 1:
+                                    print(stock, '$$$$$$$', '\n', '买买买买买！！')
+                            break
+                        elif (thd.df_day.loc[m, 'HL'] == 'L' ):
+                            if thd.df_day.loc[x-1, 'low'] < thd.df_day.loc[m-1, 'low']:
+                                # 前一个为底，且中间存在不包含 or 更低的底
+                                thd.df_day.loc[x, 'HL'] = 'L'
+                                if x == len(thd.df_day.index) - 1:
+                                    print(stock, '$$$$$$$', '\n', '中继后的底————买吗？！')
+                                break
+                            else:
+                                break
+                        m = m-1
+
+                # 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*'
+                    if x == len(thd.df_day.index) - 1:
+                        # print(stock, '!!!!!!!', '\n', thd.df_day.loc[x, 'date'], '考虑————卖出！！')
+                        pass
+                    while m:
+                        if thd.df_day.loc[m, 'HL'] == 'L':
+                            if i-m > 3:
+                                thd.df_day.loc[x, 'HL'] = 'H'
+                                if x == len(thd.df_day.index) - 1:
+                                    # print(stock, '!!!!!!!', '\n', '卖卖卖卖卖卖卖!')
+                                    pass
+                                thd.df_day.loc[x, 9] = thd.df_day.loc[x, 'close'] - thd.df_day.loc[m, 'close']
+                            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'
+                                if x == len(thd.df_day.index) - 1:
+                                    pass
+                                    # print(stock, '/\/\/\/\/\/\/', '一顶更有一顶高！')
+                                break
+                            break
+                        m = m-1
+                else:
+                    thd.df_day.loc[x, 'HL'] = '-'
+        except BaseException:
+            continue
+
+
+while True:
+    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')
+    # strattime = dt.now()
+    qbh_hlfx(stocks, df)
+    # endtime = dt.now()
+
+# end = dt.now()
+# print('单次时长为：', (endtime - strattime).seconds, '\n', '全时长为：', (end - start).seconds)