no message

.idea/encodings.xml

@@ -8,9 +8,22 @@
     <file url="file://$PROJECT_DIR$/10日未涨停回封/2022-202307/202201-202307月-10日未涨停(000)股本首笔大单大于700w-b1-8%type第一个为B小于1600w炸板率.csv" charset="GBK" />
     <file url="file://$PROJECT_DIR$/10日未涨停回封/2022-202307/2022未涨停(000)股本首笔大单大于700w-b1-8%type第一个为B炸板率(振幅).csv" charset="GBK" />
     <file url="file://$PROJECT_DIR$/10日未涨停回封/2022-202307/2022未涨停(000)股本首笔大单大于700w-b1-8%type第一个为B炸板率.csv" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/10日未涨停回封/2022-202307/2023未涨停(000)股本.csv" charset="GBK" />
     <file url="file://$PROJECT_DIR$/10日未涨停回封/2022-202307/2023未涨停(000)股本首笔大单大于700w-b1-8%type第一个为B(振幅).csv" charset="GBK" />
     <file url="file://$PROJECT_DIR$/10日未涨停回封/2022-202307/2023未涨停(000)股本首笔大单大于700w-b1-8%type第一个为B-1.csv" charset="GBK" />
     <file url="file://$PROJECT_DIR$/10日未涨停回封/2022-202307/2023未涨停(000)股本首笔大单大于700w-b1-8%type第一个为B炸板率.csv" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/10日未涨停回封/2022-202307/2023涨停(000)股本涨停时间.csv" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/10日未涨停回封/2022-202307/不复权/2022-2023涨停(000)股本2连大于700w炸板率.csv" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/10日未涨停回封/2022-202307/不复权/2022涨停(000)股本2连大于700w.csv" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/10日未涨停回封/2022-202307/不复权/2023涨停(000)股本2连大于700w.csv" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/10日未涨停回封/2022-202307/不复权/2023涨停(000)股本N连单10%大于700w.csv" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/10日未涨停回封/2022-202307/不复权/2023涨停(000)股本N连单大于700w.csv" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/10日未涨停回封/2022-202307/不复权/300/2022-2023涨停(300)股本N连单大于700w.csv" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/10日未涨停回封/2022-202307/不复权/300/2023涨停(000)股本N连单大于700w.csv" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/10日未涨停回封/2022-202307/不复权/688/2022-2023涨停(688)股本N连单大于700w.csv" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/10日未涨停回封/2022-202307/不复权/688/2023涨停(688)股本N连单大于700w.csv" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/10日未涨停回封/2022-202307/不复权/数据修复/2022涨停(000)股本N连单10%大于700w.csv" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/10日未涨停回封/2022-202307/不复权/数据修复/2022涨停(000)股本N连单大于700w.csv" charset="GBK" />
     <file url="file://$PROJECT_DIR$/10日未涨停回封/2023-1-7月-10日未涨停首笔大单大于700w-b1-8%type第一个为B(小于1600W).csv" charset="GBK" />
     <file url="file://$PROJECT_DIR$/10日未涨停回封/2023-1-7月-10日未涨停首笔大单大于700w-b1-8%type第一个为B.csv" charset="GBK" />
     <file url="file://$PROJECT_DIR$/10日未涨停回封/2023-1-7月-10日未涨停首笔大单大于700w-b1-8%type第一个为B炸板率.csv" charset="GBK" />
@@ -29,10 +42,7 @@
     <file url="file://$PROJECT_DIR$/2023-08-25炸板率.csv" charset="GBK" />
     <file url="file://$PROJECT_DIR$/2023-08-29-000-7.csv" charset="GBK" />
     <file url="file://$PROJECT_DIR$/2023-09-01炸板率.csv" charset="GBK" />
-    <file url="file://$PROJECT_DIR$/2023-09-04-000.csv" charset="GBK" />
-    <file url="file://$PROJECT_DIR$/2023-09-04-600.csv" charset="GBK" />
-    <file url="file://$PROJECT_DIR$/2023-09-07炸板率.csv" charset="GBK" />
-    <file url="file://$PROJECT_DIR$/2023-09-13炸板率.csv" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/2023-09-18-000.csv" charset="GBK" />
     <file url="file://$PROJECT_DIR$/2板新高/2023-000-45日未涨停2板.csv" charset="GBK" />
     <file url="file://$PROJECT_DIR$/300-600/300-600.csv" charset="GBK" />
     <file url="file://$PROJECT_DIR$/300/2022-10日未涨停(300)首笔大单大于700w-b1-8%type第一个为B炸板率(振幅).csv" charset="GBK" />
@@ -49,6 +59,7 @@
     <file url="file://$PROJECT_DIR$/600打板/2023-1-7月-10日未涨停600首笔大单大于700w-b1-8%type第一个为B.csv" charset="GBK" />
     <file url="file://$PROJECT_DIR$/600打板/2023未涨停(600)股本.csv" charset="GBK" />
     <file url="file://$PROJECT_DIR$/600打板/2023涨停(600)股本首笔大单大于700w-b1-8%type第一个为B炸板率(振幅).csv" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/600打板/600统计.csv" charset="GBK" />
     <file url="file://$PROJECT_DIR$/N字反包/2022/2022反包7距板收盘价跌幅大于5%首笔大单大于700w-b1-8%type第一个为B.csv" charset="GBK" />
     <file url="file://$PROJECT_DIR$/N字反包/2022/2022反包7距板收盘价跌幅大于5%首笔大单大于700w-b1-8%type第一个为B炸板率(振幅).csv" charset="GBK" />
     <file url="file://$PROJECT_DIR$/N字反包/2022/2022反包7距板收盘价跌幅大于5%首笔大单大于700w-b1-8%type第一个为B炸板率.csv" charset="GBK" />
@@ -119,5 +130,15 @@
     <file url="file://$PROJECT_DIR$/集合竞价数据/2023-08-13-000-7.csv" charset="GBK" />
     <file url="file://$PROJECT_DIR$/集合竞价数据/2023-08-14-000.csv" charset="GBK" />
     <file url="file://$PROJECT_DIR$/集合竞价数据/2023-08-23-000.csv" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/集合竞价数据/2023-09-04-000.csv" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/集合竞价数据/2023-09-04-000.csv" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/集合竞价数据/2023-09-04-600.csv" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/集合竞价数据/2023-09-04-600.csv" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/集合竞价数据/2023-09-07炸板率.csv" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/集合竞价数据/2023-09-07炸板率.csv" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/集合竞价数据/2023-09-13炸板率.csv" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/集合竞价数据/2023-09-13炸板率.csv" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/集合竞价数据/2023-09-14炸板率.csv" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/集合竞价数据/2023-09-14炸板率.csv" charset="GBK" />
   </component>
 </project>
\ No newline at end of file

000黑名单.csv

@@ -39,3 +39,21 @@ SZ003026 ,中晶科技
 SZ002883,中设股份
 SZ002915,中欣氟材
 SZ002166,莱茵生物
+SH603330,天洋新材
+SH603887,城地香江
+SH605589,圣泉集团
+SH600776,东方通信
+SH603777,来伊份
+SH603130,云中马
+SH603507,振江股份
+SH603915,国茂股份
+SH603324,盛剑环境
+SH600686,金龙汽车
+SH603258,电魂网络
+SH600797,浙大网新
+SH603429,集友股份
+SH600288,大恒科技
+SH603220,中贝通信
+SH601528,瑞丰银行
+SH603011,合锻智能
+SH603815,交建股份

10日未涨停回封/2022-202307/不复权/300/700wN连大单.py

+import csv
+import math
+import os
+import time
+
+import numpy as np
+import pandas as pd
+root='/Volumes/Elements SE/逐笔202301-03/'
+import datetime
+
+data=pd.read_csv('./2022-2023涨停(300)股本.csv',encoding='gbk')
+data['日期']=pd.to_datetime(data['日期'])
+data = data.groupby('日期')
+percent=0.1
+name = pd.read_excel('/Users/yumingwu/Desktop/沪深A股.xlsx')
+with open('./2023涨停(000)股本N连单10%大于700w'+'.csv','w',encoding='gbk',newline='') as csfile:
+    writer =csv.writer(csfile)
+    writer.writerow(['代码','名称','日期', '昨日收盘价','开盘价','最低价','最高价涨幅','成交量','成交额','换手率','买入价格','卖出价格','盈利','涨幅','流通市值','跟单明细','连单笔数','总金额','时间'])
+    for day,dataR in data:
+
+        print(day)
+        if(day<pd.to_datetime('2023-01-01')):
+            continue
+        daystr= day.strftime("%Y-%m-%d")
+
+        for index,item in dataR.iterrows():
+            current_time = datetime.datetime.now()
+            print(item['代码'])
+            print("现在时间是: " + str(current_time))
+            # if item['代码']!='SZ002851':
+            #     continue
+            if os.path.exists(root + daystr + '/' + item['代码'][2:8] + '.csv') == False:
+                print(root + daystr + '/' + item['代码'][2:8] + '.csv')
+                continue
+
+            zhubiData= pd.read_csv(root+daystr+'/'+item['代码'][2:8]+'.csv')
+            zhubiData=zhubiData[zhubiData['Time']>'09:25:00']
+            zhubiData['amount']=zhubiData['Volume']*zhubiData['BuyOrderPrice']
+            zhangtingjia=zhubiData['BuyOrderPrice'].max()
+            maxPrice= zhubiData['Price'].max()
+            maxPriceItem = zhubiData[zhubiData['Price']>=maxPrice]
+            maxPriceItem=maxPriceItem.iloc[0]
+
+            zhangtingdata = zhubiData[zhubiData['BuyOrderID']==maxPriceItem['BuyOrderID']]
+
+            # stockNum = 7000000 / (zhangtingjia-0.01)
+            # zhubiData = zhubiData[zhubiData['BuyOrderVolume'] > stockNum]
+
+
+            # zhangtingjiaData=zhubiData[zhubiData['Price']>=item['昨日收盘价']*1.09]
+            zhangtingjiaData = zhubiData[zhubiData['BuyOrderPrice'] >= zhangtingjia-0.03]
+
+            zhangtingjiaData=zhangtingjiaData[zhangtingjiaData['Type']=='B']
+
+            zhangtingjiaData=zhangtingjiaData.groupby('BuyOrderID')
+            zhangtingjiaData1=pd.DataFrame()
+            dicARR = {}
+            keyArr = []
+            for id, zhangtingjiaDataItem in zhangtingjiaData:
+                zhangtingjiaDataItem1 =zhubiData[zhubiData['BuyOrderID']==id].copy()
+                zhangtingjiaDataItem1 = zhangtingjiaDataItem1[zhangtingjiaDataItem1['BuyOrderPrice'] >= zhangtingjia-0.03]
+
+                amount = zhangtingjiaDataItem1['amount'].sum()
+                if amount<1000000:
+                    continue
+                vol = zhangtingjiaDataItem1['Volume'].sum()
+                p0 = zhangtingjiaDataItem1.iloc[0]
+                p1 = zhangtingjiaDataItem1.iloc[-1]
+                time =p0['Time']
+                TranID1 = p0['TranID']
+                TranID2 = p1['TranID']
+                buyId = p0['BuyOrderID']
+
+                buyidData= {
+                    'amount':amount,
+                    'vol': vol,
+                    'TranID1': TranID1,
+                    'TranID2': TranID2,
+                    'buyId':buyId,
+                    'time':time
+
+                }
+                key =str(TranID1)
+                dicARR[key] = buyidData
+                keyArr.append(TranID1)
+
+
+
+
+
+
+            if len(dicARR)==0:
+                continue
+            keyArr.sort()
+
+            for trainIDIndex in np.arange(len(keyArr)):
+                key = keyArr[trainIDIndex]
+                subkeyArr = keyArr[trainIDIndex+1:len(keyArr)]
+
+                item_key = dicARR[str(key)]
+
+
+                vol = item_key['vol']
+                amount = item_key['amount']
+                TranID1 = item_key['TranID1']
+                TranID2 = item_key['TranID2']
+                buyId = item_key['buyId']
+                time = item_key['time']
+
+
+                preItem = item_key
+                itemArr = []
+                itemArr.append(item_key)
+                amountZ = amount
+                flag = False
+                for subkey in subkeyArr:
+                    item1 = dicARR[str(subkey)]
+                    vol_01 = item1['vol']
+                    amount_01 = item1['amount']
+                    TranID1_01 = item1['TranID1']
+                    TranID2_01 = item1['TranID2']
+
+                    if vol_01>(vol*(1+percent)):
+                        break
+                    if vol_01<(vol*(1-percent)):
+                        break
+                    if TranID1_01 !=preItem['TranID2']+1:
+                        break
+                    amountZ = amountZ+amount_01
+                    itemArr.append(item1)
+                    preItem = item1
+
+                    liandan= []
+                    for detailItem in  itemArr:
+                        info=str(detailItem['vol']) + ',' + str(int(detailItem['amount'])) + ',' + str(
+                            detailItem['TranID1']) + ',' + str(detailItem['TranID2']) + ',' + str(
+                            detailItem['buyId']) + ',' + str(detailItem['time'])
+                        liandan.append(info)
+
+                    info='--|--'.join(liandan)
+
+                    if amountZ>=7000000:
+                        flag =True
+                        codename = item['代码'][2:8] + "." + item['代码'][0:2]
+
+                        codename = name[name['股票代码'] == codename]
+                        if len(codename) == 0:
+                            codename = ''
+                        else:
+                            codename = codename.iloc[0]['股票简称']
+
+                        writer.writerow(
+                            [item['代码'],
+                             codename,
+                             item['日期'],
+                             item['昨日收盘价'],
+                             item['开盘价'],
+                             item['最低价'],
+                             item['最高价涨幅'],
+                             item['成交量'],
+                             item['成交额'],
+                             item['换手率'],
+                             item['买入价格'],
+                             item['卖出价格'],
+                             item['盈利'],
+                             item['涨幅'],
+                             item['流通市值'],
+                             info,
+                             len(liandan),
+                             amountZ,
+                             item1['time']
+
+                             ])
+                        break
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+                if flag:
+                    break
+
+

10日未涨停回封/2022-202307/不复权/300/振幅.py

+import csv
+import os
+import time
+from _decimal import Decimal
+
+import numpy as np
+import pandas as pd
+root='/Users/yumingwu/Downloads/Stk_Day_QFQ/'
+path =os.listdir(root)
+
+data = pd.read_csv('./2022-2023涨停(300)股本N连单大于700w炸板率.csv',encoding='gbk')
+
+file000 = open('./2022-2023涨停(300)股本N连单大于700w振幅.csv','w',encoding='gbk',newline='')
+writer000 = csv.writer(file000)
+writer000.writerow(
+    ['代码','名称','日期', '昨日收盘价','开盘价','开盘涨幅','最低价','最高价涨幅','成交量','成交额','换手率','买入价格','卖出价格','昨日是否涨停','盈利','涨幅','流通市值','跟单明细','连单笔数','总金额','时间','炸板率','涨停次数','炸板次数','触板次数','炸板幅度','涨停次日溢价率','次日总溢价率','最近3次触板溢价','一年最高价跌幅','半年最高价跌幅','一日最高价跌幅','三日最高价跌幅','6日最高价跌幅','10日最高价跌幅','12日最高价跌幅','一个月最高价跌幅','3个月最高价跌幅','9个月最高价跌幅',
+     '1个月最低价涨幅','3个月最低价涨幅','半年最低价涨幅','9个月最低价涨幅','一年最低价涨幅','振幅','昨日振幅','10日涨幅'])
+for index,item in data.iterrows():
+    day = item['日期']
+    day =pd.to_datetime(day)
+    code= item['代码']+'.csv'
+
+    codeData=pd.read_csv(root+code,encoding='gbk')
+    codeData['时间'] = pd.to_datetime(codeData['时间'])
+    codeData =codeData[codeData['时间']<day]
+    codeData0=codeData.iloc[-1]
+    codeData1 = codeData.iloc[-2]
+    codeData=codeData[len(codeData)-10:len(codeData)]
+    min = codeData0['收盘价']/(codeData['最低价'].min())-1
+    writer000.writerow(
+        [item['代码'],
+         item['名称'],
+         item['日期'],
+         item['昨日收盘价'],
+         item['开盘价'],
+         item['开盘涨幅'],
+         item['最低价'],
+         item['最高价涨幅'],
+         item['成交量'],
+         item['成交额'],
+         item['换手率'],
+         item['买入价格'],
+         item['卖出价格'],
+         item['昨日是否涨停'],
+         item['盈利'],
+         item['涨幅'],
+         item['流通市值'],
+         item['跟单明细'],
+         item['连单笔数'],
+         item['总金额'],
+         item['时间'],
+         item['炸板率'],
+         item['涨停次数'],
+         item['炸板次数'],
+         item['触板次数'],
+         item['炸板幅度'],
+         item['涨停次日溢价率'],
+         item['次日总溢价率'],
+         item['最近3次触板溢价'],
+         item['一年最高价跌幅'],
+         item['半年最高价跌幅'],
+         item['一日最高价跌幅'],
+         item['三日最高价跌幅'],
+         item['6日最高价跌幅'],
+         item['10日最高价跌幅'],
+         item['12日最高价跌幅'],
+         item['一个月最高价跌幅'],
+         item['3个月最高价跌幅'],
+         item['9个月最高价跌幅'],
+         item['1个月最低价涨幅'],
+         item['3个月最低价涨幅'],
+         item['半年最低价涨幅'],
+         item['9个月最低价涨幅'],
+         item['一年最低价涨幅'],
+         (codeData0['最高价']-codeData0['最低价'])/codeData1['收盘价'],
+         (codeData0['最高价'] - codeData0['最低价']) / codeData0['收盘价'],
+         min
+         ])

10日未涨停回封/2022-202307/不复权/300/炸板率.py

+import csv
+import os
+import time
+from _decimal import Decimal
+
+import numpy as np
+import pandas as pd
+root='/Users/yumingwu/Downloads/Stk_Day_QFQ/'
+path =os.listdir(root)
+
+data = pd.read_csv('./2022-2023涨停(300)股本N连单大于700w.csv',encoding='gbk')
+
+file000 = open('./2022-2023涨停(300)股本N连单大于700w炸板率.csv','w',encoding='gbk',newline='')
+writer000 = csv.writer(file000)
+zhangtingpercent = 1.198
+writer000.writerow(
+    ['代码','名称','日期', '昨日收盘价','开盘价','开盘涨幅','最低价','最高价涨幅','成交量','成交额','换手率','买入价格','卖出价格','昨日是否涨停','盈利','涨幅','流通市值','跟单明细','连单笔数','总金额','时间','炸板率','涨停次数','炸板次数','触板次数','炸板幅度','涨停次日溢价率','次日总溢价率','最近3次触板溢价','一年最高价跌幅','半年最高价跌幅','一日最高价跌幅','三日最高价跌幅','6日最高价跌幅','10日最高价跌幅','12日最高价跌幅','一个月最高价跌幅','3个月最高价跌幅','9个月最高价跌幅',
+     '1个月最低价涨幅','3个月最低价涨幅','半年最低价涨幅','9个月最低价涨幅','一年最低价涨幅'])
+for index,item in data.iterrows():
+    day = item['日期']
+    day =pd.to_datetime(day)
+    code= item['代码']+'.csv'
+
+
+    print(item['代码'])
+    codeData=pd.read_csv(root+code,encoding='gbk')
+    codeData['时间'] = pd.to_datetime(codeData['时间'])
+    codeData =codeData[codeData['时间']<day]
+    day1=day-pd.offsets.DateOffset(years=1)
+    codeData = codeData[codeData['时间'] >= day1]
+    if len(codeData)==0:
+        continue
+    day2 = day - pd.offsets.DateOffset(months=6)
+    day1_month = day - pd.offsets.DateOffset(months=1)
+    day3_month = day - pd.offsets.DateOffset(months=3)
+    day9_month = day - pd.offsets.DateOffset(months=9)
+    codeData_6_month = codeData[codeData['时间'] >= day2]
+
+    codeData_1_month = codeData[codeData['时间'] >= day1_month]
+    codeData_3_month = codeData[codeData['时间'] >= day3_month]
+    codeData_9_month = codeData[codeData['时间'] >= day9_month]
+
+    codeData1 = codeData[len(codeData)-1:len(codeData)]
+    codeData3= codeData[len(codeData) - 3:len(codeData)]
+    codeData6 = codeData[len(codeData) - 6:len(codeData)]
+    codeData9 = codeData[len(codeData) - 10:len(codeData)]
+    codeData12 = codeData[len(codeData) - 12:len(codeData)]
+    item_y=codeData.iloc[-1]
+    item_y2 = codeData.iloc[-1]
+    if item_y['收盘价'] / item_y2['收盘价'] > zhangtingpercent:
+        zhangting = 1
+    rate_year = item_y['收盘价']/codeData['最高价'].max()
+    rate_year_min = item_y['收盘价'] / codeData['最低价'].min()
+    rate_6_month = item_y['收盘价'] / codeData_6_month['最高价'].max()
+    rate_6_month_min = item_y['收盘价'] / codeData_6_month['最低价'].min()
+
+
+    rate_1_month = item_y['收盘价'] / codeData_1_month['最高价'].max()
+    rate_1_month_min = item_y['收盘价'] / codeData_1_month['最低价'].min()
+    rate_3_month = item_y['收盘价'] / codeData_3_month['最高价'].max()
+    rate_3_month_min = item_y['收盘价'] / codeData_3_month['最低价'].min()
+
+
+    rate_9_month = item_y['收盘价'] / codeData_9_month['最高价'].max()
+    rate_9_month_min = item_y['收盘价'] / codeData_9_month['最低价'].min()
+
+
+    rate_1 = item_y['收盘价'] / codeData1['最高价'].max()
+    rate_3= item_y['收盘价'] / codeData3['最高价'].max()
+    rate_6 = item_y['收盘价'] / codeData6['最高价'].max()
+    rate_9 = item_y['收盘价'] / codeData9['最高价'].max()
+    rate_12 = item_y['收盘价'] / codeData12['最高价'].max()
+
+
+    zhangtingTime=0
+    zhaBanTime=0
+    zhaBanTimefudu=0
+    zhangtingyijia=0
+    zhangtingyijiazong = 0
+    arr3 = []
+    for i in np.arange(len(codeData)):
+        if i==0:
+            continue
+        if i==len(codeData)-1:
+            continue
+        item0 = codeData.iloc[i-1]
+        item1 = codeData.iloc[i]
+        item2 = codeData.iloc[i+1]
+
+        if item1['最高价']/item0['收盘价']>zhangtingpercent:
+            zhangtingyijiazong = zhangtingyijiazong + item2['开盘价'] / item1['最高价'] - 1.0014
+            arr3.append(item2['开盘价'] / item1['最高价'] - 1.0014)
+            if item1['最高价']==item1['收盘价']:
+                zhangtingTime=zhangtingTime+1
+                zhangtingyijia = zhangtingyijia + item2['开盘价'] / item1['最高价'] - 1.0014
+
+
+            else:
+                zhaBanTime=zhaBanTime+1
+                zhaBanTimefudu=zhaBanTimefudu+(item2['开盘价']/item1['最高价']-1.0014)
+
+    rate =0
+    if zhangtingTime>0 or zhaBanTime>0:
+        rate = zhaBanTime/(zhangtingTime+zhaBanTime)
+
+
+    if zhaBanTime!=0:
+        zhaBanTimefudu=zhaBanTimefudu/zhaBanTime
+    if zhangtingTime!=0:
+        zhangtingyijia=zhangtingyijia/zhangtingTime
+    if (zhangtingTime +zhaBanTime)!= 0:
+        zhangtingyijiazong = zhangtingyijiazong / (zhangtingTime+zhaBanTime)
+
+    rate3=-100
+    if len(arr3)>=3:
+        arr3 = arr3[len(arr3)-3:len(arr3)]
+        rate3 = (arr3[0]+arr3[1]+arr3[2])/3
+
+    zhangting = 0;
+
+
+
+    writer000.writerow(
+        [item['代码'],
+         item['名称'],
+         item['日期'],
+         item['昨日收盘价'],
+         item['开盘价'],
+         float(item['开盘价'])/float(item['昨日收盘价']),
+         item['最低价'],
+         item['最高价涨幅'],
+         item['成交量'],
+         item['成交额'],
+         item['换手率'],
+         item['买入价格'],
+         item['卖出价格'],
+         zhangting,
+         item['盈利'],
+         item['涨幅'],
+         item['流通市值'],
+         item['跟单明细'],
+         item['连单笔数'],
+         item['总金额'],
+         item['时间'],
+         rate,
+         zhangtingTime,
+         zhaBanTime,
+         zhangtingTime+zhaBanTime,
+         zhaBanTimefudu,
+         zhangtingyijia,
+         zhangtingyijiazong,
+         rate3,
+         rate_year,
+         rate_6_month,
+         rate_1,
+         rate_3,
+         rate_6,
+         rate_9,
+         rate_12,
+         rate_1_month,
+         rate_3_month,
+         rate_9_month,
+         rate_1_month_min,
+         rate_3_month_min,
+         rate_6_month_min,
+         rate_9_month_min,
+         rate_year_min
+
+
+         ])

10日未涨停回封/2022-202307/不复权/688/700wN连大单.py

+import csv
+import math
+import os
+import time
+
+import numpy as np
+import pandas as pd
+root='/Volumes/Elements SE/逐笔202301-03/'
+import datetime
+
+data=pd.read_csv('./2022-2023涨停(688)股本.csv',encoding='gbk')
+data['日期']=pd.to_datetime(data['日期'])
+data = data.groupby('日期')
+percent=0.01
+name = pd.read_excel('/Users/yumingwu/Desktop/沪深A股.xlsx')
+with open('./2023涨停(688)股本N连单大于700w'+'.csv','w',encoding='gbk',newline='') as csfile:
+    writer =csv.writer(csfile)
+    writer.writerow(['代码','名称','日期', '昨日收盘价','开盘价','最低价','最高价涨幅','成交量','成交额','换手率','买入价格','卖出价格','盈利','涨幅','流通市值','跟单明细','连单笔数','总金额','时间'])
+    for day,dataR in data:
+
+        print(day)
+        if(day<pd.to_datetime('2023-01-01')):
+            continue
+        daystr= day.strftime("%Y-%m-%d")
+
+        for index,item in dataR.iterrows():
+            current_time = datetime.datetime.now()
+            print(item['代码'])
+            print("现在时间是: " + str(current_time))
+            # if item['代码']!='SZ002851':
+            #     continue
+            if os.path.exists(root + daystr + '/' + item['代码'][2:8] + '.csv') == False:
+                print(root + daystr + '/' + item['代码'][2:8] + '.csv')
+                continue
+
+            zhubiData= pd.read_csv(root+daystr+'/'+item['代码'][2:8]+'.csv')
+            zhubiData=zhubiData[zhubiData['Time']>'09:25:00']
+            zhubiData['amount']=zhubiData['Volume']*zhubiData['BuyOrderPrice']
+            zhangtingjia=zhubiData['BuyOrderPrice'].max()
+            maxPrice= zhubiData['Price'].max()
+            maxPriceItem = zhubiData[zhubiData['Price']>=maxPrice]
+            maxPriceItem=maxPriceItem.iloc[0]
+
+            zhangtingdata = zhubiData[zhubiData['BuyOrderID']==maxPriceItem['BuyOrderID']]
+
+            # stockNum = 7000000 / (zhangtingjia-0.01)
+            # zhubiData = zhubiData[zhubiData['BuyOrderVolume'] > stockNum]
+
+
+            # zhangtingjiaData=zhubiData[zhubiData['Price']>=item['昨日收盘价']*1.09]
+            zhangtingjiaData = zhubiData[zhubiData['BuyOrderPrice'] >= zhangtingjia-0.03]
+
+            zhangtingjiaData=zhangtingjiaData[zhangtingjiaData['Type']=='B']
+
+            zhangtingjiaData=zhangtingjiaData.groupby('BuyOrderID')
+            zhangtingjiaData1=pd.DataFrame()
+            dicARR = {}
+            keyArr = []
+            for id, zhangtingjiaDataItem in zhangtingjiaData:
+                zhangtingjiaDataItem1 =zhubiData[zhubiData['BuyOrderID']==id].copy()
+                zhangtingjiaDataItem1 = zhangtingjiaDataItem1[zhangtingjiaDataItem1['BuyOrderPrice'] >= zhangtingjia-0.03]
+
+                amount = zhangtingjiaDataItem1['amount'].sum()
+                if amount<1000000:
+                    continue
+                vol = zhangtingjiaDataItem1['Volume'].sum()
+                p0 = zhangtingjiaDataItem1.iloc[0]
+                p1 = zhangtingjiaDataItem1.iloc[-1]
+                time =p0['Time']
+                TranID1 = p0['TranID']
+                TranID2 = p1['TranID']
+                buyId = p0['BuyOrderID']
+
+                buyidData= {
+                    'amount':amount,
+                    'vol': vol,
+                    'TranID1': TranID1,
+                    'TranID2': TranID2,
+                    'buyId':buyId,
+                    'time':time
+
+                }
+                key =str(TranID1)
+                dicARR[key] = buyidData
+                keyArr.append(TranID1)
+
+
+
+
+
+
+            if len(dicARR)==0:
+                continue
+            keyArr.sort()
+
+            for trainIDIndex in np.arange(len(keyArr)):
+                key = keyArr[trainIDIndex]
+                subkeyArr = keyArr[trainIDIndex+1:len(keyArr)]
+
+                item_key = dicARR[str(key)]
+
+
+                vol = item_key['vol']
+                amount = item_key['amount']
+                TranID1 = item_key['TranID1']
+                TranID2 = item_key['TranID2']
+                buyId = item_key['buyId']
+                time = item_key['time']
+
+
+                preItem = item_key
+                itemArr = []
+                itemArr.append(item_key)
+                amountZ = amount
+                flag = False
+                for subkey in subkeyArr:
+                    item1 = dicARR[str(subkey)]
+                    vol_01 = item1['vol']
+                    amount_01 = item1['amount']
+                    TranID1_01 = item1['TranID1']
+                    TranID2_01 = item1['TranID2']
+
+                    if vol_01>(vol*(1+percent)):
+                        break
+                    if vol_01<(vol*(1-percent)):
+                        break
+                    if TranID1_01 !=preItem['TranID2']+1:
+                        break
+                    amountZ = amountZ+amount_01
+                    itemArr.append(item1)
+                    preItem = item1
+
+                    liandan= []
+                    for detailItem in  itemArr:
+                        info=str(detailItem['vol']) + ',' + str(int(detailItem['amount'])) + ',' + str(
+                            detailItem['TranID1']) + ',' + str(detailItem['TranID2']) + ',' + str(
+                            detailItem['buyId']) + ',' + str(detailItem['time'])
+                        liandan.append(info)
+
+                    info='--|--'.join(liandan)
+
+                    if amountZ>=7000000:
+                        flag =True
+                        codename = item['代码'][2:8] + "." + item['代码'][0:2]
+
+                        codename = name[name['股票代码'] == codename]
+                        if len(codename) == 0:
+                            codename = ''
+                        else:
+                            codename = codename.iloc[0]['股票简称']
+
+                        writer.writerow(
+                            [item['代码'],
+                             codename,
+                             item['日期'],
+                             item['昨日收盘价'],
+                             item['开盘价'],
+                             item['最低价'],
+                             item['最高价涨幅'],
+                             item['成交量'],
+                             item['成交额'],
+                             item['换手率'],
+                             item['买入价格'],
+                             item['卖出价格'],
+                             item['盈利'],
+                             item['涨幅'],
+                             item['流通市值'],
+                             info,
+                             len(liandan),
+                             amountZ,
+                             item1['time']
+
+                             ])
+                        break
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+                if flag:
+                    break
+
+

10日未涨停回封/2022-202307/不复权/688/振幅.py

+import csv
+import os
+import time
+from _decimal import Decimal
+
+import numpy as np
+import pandas as pd
+root='/Users/yumingwu/Downloads/Stk_Day_QFQ/'
+path =os.listdir(root)
+
+data = pd.read_csv('./2022-2023涨停(688)股本N连单大于700w炸板率.csv',encoding='gbk')
+
+file000 = open('./2022-2023涨停(688)股本N连单大于700w振幅.csv','w',encoding='gbk',newline='')
+writer000 = csv.writer(file000)
+writer000.writerow(
+    ['代码','名称','日期', '昨日收盘价','开盘价','开盘涨幅','最低价','最高价涨幅','成交量','成交额','换手率','买入价格','卖出价格','昨日是否涨停','盈利','涨幅','流通市值','跟单明细','连单笔数','总金额','时间','炸板率','涨停次数','炸板次数','触板次数','炸板幅度','涨停次日溢价率','次日总溢价率','最近3次触板溢价','一年最高价跌幅','半年最高价跌幅','一日最高价跌幅','三日最高价跌幅','6日最高价跌幅','10日最高价跌幅','12日最高价跌幅','一个月最高价跌幅','3个月最高价跌幅','9个月最高价跌幅',
+     '1个月最低价涨幅','3个月最低价涨幅','半年最低价涨幅','9个月最低价涨幅','一年最低价涨幅','振幅','昨日振幅','10日涨幅'])
+for index,item in data.iterrows():
+    day = item['日期']
+    day =pd.to_datetime(day)
+    code= item['代码']+'.csv'
+
+    codeData=pd.read_csv(root+code,encoding='gbk')
+    codeData['时间'] = pd.to_datetime(codeData['时间'])
+    codeData =codeData[codeData['时间']<day]
+    codeData0=codeData.iloc[-1]
+    codeData1 = codeData.iloc[-2]
+    codeData=codeData[len(codeData)-10:len(codeData)]
+    min = codeData0['收盘价']/(codeData['最低价'].min())-1
+    writer000.writerow(
+        [item['代码'],
+         item['名称'],
+         item['日期'],
+         item['昨日收盘价'],
+         item['开盘价'],
+         item['开盘涨幅'],
+         item['最低价'],
+         item['最高价涨幅'],
+         item['成交量'],
+         item['成交额'],
+         item['换手率'],
+         item['买入价格'],
+         item['卖出价格'],
+         item['昨日是否涨停'],
+         item['盈利'],
+         item['涨幅'],
+         item['流通市值'],
+         item['跟单明细'],
+         item['连单笔数'],
+         item['总金额'],
+         item['时间'],
+         item['炸板率'],
+         item['涨停次数'],
+         item['炸板次数'],
+         item['触板次数'],
+         item['炸板幅度'],
+         item['涨停次日溢价率'],
+         item['次日总溢价率'],
+         item['最近3次触板溢价'],
+         item['一年最高价跌幅'],
+         item['半年最高价跌幅'],
+         item['一日最高价跌幅'],
+         item['三日最高价跌幅'],
+         item['6日最高价跌幅'],
+         item['10日最高价跌幅'],
+         item['12日最高价跌幅'],
+         item['一个月最高价跌幅'],
+         item['3个月最高价跌幅'],
+         item['9个月最高价跌幅'],
+         item['1个月最低价涨幅'],
+         item['3个月最低价涨幅'],
+         item['半年最低价涨幅'],
+         item['9个月最低价涨幅'],
+         item['一年最低价涨幅'],
+         (codeData0['最高价']-codeData0['最低价'])/codeData1['收盘价'],
+         (codeData0['最高价'] - codeData0['最低价']) / codeData0['收盘价'],
+         min
+         ])

10日未涨停回封/2022-202307/不复权/688/炸板率.py

+import csv
+import os
+import time
+from _decimal import Decimal
+
+import numpy as np
+import pandas as pd
+root='/Users/yumingwu/Downloads/Stk_Day_QFQ/'
+path =os.listdir(root)
+
+data = pd.read_csv('./2022-2023涨停(688)股本N连单大于700w.csv',encoding='gbk')
+
+file000 = open('./2022-2023涨停(688)股本N连单大于700w炸板率.csv','w',encoding='gbk',newline='')
+writer000 = csv.writer(file000)
+zhangtingpercent = 1.198
+writer000.writerow(
+    ['代码','名称','日期', '昨日收盘价','开盘价','开盘涨幅','最低价','最高价涨幅','成交量','成交额','换手率','买入价格','卖出价格','昨日是否涨停','盈利','涨幅','流通市值','跟单明细','连单笔数','总金额','时间','炸板率','涨停次数','炸板次数','触板次数','炸板幅度','涨停次日溢价率','次日总溢价率','最近3次触板溢价','一年最高价跌幅','半年最高价跌幅','一日最高价跌幅','三日最高价跌幅','6日最高价跌幅','10日最高价跌幅','12日最高价跌幅','一个月最高价跌幅','3个月最高价跌幅','9个月最高价跌幅',
+     '1个月最低价涨幅','3个月最低价涨幅','半年最低价涨幅','9个月最低价涨幅','一年最低价涨幅'])
+for index,item in data.iterrows():
+    day = item['日期']
+    day =pd.to_datetime(day)
+    code= item['代码']+'.csv'
+
+
+    print(item['代码'])
+    codeData=pd.read_csv(root+code,encoding='gbk')
+    codeData['时间'] = pd.to_datetime(codeData['时间'])
+    codeData =codeData[codeData['时间']<day]
+    day1=day-pd.offsets.DateOffset(years=1)
+    codeData = codeData[codeData['时间'] >= day1]
+    if len(codeData)==0:
+        continue
+    day2 = day - pd.offsets.DateOffset(months=6)
+    day1_month = day - pd.offsets.DateOffset(months=1)
+    day3_month = day - pd.offsets.DateOffset(months=3)
+    day9_month = day - pd.offsets.DateOffset(months=9)
+    codeData_6_month = codeData[codeData['时间'] >= day2]
+
+    codeData_1_month = codeData[codeData['时间'] >= day1_month]
+    codeData_3_month = codeData[codeData['时间'] >= day3_month]
+    codeData_9_month = codeData[codeData['时间'] >= day9_month]
+
+    codeData1 = codeData[len(codeData)-1:len(codeData)]
+    codeData3= codeData[len(codeData) - 3:len(codeData)]
+    codeData6 = codeData[len(codeData) - 6:len(codeData)]
+    codeData9 = codeData[len(codeData) - 10:len(codeData)]
+    codeData12 = codeData[len(codeData) - 12:len(codeData)]
+    item_y=codeData.iloc[-1]
+    item_y2 = codeData.iloc[-1]
+    if item_y['收盘价'] / item_y2['收盘价'] > zhangtingpercent:
+        zhangting = 1
+    rate_year = item_y['收盘价']/codeData['最高价'].max()
+    rate_year_min = item_y['收盘价'] / codeData['最低价'].min()
+    rate_6_month = item_y['收盘价'] / codeData_6_month['最高价'].max()
+    rate_6_month_min = item_y['收盘价'] / codeData_6_month['最低价'].min()
+
+
+    rate_1_month = item_y['收盘价'] / codeData_1_month['最高价'].max()
+    rate_1_month_min = item_y['收盘价'] / codeData_1_month['最低价'].min()
+    rate_3_month = item_y['收盘价'] / codeData_3_month['最高价'].max()
+    rate_3_month_min = item_y['收盘价'] / codeData_3_month['最低价'].min()
+
+
+    rate_9_month = item_y['收盘价'] / codeData_9_month['最高价'].max()
+    rate_9_month_min = item_y['收盘价'] / codeData_9_month['最低价'].min()
+
+
+    rate_1 = item_y['收盘价'] / codeData1['最高价'].max()
+    rate_3= item_y['收盘价'] / codeData3['最高价'].max()
+    rate_6 = item_y['收盘价'] / codeData6['最高价'].max()
+    rate_9 = item_y['收盘价'] / codeData9['最高价'].max()
+    rate_12 = item_y['收盘价'] / codeData12['最高价'].max()
+
+
+    zhangtingTime=0
+    zhaBanTime=0
+    zhaBanTimefudu=0
+    zhangtingyijia=0
+    zhangtingyijiazong = 0
+    arr3 = []
+    for i in np.arange(len(codeData)):
+        if i==0:
+            continue
+        if i==len(codeData)-1:
+            continue
+        item0 = codeData.iloc[i-1]
+        item1 = codeData.iloc[i]
+        item2 = codeData.iloc[i+1]
+
+        if item1['最高价']/item0['收盘价']>zhangtingpercent:
+            zhangtingyijiazong = zhangtingyijiazong + item2['开盘价'] / item1['最高价'] - 1.0014
+            arr3.append(item2['开盘价'] / item1['最高价'] - 1.0014)
+            if item1['最高价']==item1['收盘价']:
+                zhangtingTime=zhangtingTime+1
+                zhangtingyijia = zhangtingyijia + item2['开盘价'] / item1['最高价'] - 1.0014
+
+
+            else:
+                zhaBanTime=zhaBanTime+1
+                zhaBanTimefudu=zhaBanTimefudu+(item2['开盘价']/item1['最高价']-1.0014)
+
+    rate =0
+    if zhangtingTime>0 or zhaBanTime>0:
+        rate = zhaBanTime/(zhangtingTime+zhaBanTime)
+
+
+    if zhaBanTime!=0:
+        zhaBanTimefudu=zhaBanTimefudu/zhaBanTime
+    if zhangtingTime!=0:
+        zhangtingyijia=zhangtingyijia/zhangtingTime
+    if (zhangtingTime +zhaBanTime)!= 0:
+        zhangtingyijiazong = zhangtingyijiazong / (zhangtingTime+zhaBanTime)
+
+    rate3=-100
+    if len(arr3)>=3:
+        arr3 = arr3[len(arr3)-3:len(arr3)]
+        rate3 = (arr3[0]+arr3[1]+arr3[2])/3
+
+    zhangting = 0;
+
+
+
+    writer000.writerow(
+        [item['代码'],
+         item['名称'],
+         item['日期'],
+         item['昨日收盘价'],
+         item['开盘价'],
+         float(item['开盘价'])/float(item['昨日收盘价']),
+         item['最低价'],
+         item['最高价涨幅'],
+         item['成交量'],
+         item['成交额'],
+         item['换手率'],
+         item['买入价格'],
+         item['卖出价格'],
+         zhangting,
+         item['盈利'],
+         item['涨幅'],
+         item['流通市值'],
+         item['跟单明细'],
+         item['连单笔数'],
+         item['总金额'],
+         item['时间'],
+         rate,
+         zhangtingTime,
+         zhaBanTime,
+         zhangtingTime+zhaBanTime,
+         zhaBanTimefudu,
+         zhangtingyijia,
+         zhangtingyijiazong,
+         rate3,
+         rate_year,
+         rate_6_month,
+         rate_1,
+         rate_3,
+         rate_6,
+         rate_9,
+         rate_12,
+         rate_1_month,
+         rate_3_month,
+         rate_9_month,
+         rate_1_month_min,
+         rate_3_month_min,
+         rate_6_month_min,
+         rate_9_month_min,
+         rate_year_min
+
+
+         ])

10日未涨停回封/2022-202307/不复权/700w2连大单.py

+import csv
+import math
+import os
+import time
+
+import numpy as np
+import pandas as pd
+root='/Volumes/Elements SE/逐笔202301-03/'
+import datetime
+
+data=pd.read_csv('./2022-2023涨停(000)股本.csv',encoding='gbk')
+data['日期']=pd.to_datetime(data['日期'])
+data = data.groupby('日期')
+
+name = pd.read_excel('/Users/yumingwu/Desktop/沪深A股.xlsx')
+with open('./2023涨停(000)股本2连大于700w'+'.csv','w',encoding='gbk',newline='') as csfile:
+    writer =csv.writer(csfile)
+    writer.writerow(['代码','名称','日期', '昨日收盘价','开盘价','最低价','最高价涨幅','成交量','成交额','换手率','买入价格','卖出价格','盈利','涨幅','流通市值','涨停trainID','涨停BuyOrderID','涨停vol','涨停amount','大单ID','时间'])
+    for day,dataR in data:
+
+        print(day)
+        if(day<pd.to_datetime('2023-01-01')):
+            continue
+        daystr= day.strftime("%Y-%m-%d")
+
+        for index,item in dataR.iterrows():
+            current_time = datetime.datetime.now()
+            print(item['代码'])
+            print("现在时间是: " + str(current_time))
+            # if item['代码']!='SZ002851':
+            #     continue
+            if os.path.exists(root + daystr + '/' + item['代码'][2:8] + '.csv') == False:
+                print(root + daystr + '/' + item['代码'][2:8] + '.csv')
+                continue
+
+            zhubiData= pd.read_csv(root+daystr+'/'+item['代码'][2:8]+'.csv')
+            zhubiData=zhubiData[zhubiData['Time']>'09:25:00']
+            zhubiData['amount']=zhubiData['Volume']*zhubiData['BuyOrderPrice']
+            zhangtingjia=zhubiData['BuyOrderPrice'].max()
+            maxPrice= zhubiData['Price'].max()
+            maxPriceItem = zhubiData[zhubiData['Price']>=maxPrice]
+            maxPriceItem=maxPriceItem.iloc[0]
+
+            zhangtingdata = zhubiData[zhubiData['BuyOrderID']==maxPriceItem['BuyOrderID']]
+
+            # stockNum = 7000000 / (zhangtingjia-0.01)
+            # zhubiData = zhubiData[zhubiData['BuyOrderVolume'] > stockNum]
+
+
+            # zhangtingjiaData=zhubiData[zhubiData['Price']>=item['昨日收盘价']*1.09]
+            zhangtingjiaData = zhubiData[zhubiData['BuyOrderPrice'] >= zhangtingjia - 0.01]
+            zhangtingjiaData=zhangtingjiaData[zhangtingjiaData['Price']>=item['昨日收盘价']*1.08]
+
+            zhangtingjiaData=zhangtingjiaData.groupby('BuyOrderID')
+            zhangtingjiaData1=pd.DataFrame()
+            for id, zhangtingjiaDataItem in zhangtingjiaData:
+                zhangtingjiaDataItem1 =zhubiData[zhubiData['BuyOrderID']==id].copy()
+                amount = zhangtingjiaDataItem1['amount'].sum()
+                if (amount >= 7000000):
+                    continue
+
+
+                if (amount < 2000000):
+                    continue
+                p0 = zhangtingjiaDataItem1.iloc[0]
+                TranID = p0['TranID']
+                zhangtingjiaDataItem1['ID']=TranID
+                zhangtingjiaData1=zhangtingjiaData1.append(zhangtingjiaDataItem1)
+            if len(zhangtingjiaData1)==0:
+                continue
+
+            zhangtingjiaData =zhangtingjiaData1.groupby('ID')
+            preamount = 0
+            prezhangtingjiaDataItem=0
+            prezhangtingjiaDataItemLast = 0
+            for id ,zhangtingjiaDataItem in zhangtingjiaData:
+
+                amount = zhangtingjiaDataItem['amount'].sum()
+
+                if preamount == 0:
+                    preamount =amount
+                    prezhangtingjiaDataItem =zhangtingjiaDataItem.iloc[0]
+                    prezhangtingjiaDataItemLast = zhangtingjiaDataItem.iloc[-1]
+                    continue
+
+
+
+                if preamount>=7000000:
+                    preamount = amount
+                    prezhangtingjiaDataItem = zhangtingjiaDataItem.iloc[0]
+                    prezhangtingjiaDataItemLast = zhangtingjiaDataItem.iloc[-1]
+                    continue
+
+                if prezhangtingjiaDataItem['Type']!= 'B':
+                    preamount = amount
+                    prezhangtingjiaDataItem = zhangtingjiaDataItem.iloc[0]
+                    prezhangtingjiaDataItemLast = zhangtingjiaDataItem.iloc[-1]
+                    continue
+
+
+                p0 = zhangtingjiaDataItem.iloc[0]
+                middledata = zhubiData[zhubiData['TranID'] <p0['TranID']]
+                middledata = middledata[middledata['TranID'] > prezhangtingjiaDataItemLast['TranID']]
+
+                if len(middledata)>0:
+                    preamount = amount
+                    prezhangtingjiaDataItem = zhangtingjiaDataItem.iloc[0]
+                    prezhangtingjiaDataItemLast = zhangtingjiaDataItem.iloc[-1]
+                    continue
+
+                if amount >= 7000000 :
+                    preamount = amount
+                    prezhangtingjiaDataItem = zhangtingjiaDataItem.iloc[0]
+                    prezhangtingjiaDataItemLast = zhangtingjiaDataItem.iloc[-1]
+                    continue
+
+                if p0['Type'] != 'B':
+                    preamount = amount
+                    prezhangtingjiaDataItem = zhangtingjiaDataItem.iloc[0]
+                    prezhangtingjiaDataItemLast = zhangtingjiaDataItem.iloc[-1]
+                    continue
+                if preamount+amount<7000000:
+                    preamount = amount
+                    prezhangtingjiaDataItem = zhangtingjiaDataItem.iloc[0]
+                    prezhangtingjiaDataItemLast = zhangtingjiaDataItem.iloc[-1]
+
+                    lastItem = zhangtingjiaDataItem.iloc[-1]
+                    sellAll=zhangtingjiaData1[zhangtingjiaData1['ID'] > id]
+                    sellAll = sellAll[sellAll['Type']=='B']
+                    if len(sellAll)==0:
+                        break
+                    continue
+
+
+                type = p0['Type']
+                time1 =p0['Time']
+                # zhangtingjiaDataItemS = zhangtingjiaDataItem[zhangtingjiaDataItem['Type']=='S']
+                # zhangtingjiaDataItemSLength = len(zhangtingjiaDataItemS)
+                # p0 = zhangtingjiaDataItem.iloc[-1]
+                p0=p0['Price']
+
+                num = math.floor(2000000 / item['买入价格'] / 100) * 100
+                codename = item['代码'][2:8]+"."+item['代码'][0:2]
+
+                codename = name[name['股票代码']==codename]
+                if len(codename)==0:
+                    codename=''
+                else:
+                    codename = codename.iloc[0]['股票简称']
+
+                print(item['代码'],'信号')
+                writer.writerow(
+                    [item['代码'],
+                     codename,
+                     item['日期'],
+                     item['昨日收盘价'],
+                     item['开盘价'],
+                     item['最低价'],
+                     item['最高价涨幅'],
+                     item['成交量'],
+                     item['成交额'],
+                     item['换手率'],
+                     item['买入价格'],
+                     item['卖出价格'],
+                     num * (item['卖出价格'] - item['买入价格']) - item['买入价格'] * num * 0.0002 - item[
+                         '卖出价格'] * num * 0.0012,
+
+                     item['涨幅'],
+                     item['流通市值'],
+                     maxPriceItem['TranID'],
+                     maxPriceItem['BuyOrderID'],
+                     zhangtingdata['Volume'].sum(),
+                     amount,
+                     id,
+
+                     time1,
+
+                     ])
+                break
+
+
+
+
+
+

10日未涨停回封/2022-202307/不复权/700wN连大单.py

+import csv
+import math
+import os
+import time
+
+import numpy as np
+import pandas as pd
+root='/Volumes/Elements SE/逐笔202301-03/'
+import datetime
+
+data=pd.read_csv('./2022-2023涨停(000)股本.csv',encoding='gbk')
+data['日期']=pd.to_datetime(data['日期'])
+data = data.groupby('日期')
+
+name = pd.read_excel('/Users/yumingwu/Desktop/沪深A股.xlsx')
+with open('./2023涨停(000)股本N连单10%大于700w'+'.csv','w',encoding='gbk',newline='') as csfile:
+    writer =csv.writer(csfile)
+    writer.writerow(['代码','名称','日期', '昨日收盘价','开盘价','最低价','最高价涨幅','成交量','成交额','换手率','买入价格','卖出价格','盈利','涨幅','流通市值','跟单明细','连单笔数','总金额','时间'])
+    for day,dataR in data:
+
+        print(day)
+        if(day<pd.to_datetime('2023-01-01')):
+            continue
+        daystr= day.strftime("%Y-%m-%d")
+
+        for index,item in dataR.iterrows():
+            current_time = datetime.datetime.now()
+            print(item['代码'])
+            print("现在时间是: " + str(current_time))
+            # if item['代码']!='SZ002851':
+            #     continue
+            if os.path.exists(root + daystr + '/' + item['代码'][2:8] + '.csv') == False:
+                print(root + daystr + '/' + item['代码'][2:8] + '.csv')
+                continue
+
+            zhubiData= pd.read_csv(root+daystr+'/'+item['代码'][2:8]+'.csv')
+            zhubiData=zhubiData[zhubiData['Time']>'09:25:00']
+            zhubiData['amount']=zhubiData['Volume']*zhubiData['BuyOrderPrice']
+            zhangtingjia=zhubiData['BuyOrderPrice'].max()
+            maxPrice= zhubiData['Price'].max()
+            maxPriceItem = zhubiData[zhubiData['Price']>=maxPrice]
+            maxPriceItem=maxPriceItem.iloc[0]
+
+            zhangtingdata = zhubiData[zhubiData['BuyOrderID']==maxPriceItem['BuyOrderID']]
+
+            # stockNum = 7000000 / (zhangtingjia-0.01)
+            # zhubiData = zhubiData[zhubiData['BuyOrderVolume'] > stockNum]
+
+
+            # zhangtingjiaData=zhubiData[zhubiData['Price']>=item['昨日收盘价']*1.09]
+            zhangtingjiaData = zhubiData[zhubiData['BuyOrderPrice'] >= zhangtingjia-0.03]
+
+            zhangtingjiaData=zhangtingjiaData[zhangtingjiaData['Type']=='B']
+
+            zhangtingjiaData=zhangtingjiaData.groupby('BuyOrderID')
+            zhangtingjiaData1=pd.DataFrame()
+            dicARR = {}
+            keyArr = []
+            for id, zhangtingjiaDataItem in zhangtingjiaData:
+                zhangtingjiaDataItem1 =zhubiData[zhubiData['BuyOrderID']==id].copy()
+                zhangtingjiaDataItem1 = zhangtingjiaDataItem1[zhangtingjiaDataItem1['BuyOrderPrice'] >= zhangtingjia-0.03]
+
+                amount = zhangtingjiaDataItem1['amount'].sum()
+                if amount<1000000:
+                    continue
+                vol = zhangtingjiaDataItem1['Volume'].sum()
+                p0 = zhangtingjiaDataItem1.iloc[0]
+                p1 = zhangtingjiaDataItem1.iloc[-1]
+                time =p0['Time']
+                TranID1 = p0['TranID']
+                TranID2 = p1['TranID']
+                buyId = p0['BuyOrderID']
+
+                buyidData= {
+                    'amount':amount,
+                    'vol': vol,
+                    'TranID1': TranID1,
+                    'TranID2': TranID2,
+                    'buyId':buyId,
+                    'time':time
+
+                }
+                key =str(TranID1)
+                dicARR[key] = buyidData
+                keyArr.append(TranID1)
+
+
+
+
+
+
+            if len(dicARR)==0:
+                continue
+            keyArr.sort()
+
+            for trainIDIndex in np.arange(len(keyArr)):
+                key = keyArr[trainIDIndex]
+                subkeyArr = keyArr[trainIDIndex+1:len(keyArr)]
+
+                item_key = dicARR[str(key)]
+
+
+                vol = item_key['vol']
+                amount = item_key['amount']
+                TranID1 = item_key['TranID1']
+                TranID2 = item_key['TranID2']
+                buyId = item_key['buyId']
+                time = item_key['time']
+
+
+                preItem = item_key
+                itemArr = []
+                itemArr.append(item_key)
+                amountZ = amount
+                flag = False
+                for subkey in subkeyArr:
+                    item1 = dicARR[str(subkey)]
+                    vol_01 = item1['vol']
+                    amount_01 = item1['amount']
+                    TranID1_01 = item1['TranID1']
+                    TranID2_01 = item1['TranID2']
+
+                    if vol_01>vol*1.1:
+                        break
+                    if vol_01<vol*0.9:
+                        break
+                    if TranID1_01 !=preItem['TranID2']+1:
+                        break
+                    amountZ = amountZ+amount_01
+                    itemArr.append(item1)
+                    preItem = item1
+
+                    liandan= []
+                    for detailItem in  itemArr:
+                        info=str(detailItem['vol']) + ',' + str(int(detailItem['amount'])) + ',' + str(
+                            detailItem['TranID1']) + ',' + str(detailItem['TranID2']) + ',' + str(
+                            detailItem['buyId']) + ',' + str(detailItem['time'])
+                        liandan.append(info)
+
+                    info='--|--'.join(liandan)
+
+                    if amountZ>=7000000:
+                        flag =True
+                        codename = item['代码'][2:8] + "." + item['代码'][0:2]
+
+                        codename = name[name['股票代码'] == codename]
+                        if len(codename) == 0:
+                            codename = ''
+                        else:
+                            codename = codename.iloc[0]['股票简称']
+
+                        writer.writerow(
+                            [item['代码'],
+                             codename,
+                             item['日期'],
+                             item['昨日收盘价'],
+                             item['开盘价'],
+                             item['最低价'],
+                             item['最高价涨幅'],
+                             item['成交量'],
+                             item['成交额'],
+                             item['换手率'],
+                             item['买入价格'],
+                             item['卖出价格'],
+                             item['盈利'],
+                             item['涨幅'],
+                             item['流通市值'],
+                             info,
+                             len(liandan),
+                             amountZ,
+                             item1['time']
+
+                             ])
+                        break
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+                if flag:
+                    break
+
+

10日未涨停回封/2022-202307/不复权/700大单.py

+import csv
+import math
+import os
+import time
+
+import numpy as np
+import pandas as pd
+root='/Volumes/Elements SE/逐笔202301-03/'
+import datetime
+
+data=pd.read_csv('./2023涨停(000)股本2连大于700w.csv',encoding='gbk')
+data['日期']=pd.to_datetime(data['日期'])
+data = data.groupby('日期')
+
+name = pd.read_excel('/Users/yumingwu/Desktop/沪深A股.xlsx')
+
+with open('./2023涨停(000)股本2连大于700w明细'+'.csv','w',encoding='gbk',newline='') as csfile:
+    writer =csv.writer(csfile)
+    writer.writerow(['代码','名称','日期', '昨日收盘价','开盘价','最低价','最高价涨幅','成交量','成交额','换手率','买入价格','卖出价格','盈利','涨幅','流通市值','涨停trainID','涨停BuyOrderID','涨停vol','第二单涨停amount','第二单大单vol','第二单大单ID','第二单大单时间','第一单涨停amount','第一单大单vol','第一单大单ID','第一单大单时间','2单amount'])
+    for day,dataR in data:
+
+        print(day)
+        if (day < pd.to_datetime('2023-01-01')):
+            continue
+        daystr= day.strftime("%Y-%m-%d")
+
+        for index,item in dataR.iterrows():
+            current_time = datetime.datetime.now()
+            print(item['代码'])
+            print("现在时间是: " + str(current_time))
+            # if item['代码']!='SZ002851':
+            #     continue
+            if os.path.exists(root + daystr + '/' + item['代码'][2:8] + '.csv') == False:
+                print(root + daystr + '/' + item['代码'][2:8] + '.csv')
+                continue
+
+            zhubiData= pd.read_csv(root+daystr+'/'+item['代码'][2:8]+'.csv')
+            zhubiData=zhubiData[zhubiData['Time']>'09:25:00']
+            zhubiData['amount']=zhubiData['Volume']*zhubiData['BuyOrderPrice']
+            zhangting2 = zhubiData[zhubiData['TranID'] ==item['大单ID']]
+            zhangting2=zhangting2.iloc[0]
+            zhangting2 = zhubiData[zhubiData['BuyOrderID'] ==zhangting2['BuyOrderID']]
+            amount  = zhangting2['amount'].sum()
+            vol =  zhangting2['Volume'].sum()
+            zhangting1 = zhubiData[zhubiData['TranID'] < item['大单ID']]
+            zhangting1 = zhangting1.iloc[-1]
+
+            zhangting1 = zhubiData[zhubiData['BuyOrderID'] == zhangting1['BuyOrderID']]
+            id=zhangting1.iloc[0]['TranID']
+            time = zhangting1.iloc[0]['Time']
+            amount1 = zhangting1['amount'].sum()
+            vol1 = zhangting1['Volume'].sum()
+
+            writer.writerow(
+                [item['代码'],
+                 item['名称'],
+                 item['日期'],
+                 item['昨日收盘价'],
+                 item['开盘价'],
+                 item['最低价'],
+                 item['最高价涨幅'],
+                 item['成交量'],
+                 item['成交额'],
+                 item['换手率'],
+                 item['买入价格'],
+                 item['卖出价格'],
+                 item['盈利'],
+
+                 item['涨幅'],
+                 item['流通市值'],
+                 item['涨停trainID'],
+                 item['涨停BuyOrderID'],
+                 item['涨停vol'],
+                 amount,
+                 vol,
+                 item['大单ID'],
+                 item['时间'],
+                 amount1,
+                 vol1,
+                 id,
+                 time,
+                 amount+amount1
+
+
+
+
+                 ])
+            # stockNum = 7000000 / (zhangtingjia-0.01)
+            # zhubiData = zhubiData[zhubiData['BuyOrderVolume'] > stockNum]
+

10日未涨停回封/2022-202307/不复权/main涨停(1).py

+import csv
+import math
+import os
+import time
+
+import numpy as np
+import pandas as pd
+root='D:/Administrator/Desktop/Stk_Day/'
+
+path =os.listdir(root)
+path.sort()
+
+num =10
+
+guben1 = pd.read_csv('../../../股本/流通股本20230924.csv',encoding='gbk')
+guben1['变更日期']=pd.to_datetime(guben1['变更日期'])
+with open('./2022-2023涨停(000)股本'+'.csv','w',encoding='gbk',newline='') as csfile:
+    writer =csv.writer(csfile)
+    writer.writerow(['代码','日期','昨日收盘价','开盘价','最低价','最高价涨幅','成交量','成交额','换手率','买入价格','卖出价格','盈利','涨幅','10日涨幅','流通市值'])
+
+    for spath in path:
+        # 过滤000
+        if spath.find("SZ0") == -1:
+            continue
+
+        print(spath)
+        dataS = pd.read_csv(root + spath, encoding='gbk')
+        dataS['时间'] = pd.to_datetime(dataS['时间'])
+        data1=dataS[dataS['时间']>=pd.to_datetime('20220101')]
+        data1 = data1[data1['时间'] < pd.to_datetime('20230907')]
+        code = spath[2:8] + "." + spath[0:2]
+
+        guben = guben1[guben1['代码']==code]
+        guben =guben.sort_values('变更日期',ascending=False)
+        for indexitemn in np.arange(len(data1)):
+            if indexitemn==len(data1)-1:
+                continue
+            itemn=data1.iloc[indexitemn]
+
+            itemn1 = data1.iloc[indexitemn+1]
+            data=dataS[dataS['时间']<itemn['时间']]
+
+
+
+
+            if (len(data) <5):
+                continue
+
+            item = data.iloc[-1]
+
+            if itemn['最高价'] / item['收盘价'] <= 1.09:
+                continue
+            zhangting = 0
+
+
+            if zhangting == 0:
+
+
+                data10 = data.iloc[len(data) - 10 - 1:len(data)]
+                minprice = data10['开盘价'].min()
+                zhangfu = item['收盘价'] / minprice
+                # if zhangfu >= 1.15:
+                #     continue
+
+
+                gubenitem = guben.iloc[0]
+                gubenitem1 = guben[guben['变更日期']<=item['时间']]
+                if len(gubenitem1)==0:
+                    gubenitem1 = guben.iloc[-1]
+                else:
+                    gubenitem1=gubenitem1.iloc[0]
+
+
+                huanshou=item['成交量(股)'] / gubenitem1['流通A股']
+
+
+                shizhi = gubenitem['流通A股'] * item['收盘价'] / 100000000.0
+                # if shizhi > 200:
+                #     continue
+                num1 = math.floor(2000000 / itemn['最高价'] / 100) * 100
+                writer.writerow([itemn['代码'],
+                                 itemn['时间'],
+                                 item['收盘价'],
+                                 itemn['开盘价'],
+                                 itemn['最低价'],
+                                 itemn['最高价']/item['收盘价']-1,
+                                 item['成交量(股)'],
+                                 item['成交额(元)'],
+                                 huanshou,
+                                 itemn['最高价'],
+                                 itemn1['开盘价'],
+                                 num1 * (itemn1['开盘价'] -itemn['最高价']) - itemn['最高价'] * num1 * 0.0002 -
+                                 itemn1['开盘价'] * num1 * 0.0012,
+                                 itemn1['开盘价'] / itemn['最高价']-1-0.0014,
+                                 zhangfu,
+                                 shizhi,
+
+                                 ])
+

10日未涨停回封/2022-202307/不复权/振幅.py

+import csv
+import os
+import time
+from _decimal import Decimal
+
+import numpy as np
+import pandas as pd
+root='/Users/yumingwu/Downloads/Stk_Day_QFQ/'
+path =os.listdir(root)
+
+data = pd.read_csv('./2022-2023涨停(000)股本2连大于700w炸板率.csv',encoding='gbk')
+
+file000 = open('./2022-2023涨停(000)股本2连大于700w炸板率(振幅).csv','w',encoding='gbk',newline='')
+writer000 = csv.writer(file000)
+writer000.writerow(
+    ['代码', '名称', '日期', '昨日收盘价', '开盘价', '最低价', '最高价涨幅', '成交量', '成交额', '换手率', '买入价格',
+     '卖出价格', '盈利', '涨幅', '流通市值', '涨停trainID', '涨停BuyOrderID', '涨停vol', '涨停amount','大单ID',
+     '时间','炸板率','涨停次数','炸板次数','炸板幅度','涨停次日溢价率','次日总溢价率','最近3次触板溢价','一年最高价跌幅','半年最高价跌幅','一日最高价跌幅','三日最高价跌幅','6日最高价跌幅','10日最高价跌幅','12日最高价跌幅','一个月最高价跌幅','3个月最高价跌幅','9个月最高价跌幅','1个月最低价涨幅','3个月最低价涨幅','半年最低价涨幅','9个月最低价涨幅','一年最低价涨幅','振幅','昨日振幅','10日涨幅'])
+for index,item in data.iterrows():
+    day = item['日期']
+    day =pd.to_datetime(day)
+    code= item['代码']+'.csv'
+
+    codeData=pd.read_csv(root+code,encoding='gbk')
+    codeData['时间'] = pd.to_datetime(codeData['时间'])
+    codeData =codeData[codeData['时间']<day]
+    codeData0=codeData.iloc[-1]
+    codeData1 = codeData.iloc[-2]
+    codeData=codeData[len(codeData)-10:len(codeData)]
+    min = codeData0['收盘价']/(codeData['最低价'].min())-1
+    writer000.writerow(
+        [item['代码'],
+         item['名称'],
+         item['日期'],
+         item['昨日收盘价'],
+         item['开盘价'],
+         item['最低价'],
+         item['最高价涨幅'],
+         item['成交量'],
+         item['成交额'],
+         item['换手率'],
+         item['买入价格'],
+         item['卖出价格'],
+         item['盈利'],
+         item['涨幅'],
+         item['流通市值'],
+         item['涨停trainID'],
+         item['涨停BuyOrderID'],
+         item['涨停vol'],
+         item['涨停amount'],
+         item['大单ID'],
+         item['时间'],
+         item['炸板率'],
+         item['涨停次数'],
+         item['炸板次数'],
+         item['炸板幅度'],
+         item['涨停次日溢价率'],
+         item['次日总溢价率'],
+         item['最近3次触板溢价'],
+         item['一年最高价跌幅'],
+         item['半年最高价跌幅'],
+         item['一日最高价跌幅'],
+         item['三日最高价跌幅'],
+         item['6日最高价跌幅'],
+         item['10日最高价跌幅'],
+         item['12日最高价跌幅'],
+         item['一个月最高价跌幅'],
+         item['3个月最高价跌幅'],
+         item['9个月最高价跌幅'],
+         item['1个月最低价涨幅'],
+         item['3个月最低价涨幅'],
+         item['半年最低价涨幅'],
+         item['9个月最低价涨幅'],
+         item['一年最低价涨幅'],
+         (codeData0['最高价']-codeData0['最低价'])/codeData1['收盘价'],
+         (codeData0['最高价'] - codeData0['最低价']) / codeData0['收盘价'],
+         min
+         ])

10日未涨停回封/2022-202307/不复权/数据修复/反包.py

+import csv
+import os
+import time
+from _decimal import Decimal
+
+import numpy as np
+import pandas as pd
+root='/Users/yumingwu/Downloads/Stk_Day_QFQ/'
+path =os.listdir(root)
+
+data = pd.read_csv('./2022-2023涨停(000)股本N连单大于700w振幅.csv',encoding='gbk')
+
+file000 = open('./2022-2023涨停(000)股本N连单大于700w振幅反包.csv','w',encoding='gbk',newline='')
+writer000 = csv.writer(file000)
+writer000.writerow(
+    ['代码','名称','日期', '昨日收盘价','开盘价','开盘涨幅','最低价','最高价涨幅','成交量','成交额','换手率','买入价格','卖出价格','昨日是否涨停','盈利','涨幅','流通市值','跟单明细','连单笔数','总金额','时间','炸板率','涨停次数','炸板次数','触板次数','炸板幅度','涨停次日溢价率','一年最高价跌幅','半年最高价跌幅','一日最高价跌幅','三日最高价跌幅','6日最高价跌幅','10日最高价跌幅','12日最高价跌幅','一个月最高价跌幅','3个月最高价跌幅','9个月最高价跌幅',
+     '1个月最低价涨幅','3个月最低价涨幅','半年最低价涨幅','9个月最低价涨幅','一年最低价涨幅','振幅','昨日振幅','10日涨幅','十日是否有涨停','十日最高价跌幅','十日涨停价跌幅'])
+for index,item in data.iterrows():
+    day = item['日期']
+    day =pd.to_datetime(day)
+    code= item['代码']+'.csv'
+
+    codeData=pd.read_csv(root+code,encoding='gbk')
+    codeData['时间'] = pd.to_datetime(codeData['时间'])
+    codeData =codeData[codeData['时间']<day]
+    codeData0=codeData.iloc[-1]
+    codeData1 = codeData.iloc[-2]
+    if len(codeData)<10:
+        continue
+    codeData=codeData[len(codeData)-10:len(codeData)]
+    codeData7 = codeData[len(codeData) - 8:len(codeData)]
+    min = codeData0['收盘价']/(codeData['最低价'].min())-1
+    max = codeData0['收盘价']/(codeData['最高价'].max())-1
+    data =codeData7
+    zhangtingItem=0
+    zhangting=0
+    for i in np.arange(len(data)):
+        if i > 0:
+            item0 = data.iloc[i - 1]
+            item1 = data.iloc[i]
+
+            if item1['收盘价'] / item0['收盘价'] > 1.08:
+                n = len(data) - i + 1
+                zhangting = zhangting + 1
+                zhangtingItem = item1
+
+        # if flag:
+        #     continue
+    zhangtingFlag=0
+    zhangtingrate = -1
+    if zhangting >0:
+        zhangtingFlag=1
+        zhangtingrate=codeData0['收盘价'] / zhangtingItem['最高价']- 1
+    times = item['跟单明细'].split('--|--')
+    times = times[len(times)-1]
+    times = times.split(',')
+    time = times[len(times) - 1]
+
+
+
+    writer000.writerow(
+        [item['代码'],
+         item['名称'],
+         item['日期'],
+         item['昨日收盘价'],
+         item['开盘价'],
+         item['开盘涨幅'],
+         item['最低价'],
+         item['最高价涨幅'],
+         item['成交量'],
+         item['成交额'],
+         item['换手率'],
+         item['买入价格'],
+         item['卖出价格'],
+         item['昨日是否涨停'],
+         item['盈利'],
+         item['涨幅'],
+         item['流通市值'],
+         item['跟单明细'],
+         item['连单笔数'],
+         item['总金额'],
+         time,
+         item['炸板率'],
+         item['涨停次数'],
+         item['炸板次数'],
+         item['触板次数'],
+         item['炸板幅度'],
+         item['涨停次日溢价率'],
+         item['一年最高价跌幅'],
+         item['半年最高价跌幅'],
+         item['一日最高价跌幅'],
+         item['三日最高价跌幅'],
+         item['6日最高价跌幅'],
+         item['10日最高价跌幅'],
+         item['12日最高价跌幅'],
+         item['一个月最高价跌幅'],
+         item['3个月最高价跌幅'],
+         item['9个月最高价跌幅'],
+         item['1个月最低价涨幅'],
+         item['3个月最低价涨幅'],
+         item['半年最低价涨幅'],
+         item['9个月最低价涨幅'],
+         item['一年最低价涨幅'],
+         item['振幅'],
+         item['昨日振幅'],
+         item['10日涨幅'],
+         zhangtingFlag,
+         max,
+         zhangtingrate
+
+         ])

10日未涨停回封/2022-202307/不复权/数据修复/市值修复.py

+import csv
+import math
+import os
+import time
+
+import numpy as np
+import pandas as pd
+root='/Users/yumingwu/Downloads/Stk_Day/'
+path =os.listdir(root)
+path.sort()
+
+
+
+guben1 = pd.read_csv('../../../../600打板/流通股本.csv',encoding='utf-8')
+guben1['变更日期']=pd.to_datetime(guben1['变更日期'])
+data = pd.read_csv('./2022涨停(000)股本N连单大于700w.csv',encoding='gbk')
+data['日期']=pd.to_datetime(data['日期'])
+with open('./2022-2023涨停(000)股本N连单大于700w市值'+'.csv','w',encoding='gbk',newline='') as csfile:
+    writer =csv.writer(csfile)
+    writer.writerow(['代码','名称','日期', '昨日收盘价','开盘价','最低价','最高价涨幅','成交量','成交额','换手率','买入价格','卖出价格','昨日是否涨停','盈利','涨幅','流通市值','跟单明细','连单笔数','总金额'])
+
+
+    for index,item in data.iterrows():
+        dataS = pd.read_csv(root + item['代码']+'.csv', encoding='gbk')
+        dataS['时间'] = pd.to_datetime(dataS['时间'])
+
+        data1 = dataS[dataS['时间'] < item['日期']]
+
+        item2 = data1.iloc[-1]
+        item3 = data1.iloc[-2]
+        code = item['代码'][2:8] + "." + item['代码'][0:2]
+
+        guben = guben1[guben1['代码'] == code]
+        guben = guben.sort_values('变更日期', ascending=False)
+
+        gubenitem1 = guben[guben['变更日期'] <= item['日期']]
+        if len(gubenitem1) == 0:
+            gubenitem1 = guben.iloc[-1]
+        else:
+            gubenitem1 = gubenitem1.iloc[0]
+
+        huanshou = item2['成交量(股)'] / gubenitem1['流通A股']
+
+        shizhi = gubenitem1['流通A股'] * item2['收盘价'] / 100000000.0
+        zhangting =0;
+        if item2['收盘价']/item3['收盘价']>1.098:
+            zhangting=1
+
+
+
+        writer.writerow([item['代码'],
+                         item['名称'],
+                         item['日期'],
+                         item['昨日收盘价'],
+                         item['开盘价'],
+                         item['最低价'],
+                         item['最高价涨幅'],
+                         item['成交量'],
+                         item['成交额'],
+                         item['换手率'],
+                         item['买入价格'],
+                         item['卖出价格'],
+                         zhangting,
+                         item['盈利'],
+                         item['涨幅'],
+                         shizhi,
+                         item['跟单明细'],
+                         item['连单笔数'],
+                         item['总金额'],
+
+
+
+
+
+                         ])

10日未涨停回封/2022-202307/不复权/数据修复/振幅.py

+import csv
+import os
+import time
+from _decimal import Decimal
+
+import numpy as np
+import pandas as pd
+root='/Users/yumingwu/Downloads/Stk_Day_QFQ/'
+path =os.listdir(root)
+
+data = pd.read_csv('./2022-2023涨停(000)股本N连单10%大于700w炸板率.csv',encoding='gbk')
+
+file000 = open('./2022-2023涨停(000)股本N连单10%大于700w振幅.csv','w',encoding='gbk',newline='')
+writer000 = csv.writer(file000)
+writer000.writerow(
+    ['代码','名称','日期', '昨日收盘价','开盘价','开盘涨幅','最低价','最高价涨幅','成交量','成交额','换手率','买入价格','卖出价格','昨日是否涨停','盈利','涨幅','流通市值','跟单明细','连单笔数','总金额','时间','炸板率','涨停次数','炸板次数','触板次数','炸板幅度','涨停次日溢价率','次日总溢价率','最近3次触板溢价','一年最高价跌幅','半年最高价跌幅','一日最高价跌幅','三日最高价跌幅','6日最高价跌幅','10日最高价跌幅','12日最高价跌幅','一个月最高价跌幅','3个月最高价跌幅','9个月最高价跌幅',
+     '1个月最低价涨幅','3个月最低价涨幅','半年最低价涨幅','9个月最低价涨幅','一年最低价涨幅','振幅','昨日振幅','10日涨幅'])
+for index,item in data.iterrows():
+    day = item['日期']
+    day =pd.to_datetime(day)
+    code= item['代码']+'.csv'
+
+    codeData=pd.read_csv(root+code,encoding='gbk')
+    codeData['时间'] = pd.to_datetime(codeData['时间'])
+    codeData =codeData[codeData['时间']<day]
+    codeData0=codeData.iloc[-1]
+    codeData1 = codeData.iloc[-2]
+    codeData=codeData[len(codeData)-10:len(codeData)]
+    min = codeData0['收盘价']/(codeData['最低价'].min())-1
+    writer000.writerow(
+        [item['代码'],
+         item['名称'],
+         item['日期'],
+         item['昨日收盘价'],
+         item['开盘价'],
+         item['开盘涨幅'],
+         item['最低价'],
+         item['最高价涨幅'],
+         item['成交量'],
+         item['成交额'],
+         item['换手率'],
+         item['买入价格'],
+         item['卖出价格'],
+         item['昨日是否涨停'],
+         item['盈利'],
+         item['涨幅'],
+         item['流通市值'],
+         item['跟单明细'],
+         item['连单笔数'],
+         item['总金额'],
+         item['时间'],
+         item['炸板率'],
+         item['涨停次数'],
+         item['炸板次数'],
+         item['触板次数'],
+         item['炸板幅度'],
+         item['涨停次日溢价率'],
+         item['次日总溢价率'],
+         item['最近3次触板溢价'],
+         item['一年最高价跌幅'],
+         item['半年最高价跌幅'],
+         item['一日最高价跌幅'],
+         item['三日最高价跌幅'],
+         item['6日最高价跌幅'],
+         item['10日最高价跌幅'],
+         item['12日最高价跌幅'],
+         item['一个月最高价跌幅'],
+         item['3个月最高价跌幅'],
+         item['9个月最高价跌幅'],
+         item['1个月最低价涨幅'],
+         item['3个月最低价涨幅'],
+         item['半年最低价涨幅'],
+         item['9个月最低价涨幅'],
+         item['一年最低价涨幅'],
+         (codeData0['最高价']-codeData0['最低价'])/codeData1['收盘价'],
+         (codeData0['最高价'] - codeData0['最低价']) / codeData0['收盘价'],
+         min
+         ])

10日未涨停回封/2022-202307/不复权/数据修复/炸板率.py

+import csv
+import os
+import time
+from _decimal import Decimal
+
+import numpy as np
+import pandas as pd
+root='/Users/yumingwu/Downloads/Stk_Day_QFQ/'
+path =os.listdir(root)
+
+data = pd.read_csv('./2022涨停(000)股本N连单10%大于700w.csv',encoding='gbk')
+
+file000 = open('./2022-2023涨停(000)股本N连单10%大于700w炸板率.csv','w',encoding='gbk',newline='')
+writer000 = csv.writer(file000)
+writer000.writerow(
+    ['代码','名称','日期', '昨日收盘价','开盘价','开盘涨幅','最低价','最高价涨幅','成交量','成交额','换手率','买入价格','卖出价格','昨日是否涨停','盈利','涨幅','流通市值','跟单明细','连单笔数','总金额','时间','炸板率','涨停次数','炸板次数','触板次数','炸板幅度','涨停次日溢价率','次日总溢价率','最近3次触板溢价','一年最高价跌幅','半年最高价跌幅','一日最高价跌幅','三日最高价跌幅','6日最高价跌幅','10日最高价跌幅','12日最高价跌幅','一个月最高价跌幅','3个月最高价跌幅','9个月最高价跌幅',
+     '1个月最低价涨幅','3个月最低价涨幅','半年最低价涨幅','9个月最低价涨幅','一年最低价涨幅'])
+for index,item in data.iterrows():
+    day = item['日期']
+    day =pd.to_datetime(day)
+    code= item['代码']+'.csv'
+
+
+    print(item['代码'])
+    codeData=pd.read_csv(root+code,encoding='gbk')
+    codeData['时间'] = pd.to_datetime(codeData['时间'])
+    codeData =codeData[codeData['时间']<day]
+    day1=day-pd.offsets.DateOffset(years=1)
+    codeData = codeData[codeData['时间'] >= day1]
+    if len(codeData)==0:
+        continue
+    day2 = day - pd.offsets.DateOffset(months=6)
+    day1_month = day - pd.offsets.DateOffset(months=1)
+    day3_month = day - pd.offsets.DateOffset(months=3)
+    day9_month = day - pd.offsets.DateOffset(months=9)
+    codeData_6_month = codeData[codeData['时间'] >= day2]
+
+    codeData_1_month = codeData[codeData['时间'] >= day1_month]
+    codeData_3_month = codeData[codeData['时间'] >= day3_month]
+    codeData_9_month = codeData[codeData['时间'] >= day9_month]
+
+    codeData1 = codeData[len(codeData)-1:len(codeData)]
+    codeData3= codeData[len(codeData) - 3:len(codeData)]
+    codeData6 = codeData[len(codeData) - 6:len(codeData)]
+    codeData9 = codeData[len(codeData) - 10:len(codeData)]
+    codeData12 = codeData[len(codeData) - 12:len(codeData)]
+    item_y=codeData.iloc[-1]
+    item_y2 = codeData.iloc[-1]
+    if item_y['收盘价'] / item_y2['收盘价'] > 1.098:
+        zhangting = 1
+    rate_year = item_y['收盘价']/codeData['最高价'].max()
+    rate_year_min = item_y['收盘价'] / codeData['最低价'].min()
+    rate_6_month = item_y['收盘价'] / codeData_6_month['最高价'].max()
+    rate_6_month_min = item_y['收盘价'] / codeData_6_month['最低价'].min()
+
+
+    rate_1_month = item_y['收盘价'] / codeData_1_month['最高价'].max()
+    rate_1_month_min = item_y['收盘价'] / codeData_1_month['最低价'].min()
+    rate_3_month = item_y['收盘价'] / codeData_3_month['最高价'].max()
+    rate_3_month_min = item_y['收盘价'] / codeData_3_month['最低价'].min()
+
+
+    rate_9_month = item_y['收盘价'] / codeData_9_month['最高价'].max()
+    rate_9_month_min = item_y['收盘价'] / codeData_9_month['最低价'].min()
+
+
+    rate_1 = item_y['收盘价'] / codeData1['最高价'].max()
+    rate_3= item_y['收盘价'] / codeData3['最高价'].max()
+    rate_6 = item_y['收盘价'] / codeData6['最高价'].max()
+    rate_9 = item_y['收盘价'] / codeData9['最高价'].max()
+    rate_12 = item_y['收盘价'] / codeData12['最高价'].max()
+
+
+    zhangtingTime=0
+    zhaBanTime=0
+    zhaBanTimefudu=0
+    zhangtingyijia=0
+    zhangtingyijiazong = 0
+    arr3 = []
+    for i in np.arange(len(codeData)):
+        if i==0:
+            continue
+        if i==len(codeData)-1:
+            continue
+        item0 = codeData.iloc[i-1]
+        item1 = codeData.iloc[i]
+        item2 = codeData.iloc[i+1]
+
+        if item1['最高价']/item0['收盘价']>1.098:
+            zhangtingyijiazong = zhangtingyijiazong + item2['开盘价'] / item1['最高价'] - 1.0014
+            arr3.append(item2['开盘价'] / item1['最高价'] - 1.0014)
+            if item1['最高价']==item1['收盘价']:
+                zhangtingTime=zhangtingTime+1
+                zhangtingyijia = zhangtingyijia + item2['开盘价'] / item1['最高价'] - 1.0014
+
+
+            else:
+                zhaBanTime=zhaBanTime+1
+                zhaBanTimefudu=zhaBanTimefudu+(item2['开盘价']/item1['最高价']-1.0014)
+
+    rate =0
+    if zhangtingTime>0 or zhaBanTime>0:
+        rate = zhaBanTime/(zhangtingTime+zhaBanTime)
+
+
+    if zhaBanTime!=0:
+        zhaBanTimefudu=zhaBanTimefudu/zhaBanTime
+    if zhangtingTime!=0:
+        zhangtingyijia=zhangtingyijia/zhangtingTime
+    if (zhangtingTime +zhaBanTime)!= 0:
+        zhangtingyijiazong = zhangtingyijiazong / (zhangtingTime+zhaBanTime)
+
+    rate3=-100
+    if len(arr3)>=3:
+        arr3 = arr3[len(arr3)-3:len(arr3)]
+        rate3 = (arr3[0]+arr3[1]+arr3[2])/3
+
+    zhangting = 0;
+
+
+
+    writer000.writerow(
+        [item['代码'],
+         item['名称'],
+         item['日期'],
+         item['昨日收盘价'],
+         item['开盘价'],
+         float(item['开盘价'])/float(item['昨日收盘价']),
+         item['最低价'],
+         item['最高价涨幅'],
+         item['成交量'],
+         item['成交额'],
+         item['换手率'],
+         item['买入价格'],
+         item['卖出价格'],
+         zhangting,
+         item['盈利'],
+         item['涨幅'],
+         item['流通市值'],
+         item['跟单明细'],
+         item['连单笔数'],
+         item['总金额'],
+         item['时间'],
+         rate,
+         zhangtingTime,
+         zhaBanTime,
+         zhangtingTime+zhaBanTime,
+         zhaBanTimefudu,
+         zhangtingyijia,
+         zhangtingyijiazong,
+         rate3,
+         rate_year,
+         rate_6_month,
+         rate_1,
+         rate_3,
+         rate_6,
+         rate_9,
+         rate_12,
+         rate_1_month,
+         rate_3_month,
+         rate_9_month,
+         rate_1_month_min,
+         rate_3_month_min,
+         rate_6_month_min,
+         rate_9_month_min,
+         rate_year_min
+
+
+         ])

10日未涨停回封/2022-202307/不复权/数据修复/盈利统计.py

+import csv
+import math
+import os
+import time
+
+import numpy as np
+import pandas as pd
+
+data = pd.read_excel('000连单.xlsx')
+
+# data =data[data['昨日是否涨停']==0]
+# data = data[data['开盘涨停']==0]
+data = data.groupby('代码')
+
+
+
+with open('./000连单统计'+'.csv','w',encoding='gbk',newline='') as csfile:
+    writer =csv.writer(csfile)
+    writer.writerow(['代码','名称','日期','盈利','总盈利','次数'])
+    for i ,item1 in data:
+
+        for index,item in item1.iterrows():
+             writer.writerow([item['代码'], item['名称'],item['日期'],item['盈利'],item1['盈利'].sum(),len(item1)])
+
+

10日未涨停回封/2022-202307/不复权/炸板率.py

+import csv
+import os
+import time
+from _decimal import Decimal
+
+import numpy as np
+import pandas as pd
+root='/Users/yumingwu/Downloads/Stk_Day_QFQ/'
+path =os.listdir(root)
+
+data = pd.read_csv('./2022涨停(000)股本2连大于700w.csv',encoding='gbk')
+
+file000 = open('./2022-2023涨停(000)股本2连大于700w炸板率.csv','w',encoding='gbk',newline='')
+writer000 = csv.writer(file000)
+writer000.writerow(
+    ['代码', '名称', '日期', '昨日收盘价', '开盘价', '最低价', '最高价涨幅', '成交量', '成交额', '换手率', '买入价格',
+     '卖出价格', '盈利', '涨幅', '流通市值', '涨停trainID', '涨停BuyOrderID', '涨停vol', '涨停amount', '大单ID',
+     '时间','炸板率','涨停次数','炸板次数','炸板幅度','涨停次日溢价率','次日总溢价率','最近3次触板溢价','一年最高价跌幅','半年最高价跌幅','一日最高价跌幅','三日最高价跌幅','6日最高价跌幅','10日最高价跌幅','12日最高价跌幅','一个月最高价跌幅','3个月最高价跌幅','9个月最高价跌幅',
+     '1个月最低价涨幅','3个月最低价涨幅','半年最低价涨幅','9个月最低价涨幅','一年最低价涨幅'])
+for index,item in data.iterrows():
+    day = item['日期']
+    print(day)
+    print( item['代码'])
+
+    day =pd.to_datetime(day)
+    code= item['代码']+'.csv'
+
+
+
+    codeData=pd.read_csv(root+code,encoding='gbk')
+    codeData['时间'] = pd.to_datetime(codeData['时间'])
+    codeData =codeData[codeData['时间']<day]
+
+
+    day1=day-pd.offsets.DateOffset(years=1)
+    codeData = codeData[codeData['时间'] >= day1]
+    day2 = day - pd.offsets.DateOffset(months=6)
+    day1_month = day - pd.offsets.DateOffset(months=1)
+    day3_month = day - pd.offsets.DateOffset(months=3)
+    day9_month = day - pd.offsets.DateOffset(months=9)
+    codeData_6_month = codeData[codeData['时间'] >= day2]
+
+    codeData_1_month = codeData[codeData['时间'] >= day1_month]
+    codeData_3_month = codeData[codeData['时间'] >= day3_month]
+    codeData_9_month = codeData[codeData['时间'] >= day9_month]
+
+    codeData1 = codeData[len(codeData)-1:len(codeData)]
+    codeData3= codeData[len(codeData) - 3:len(codeData)]
+    codeData6 = codeData[len(codeData) - 6:len(codeData)]
+    codeData9 = codeData[len(codeData) - 10:len(codeData)]
+    codeData12 = codeData[len(codeData) - 12:len(codeData)]
+
+    if len(codeData)<10:
+        continue
+    item_y=codeData.iloc[-1]
+
+    rate_year = item_y['收盘价']/codeData['最高价'].max()
+    rate_year_min = item_y['收盘价'] / codeData['最低价'].min()
+    rate_6_month = item_y['收盘价'] / codeData_6_month['最高价'].max()
+    rate_6_month_min = item_y['收盘价'] / codeData_6_month['最低价'].min()
+
+
+    rate_1_month = item_y['收盘价'] / codeData_1_month['最高价'].max()
+    rate_1_month_min = item_y['收盘价'] / codeData_1_month['最低价'].min()
+    rate_3_month = item_y['收盘价'] / codeData_3_month['最高价'].max()
+    rate_3_month_min = item_y['收盘价'] / codeData_3_month['最低价'].min()
+
+
+    rate_9_month = item_y['收盘价'] / codeData_9_month['最高价'].max()
+    rate_9_month_min = item_y['收盘价'] / codeData_9_month['最低价'].min()
+
+
+    rate_1 = item_y['收盘价'] / codeData1['最高价'].max()
+    rate_3= item_y['收盘价'] / codeData3['最高价'].max()
+    rate_6 = item_y['收盘价'] / codeData6['最高价'].max()
+    rate_9 = item_y['收盘价'] / codeData9['最高价'].max()
+    rate_12 = item_y['收盘价'] / codeData12['最高价'].max()
+
+
+    zhangtingTime=0
+    zhaBanTime=0
+    zhaBanTimefudu=0
+    zhangtingyijia=0
+    zhangtingyijiazong = 0
+    arr3 = []
+    for i in np.arange(len(codeData)):
+        if i==0:
+            continue
+        if i==len(codeData)-1:
+            continue
+        item0 = codeData.iloc[i-1]
+        item1 = codeData.iloc[i]
+        item2 = codeData.iloc[i+1]
+
+        if item1['最高价']/item0['收盘价']>1.098:
+            zhangtingyijiazong = zhangtingyijiazong + item2['开盘价'] / item1['最高价'] - 1.0014
+            arr3.append(item2['开盘价'] / item1['最高价'] - 1.0014)
+            if item1['最高价']==item1['收盘价']:
+                zhangtingTime=zhangtingTime+1
+                zhangtingyijia = zhangtingyijia + item2['开盘价'] / item1['最高价'] - 1.0014
+
+
+            else:
+                zhaBanTime=zhaBanTime+1
+                zhaBanTimefudu=zhaBanTimefudu+(item2['开盘价']/item1['最高价']-1.0014)
+
+    rate =0
+    if zhangtingTime>0 or zhaBanTime>0:
+        rate = zhaBanTime/(zhangtingTime+zhaBanTime)
+
+
+    if zhaBanTime!=0:
+        zhaBanTimefudu=zhaBanTimefudu/zhaBanTime
+    if zhangtingTime!=0:
+        zhangtingyijia=zhangtingyijia/zhangtingTime
+    if (zhangtingTime +zhaBanTime)!= 0:
+        zhangtingyijiazong = zhangtingyijiazong / (zhangtingTime+zhaBanTime)
+
+    rate3=-100
+    if len(arr3)>=3:
+        arr3 = arr3[len(arr3)-3:len(arr3)]
+        rate3 = (arr3[0]+arr3[1]+arr3[2])/3
+
+
+    writer000.writerow(
+        [item['代码'],
+         item['名称'],
+         item['日期'],
+         item['昨日收盘价'],
+         item['开盘价'],
+         item['最低价'],
+         item['最高价涨幅'],
+         item['成交量'],
+         item['成交额'],
+         item['换手率'],
+         item['买入价格'],
+         item['卖出价格'],
+         item['盈利'],
+         item['涨幅'],
+         item['流通市值'],
+         item['涨停trainID'],
+         item['涨停BuyOrderID'],
+         item['涨停vol'],
+         item['涨停amount'],
+         item['大单ID'],
+         item['时间'],
+         rate,
+         zhangtingTime,
+         zhaBanTime,
+         zhaBanTimefudu,
+         zhangtingyijia,
+         zhangtingyijiazong,
+         rate3,
+         rate_year,
+         rate_6_month,
+         rate_1,
+         rate_3,
+         rate_6,
+         rate_9,
+         rate_12,
+         rate_1_month,
+         rate_3_month,
+         rate_9_month,
+         rate_1_month_min,
+         rate_3_month_min,
+         rate_6_month_min,
+         rate_9_month_min,
+         rate_year_min
+
+
+         ])

10日未涨停回封/2022-202307/反包.py

@@ -10,7 +10,7 @@ path =os.listdir(root)
 
 data = pd.read_csv('./2022未涨停(000)股本首笔大单大于700w-b1-8%type第一个为B炸板率(振幅).csv',encoding='utf-8')
 
-file000 = open('./2022未涨停(000)股本首笔大单大于700w-b1-8%type第一个为B炸板率(振幅)反包10.csv','w',encoding='gbk',newline='')
+file000 = open('./2022未涨停(000)股本首笔大单大于700w-b1-8%type第一个为B炸板率(振幅)反包.csv','w',encoding='gbk',newline='')
 writer000 = csv.writer(file000)
 writer000.writerow(
     ['代码', '名称', '日期', '昨日收盘价', '开盘价', '最低价', '最高价涨幅', '成交量', '成交额', '换手率', '买入价格',

10日未涨停回封/2022-202307/反包时间.py

+import csv
+import os
+import time
+from _decimal import Decimal
+
+import numpy as np
+import pandas as pd
+root='/Users/yumingwu/Downloads/Stk_Day_QFQ/'
+path =os.listdir(root)
+
+data = pd.read_csv('./2023涨停(000)股本涨停时间炸板率(振幅).csv',encoding='gbk')
+
+file000 = open('./2023涨停(000)股本涨停时间炸板率(振幅)反包.csv','w',encoding='gbk',newline='')
+writer000 = csv.writer(file000)
+writer000.writerow(
+    ['代码', '名称', '日期', '昨日收盘价', '开盘价', '最低价', '最高价涨幅', '成交量', '成交额', '换手率', '买入价格',
+     '卖出价格', '盈利', '涨幅', '流通市值', '涨停trainID', '涨停BuyOrderID', '涨停vol',
+     '时间','炸板率','涨停次数','炸板次数','炸板幅度','涨停次日溢价率','振幅','昨日振幅','昨日跌幅','10日涨幅','十日是否有涨停','十日最高价跌幅','十日涨停价跌幅'])
+for index,item in data.iterrows():
+    day = item['日期']
+    day =pd.to_datetime(day)
+    code= item['代码']+'.csv'
+
+    codeData=pd.read_csv(root+code,encoding='gbk')
+    codeData['时间'] = pd.to_datetime(codeData['时间'])
+    codeData =codeData[codeData['时间']<day]
+    codeData0=codeData.iloc[-1]
+    codeData1 = codeData.iloc[-2]
+    if len(codeData)<10:
+        continue
+    codeData=codeData[len(codeData)-10:len(codeData)]
+    codeData7 = codeData[len(codeData) -8:len(codeData)]
+    min = codeData0['收盘价']/(codeData['最低价'].min())-1
+    max = codeData0['收盘价']/(codeData['最高价'].max())-1
+    data =codeData7
+    zhangtingItem=0
+    zhangting=0
+    for i in np.arange(len(data)):
+        if i > 0:
+            item0 = data.iloc[i - 1]
+            item1 = data.iloc[i]
+
+            if item1['收盘价'] / item0['收盘价'] > 1.08:
+                n = len(data) - i + 1
+                zhangting = zhangting + 1
+                zhangtingItem = item1
+
+        # if flag:
+        #     continue
+    zhangtingFlag=0
+    zhangtingrate = -1
+    if zhangting >0:
+        zhangtingFlag=1
+        zhangtingrate=codeData0['收盘价'] / zhangtingItem['最高价']- 1
+    writer000.writerow(
+        [item['代码'],
+         item['名称'],
+         item['日期'],
+         item['昨日收盘价'],
+         item['开盘价'],
+         item['最低价'],
+         item['最高价涨幅'],
+         item['成交量'],
+         item['成交额'],
+         item['换手率'],
+         item['买入价格'],
+         item['卖出价格'],
+         item['盈利'],
+         item['涨幅'],
+         item['流通市值'],
+         item['涨停trainID'],
+         item['涨停BuyOrderID'],
+         item['涨停vol'],
+         item['时间'],
+         item['炸板率'],
+         item['涨停次数'],
+         item['炸板次数'],
+         item['炸板幅度'],
+         item['涨停次日溢价率'],
+         (codeData0['最高价']-codeData0['最低价'])/codeData1['收盘价'],
+         (codeData0['最高价'] - codeData0['最低价']) / codeData0['收盘价'],
+         codeData0['收盘价']/codeData1['收盘价']-1,
+         min,
+         zhangtingFlag,
+         max,
+         zhangtingrate
+
+         ])

10日未涨停回封/2022-202307/振幅.py

@@ -8,7 +8,7 @@ import pandas as pd
 root='/Users/yumingwu/Downloads/Stk_Day_QFQ/'
 path =os.listdir(root)
 
-data = pd.read_csv('./2022-202307未涨停(000)股本首笔大单大于700w-b1-8%type第一个为B炸板率.csv',encoding='gbk')
+data = pd.read_csv('./2023涨停(000)股本涨停时间炸板率.csv',encoding='gbk')
 
 file000 = open('./2022-202307未涨停(000)股本首笔大单大于700w-b1-8%type第一个为B(振幅)3.csv','w',encoding='gbk',newline='')
 writer000 = csv.writer(file000)

10日未涨停回封/2022-202307/振幅时间.py

+import csv
+import os
+import time
+from _decimal import Decimal
+
+import numpy as np
+import pandas as pd
+root='/Users/yumingwu/Downloads/Stk_Day_QFQ/'
+path =os.listdir(root)
+
+data = pd.read_csv('./2023涨停(000)股本涨停时间炸板率.csv',encoding='gbk')
+
+file000 = open('./2023涨停(000)股本涨停时间炸板率(振幅).csv','w',encoding='gbk',newline='')
+writer000 = csv.writer(file000)
+writer000.writerow(
+    ['代码', '名称', '日期', '昨日收盘价', '开盘价', '最低价', '最高价涨幅', '成交量', '成交额', '换手率', '买入价格',
+     '卖出价格', '盈利', '涨幅', '流通市值', '涨停trainID', '涨停BuyOrderID', '涨停vol',
+     '时间','炸板率','涨停次数','炸板次数','炸板幅度','涨停次日溢价率','次日总溢价率','最近3次触板溢价','一年最高价跌幅','半年最高价跌幅','一日最高价跌幅','三日最高价跌幅','6日最高价跌幅','9日最高价跌幅','12日最高价跌幅','一个月最高价跌幅','3个月最高价跌幅','9个月最高价跌幅','1个月最低价涨幅','3个月最低价涨幅','半年最低价涨幅','9个月最低价涨幅','一年最低价涨幅','振幅','昨日振幅','10日涨幅'])
+for index,item in data.iterrows():
+    day = item['日期']
+    day =pd.to_datetime(day)
+    code= item['代码']+'.csv'
+
+    codeData=pd.read_csv(root+code,encoding='gbk')
+    codeData['时间'] = pd.to_datetime(codeData['时间'])
+    codeData =codeData[codeData['时间']<day]
+    codeData0=codeData.iloc[-1]
+    codeData1 = codeData.iloc[-2]
+    codeData=codeData[len(codeData)-10:len(codeData)]
+    min = codeData0['收盘价']/(codeData['最低价'].min())-1
+    writer000.writerow(
+        [item['代码'],
+         item['名称'],
+         item['日期'],
+         item['昨日收盘价'],
+         item['开盘价'],
+         item['最低价'],
+         item['最高价涨幅'],
+         item['成交量'],
+         item['成交额'],
+         item['换手率'],
+         item['买入价格'],
+         item['卖出价格'],
+         item['盈利'],
+         item['涨幅'],
+         item['流通市值'],
+         item['涨停trainID'],
+         item['涨停BuyOrderID'],
+         item['涨停vol'],
+         item['时间'],
+         item['炸板率'],
+         item['涨停次数'],
+         item['炸板次数'],
+         item['炸板幅度'],
+         item['涨停次日溢价率'],
+         item['次日总溢价率'],
+         item['最近3次触板溢价'],
+         item['一年最高价跌幅'],
+         item['半年最高价跌幅'],
+         item['一日最高价跌幅'],
+         item['三日最高价跌幅'],
+         item['6日最高价跌幅'],
+         item['9日最高价跌幅'],
+         item['12日最高价跌幅'],
+         item['一个月最高价跌幅'],
+         item['3个月最高价跌幅'],
+         item['9个月最高价跌幅'],
+         item['1个月最低价涨幅'],
+         item['3个月最低价涨幅'],
+         item['半年最低价涨幅'],
+         item['9个月最低价涨幅'],
+         item['一年最低价涨幅'],
+         (codeData0['最高价']-codeData0['最低价'])/codeData1['收盘价'],
+         (codeData0['最高价'] - codeData0['最低价']) / codeData0['收盘价'],
+         min
+         ])

10日未涨停回封/2022-202307/涨停时间.py

+import csv
+import math
+import os
+import time
+
+import numpy as np
+import pandas as pd
+root='/Volumes/Elements SE/逐笔202301-03/'
+
+
+data=pd.read_csv('./2023未涨停(000)股本.csv',encoding='gbk')
+data['日期']=pd.to_datetime(data['日期'])
+data = data.groupby('日期')
+
+name = pd.read_excel('/Users/yumingwu/Desktop/沪深A股.xlsx')
+with open('./2023涨停(000)股本涨停时间'+'.csv','w',encoding='gbk',newline='') as csfile:
+    writer =csv.writer(csfile)
+    writer.writerow(['代码','名称','日期', '昨日收盘价','开盘价','最低价','最高价涨幅','成交量','成交额','换手率','买入价格','卖出价格','盈利','涨幅','流通市值','涨停trainID','涨停BuyOrderID','涨停vol','时间'])
+    for day,dataR in data:
+
+        print(day)
+        # if(day>=pd.to_datetime('2023-07-19')):
+        #     continue
+        daystr= day.strftime("%Y-%m-%d")
+
+        for index,item in dataR.iterrows():
+
+            # if item['代码']!='SZ002851':
+            #     continue
+            if os.path.exists(root + daystr + '/' + item['代码'][2:8] + '.csv') == False:
+                print(root + daystr + '/' + item['代码'][2:8] + '.csv')
+                continue
+
+            zhubiData= pd.read_csv(root+daystr+'/'+item['代码'][2:8]+'.csv')
+            zhubiData=zhubiData[zhubiData['Time']>'09:25:00']
+            zhubiData['amount']=zhubiData['Volume']*zhubiData['BuyOrderPrice']
+            zhangtingjia=zhubiData['BuyOrderPrice'].max()
+            maxPrice= zhubiData['Price'].max()
+            maxPriceItem = zhubiData[zhubiData['Price']>=maxPrice]
+            maxPriceItem=maxPriceItem.iloc[0]
+
+            zhangtingdata = zhubiData[zhubiData['BuyOrderID']==maxPriceItem['BuyOrderID']]
+
+            num = math.floor(2000000 / item['买入价格'] / 100) * 100
+            codename = item['代码'][2:8] + "." + item['代码'][0:2]
+
+            codename = name[name['股票代码'] == codename]
+            if len(codename) == 0:
+                codename = ''
+            else:
+                codename = codename.iloc[0]['股票简称']
+
+            if (maxPrice / item['昨日收盘价'] > 1.08 ):
+                writer.writerow(
+                    [item['代码'],
+                     codename,
+                     item['日期'],
+                     item['昨日收盘价'],
+                     item['开盘价'],
+                     item['最低价'],
+                     item['最高价涨幅'],
+                     item['成交量'],
+                     item['成交额'],
+                     item['换手率'],
+                     item['买入价格'],
+                     item['卖出价格'],
+                     num * (item['卖出价格'] - item['买入价格']) - item['买入价格'] * num * 0.0002 - item[
+                         '卖出价格'] * num * 0.0012,
+
+                     item['涨幅'],
+                     item['流通市值'],
+                     maxPriceItem['TranID'],
+                     maxPriceItem['BuyOrderID'],
+                     zhangtingdata['Volume'].sum(),
+                     maxPriceItem['Time']
+
+                     ])
+
+
+
+

10日未涨停回封/2022-202307/炸板率1.py

@@ -8,14 +8,14 @@ import pandas as pd
 root='/Users/yumingwu/Downloads/Stk_Day_QFQ/'
 path =os.listdir(root)
 
-data = pd.read_csv('./2022未涨停(000)股本首笔大单大于700w-b1-8%type第一个为B炸板率(振幅).csv',encoding='utf-8')
+data = pd.read_csv('./2023涨停(000)股本涨停时间.csv',encoding='utf-8')
 
-file000 = open('./2022-202307未涨停(000)股本首笔大单大于700w-b1-8%type第一个为B炸板率.csv','w',encoding='gbk',newline='')
+file000 = open('./2023涨停(000)股本涨停时间炸板率.csv','w',encoding='gbk',newline='')
 writer000 = csv.writer(file000)
 writer000.writerow(
     ['代码', '名称', '日期', '昨日收盘价', '开盘价', '最低价', '最高价涨幅', '成交量', '成交额', '换手率', '买入价格',
      '卖出价格', '盈利', '涨幅', '流通市值', '涨停trainID', '涨停BuyOrderID', '涨停vol', '涨停amount', '大单ID',
-     '时间','炸板率','涨停次数','炸板次数','炸板幅度','涨停次日溢价率','次日总溢价率','最近3次触板溢价','一年最高价跌幅','半年最高价跌幅','一日最高价跌幅','三日最高价跌幅','6日最高价跌幅','9日最高价跌幅','12日最高价跌幅','一个月最高价跌幅','3个月最高价跌幅','9个月最高价跌幅',
+     '时间','炸板率','涨停次数','炸板次数','炸板幅度','涨停次日溢价率','次日总溢价率','最近3次触板溢价','一年最高价跌幅','半年最高价跌幅','一日最高价跌幅','三日最高价跌幅','6日最高价跌幅','10日最高价跌幅','12日最高价跌幅','一个月最高价跌幅','3个月最高价跌幅','9个月最高价跌幅',
      '1个月最低价涨幅','3个月最低价涨幅','半年最低价涨幅','9个月最低价涨幅','一年最低价涨幅'])
 for index,item in data.iterrows():
     day = item['日期']
@@ -42,7 +42,7 @@ for index,item in data.iterrows():
     codeData1 = codeData[len(codeData)-1:len(codeData)]
     codeData3= codeData[len(codeData) - 3:len(codeData)]
     codeData6 = codeData[len(codeData) - 6:len(codeData)]
-    codeData9 = codeData[len(codeData) - 9:len(codeData)]
+    codeData9 = codeData[len(codeData) - 10:len(codeData)]
     codeData12 = codeData[len(codeData) - 12:len(codeData)]
     item_y=codeData.iloc[-1]
     rate_year = item_y['收盘价']/codeData['最高价'].max()

10日未涨停回封/2022-202307/炸板率时间.py

+import csv
+import os
+import time
+from _decimal import Decimal
+
+import numpy as np
+import pandas as pd
+root='/Users/yumingwu/Downloads/Stk_Day_QFQ/'
+path =os.listdir(root)
+
+data = pd.read_csv('./2023涨停(000)股本涨停时间.csv',encoding='utf-8')
+
+file000 = open('./2023涨停(000)股本涨停时间炸板率.csv','w',encoding='gbk',newline='')
+writer000 = csv.writer(file000)
+writer000.writerow(
+    ['代码', '名称', '日期', '昨日收盘价', '开盘价', '最低价', '最高价涨幅', '成交量', '成交额', '换手率', '买入价格',
+     '卖出价格', '盈利', '涨幅', '流通市值', '涨停trainID', '涨停BuyOrderID', '涨停vol',
+     '时间','炸板率','涨停次数','炸板次数','炸板幅度','涨停次日溢价率','次日总溢价率','最近3次触板溢价','一年最高价跌幅','半年最高价跌幅','一日最高价跌幅','三日最高价跌幅','6日最高价跌幅','9日最高价跌幅','12日最高价跌幅','一个月最高价跌幅','3个月最高价跌幅','9个月最高价跌幅',
+     '1个月最低价涨幅','3个月最低价涨幅','半年最低价涨幅','9个月最低价涨幅','一年最低价涨幅'])
+for index,item in data.iterrows():
+    day = item['日期']
+    day =pd.to_datetime(day)
+    code= item['代码']+'.csv'
+
+
+
+    codeData=pd.read_csv(root+code,encoding='gbk')
+    codeData['时间'] = pd.to_datetime(codeData['时间'])
+    codeData =codeData[codeData['时间']<day]
+    day1=day-pd.offsets.DateOffset(years=1)
+    codeData = codeData[codeData['时间'] >= day1]
+    day2 = day - pd.offsets.DateOffset(months=6)
+    day1_month = day - pd.offsets.DateOffset(months=1)
+    day3_month = day - pd.offsets.DateOffset(months=3)
+    day9_month = day - pd.offsets.DateOffset(months=9)
+    codeData_6_month = codeData[codeData['时间'] >= day2]
+
+    codeData_1_month = codeData[codeData['时间'] >= day1_month]
+    codeData_3_month = codeData[codeData['时间'] >= day3_month]
+    codeData_9_month = codeData[codeData['时间'] >= day9_month]
+
+    codeData1 = codeData[len(codeData)-1:len(codeData)]
+    codeData3= codeData[len(codeData) - 3:len(codeData)]
+    codeData6 = codeData[len(codeData) - 6:len(codeData)]
+    codeData9 = codeData[len(codeData) - 9:len(codeData)]
+    codeData12 = codeData[len(codeData) - 12:len(codeData)]
+    item_y=codeData.iloc[-1]
+    rate_year = item_y['收盘价']/codeData['最高价'].max()
+    rate_year_min = item_y['收盘价'] / codeData['最低价'].min()
+    rate_6_month = item_y['收盘价'] / codeData_6_month['最高价'].max()
+    rate_6_month_min = item_y['收盘价'] / codeData_6_month['最低价'].min()
+
+
+    rate_1_month = item_y['收盘价'] / codeData_1_month['最高价'].max()
+    rate_1_month_min = item_y['收盘价'] / codeData_1_month['最低价'].min()
+    rate_3_month = item_y['收盘价'] / codeData_3_month['最高价'].max()
+    rate_3_month_min = item_y['收盘价'] / codeData_3_month['最低价'].min()
+
+
+    rate_9_month = item_y['收盘价'] / codeData_9_month['最高价'].max()
+    rate_9_month_min = item_y['收盘价'] / codeData_9_month['最低价'].min()
+
+
+    rate_1 = item_y['收盘价'] / codeData1['最高价'].max()
+    rate_3= item_y['收盘价'] / codeData3['最高价'].max()
+    rate_6 = item_y['收盘价'] / codeData6['最高价'].max()
+    rate_9 = item_y['收盘价'] / codeData9['最高价'].max()
+    rate_12 = item_y['收盘价'] / codeData12['最高价'].max()
+
+
+    zhangtingTime=0
+    zhaBanTime=0
+    zhaBanTimefudu=0
+    zhangtingyijia=0
+    zhangtingyijiazong = 0
+    arr3 = []
+    for i in np.arange(len(codeData)):
+        if i==0:
+            continue
+        if i==len(codeData)-1:
+            continue
+        item0 = codeData.iloc[i-1]
+        item1 = codeData.iloc[i]
+        item2 = codeData.iloc[i+1]
+
+        if item1['最高价']/item0['收盘价']>1.098:
+            zhangtingyijiazong = zhangtingyijiazong + item2['开盘价'] / item1['最高价'] - 1.0014
+            arr3.append(item2['开盘价'] / item1['最高价'] - 1.0014)
+            if item1['最高价']==item1['收盘价']:
+                zhangtingTime=zhangtingTime+1
+                zhangtingyijia = zhangtingyijia + item2['开盘价'] / item1['最高价'] - 1.0014
+
+
+            else:
+                zhaBanTime=zhaBanTime+1
+                zhaBanTimefudu=zhaBanTimefudu+(item2['开盘价']/item1['最高价']-1.0014)
+
+    rate =0
+    if zhangtingTime>0 or zhaBanTime>0:
+        rate = zhaBanTime/(zhangtingTime+zhaBanTime)
+
+
+    if zhaBanTime!=0:
+        zhaBanTimefudu=zhaBanTimefudu/zhaBanTime
+    if zhangtingTime!=0:
+        zhangtingyijia=zhangtingyijia/zhangtingTime
+    if (zhangtingTime +zhaBanTime)!= 0:
+        zhangtingyijiazong = zhangtingyijiazong / (zhangtingTime+zhaBanTime)
+
+    rate3=-100
+    if len(arr3)>=3:
+        arr3 = arr3[len(arr3)-3:len(arr3)]
+        rate3 = (arr3[0]+arr3[1]+arr3[2])/3
+
+
+    writer000.writerow(
+        [item['代码'],
+         item['名称'],
+         item['日期'],
+         item['昨日收盘价'],
+         item['开盘价'],
+         item['最低价'],
+         item['最高价涨幅'],
+         item['成交量'],
+         item['成交额'],
+         item['换手率'],
+         item['买入价格'],
+         item['卖出价格'],
+         item['盈利'],
+         item['涨幅'],
+         item['流通市值'],
+         item['涨停trainID'],
+         item['涨停BuyOrderID'],
+         item['涨停vol'],
+         item['时间'],
+         rate,
+         zhangtingTime,
+         zhaBanTime,
+         zhaBanTimefudu,
+         zhangtingyijia,
+         zhangtingyijiazong,
+         rate3,
+         rate_year,
+         rate_6_month,
+         rate_1,
+         rate_3,
+         rate_6,
+         rate_9,
+         rate_12,
+         rate_1_month,
+         rate_3_month,
+         rate_9_month,
+         rate_1_month_min,
+         rate_3_month_min,
+         rate_6_month_min,
+         rate_9_month_min,
+         rate_year_min
+
+
+         ])

10日未涨停回封/低价股/低价股大单.py

@@ -8,12 +8,12 @@ import pandas as pd
 root='/Volumes/Elements SE/逐笔202301-03/'
 
 
-data=pd.read_csv('../股本/2023-1-7月-10日未涨停(000)股本.csv',encoding='gbk')
+data=pd.read_csv('../2022-202307/2023未涨停(000)股本.csv',encoding='gbk')
 data['日期']=pd.to_datetime(data['日期'])
 data = data.groupby('日期')
 
 name = pd.read_csv('../../N字反包/codeName.csv')
-with open('./2023-1-7月-10日未涨停低价股大于9900手-b1-8%type第一个为B'+'.csv','w',encoding='gbk',newline='') as csfile:
+with open('./2023涨停(000)低价股大于5000手-b1-8%type第一个为B'+'.csv','w',encoding='gbk',newline='') as csfile:
     writer =csv.writer(csfile)
     writer.writerow(['代码','名称','日期', '昨日收盘价','开盘价','最低价','最高价涨幅','成交量','成交额','换手率','买入价格','卖出价格','盈利','涨幅','流通市值','涨停trainID','涨停BuyOrderID','涨停vol','涨停Volume','大单ID','时间'])
     for day,dataR in data:
@@ -48,7 +48,7 @@ with open('./2023-1-7月-10日未涨停低价股大于9900手-b1-8%type第一个
             zhangtingjiaData=zhangtingjiaData.groupby('BuyOrderID')
             zhangtingjiaData1=pd.DataFrame()
             for id, zhangtingjiaDataItem in zhangtingjiaData:
-                if zhangtingjiaDataItem['Volume'].sum()<990000:
+                if zhangtingjiaDataItem['Volume'].sum()<500000:
                     continue
 
                 p0 = zhangtingjiaDataItem.iloc[0]
@@ -66,7 +66,7 @@ with open('./2023-1-7月-10日未涨停低价股大于9900手-b1-8%type第一个
                 Volume = zhangtingjiaDataItem['Volume'].sum()
 
 
-                if Volume<990000:
+                if Volume<500000:
                     continue
 
 
@@ -86,7 +86,7 @@ with open('./2023-1-7月-10日未涨停低价股大于9900手-b1-8%type第一个
                 codename = int(codename)
                 codename = name[name['代码']==codename]
                 codename =codename.iloc[0]
-                if (Volume >= 990000 and p0/item['昨日收盘价']>1.08 and type == 'B'):
+                if (Volume >=500000 and p0/item['昨日收盘价']>1.08 and type == 'B'):
                     writer.writerow(
                         [item['代码'],
                          codename['名称'],

600打板/反包.py

@@ -10,7 +10,7 @@ path =os.listdir(root)
 
 data = pd.read_csv('./2022涨停(600)股本首笔大单大于700w-b1-8%type第一个为B炸板率(振幅).csv',encoding='gbk')
 
-file000 = open('./2022涨停(600)股本首笔大单大于700w-b1-8%type第一个为B炸板率(振幅)反包9.csv','w',encoding='gbk',newline='')
+file000 = open('./2022涨停(600)股本首笔大单大于700w-b1-8%type第一个为B炸板率(振幅)反包10.csv','w',encoding='gbk',newline='')
 writer000 = csv.writer(file000)
 writer000.writerow(
     ['代码', '名称', '日期', '昨日收盘价', '开盘价', '最低价', '最高价涨幅', '成交量', '成交额', '换手率', '买入价格',
@@ -29,7 +29,7 @@ for index,item in data.iterrows():
     if len(codeData)<10:
         continue
     codeData=codeData[len(codeData)-10:len(codeData)]
-    codeData7 = codeData[len(codeData) - 9:len(codeData)]
+    codeData7 = codeData[len(codeData) - 10:len(codeData)]
     min = codeData0['收盘价']/(codeData['最低价'].min())-1
     max = codeData0['收盘价']/(codeData['最高价'].max())-1
     data =codeData7