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这个是改进后的score排序刷票 2023.918¶

结合实时人气表做交集用板块强度前3板块的主力买入排序前15的股票做池子¶

买入人气靠前的股票¶

不按照人气排名来分类因为发现用rank排名人气效果很差，直接用人气指数值效果会更好¶

这里不知道怎么读取个股热度指数的数据类似于问财的个股热度开盘啦没有¶

这里叠加了涨停和溢价基因作为股性，用溢价>5%排序其实感觉跟大单差不多，但是我感觉比单纯大单更好，这个有一个复盘了历时数据K线的功能，可以看到这个股票的股性如何，坑不坑人¶

有一定参考意义，¶

总结这个版本实时刷票就是大单+ 人气+股性还缺一个五线谱和K线形态¶

---- 去除了 st 并且做了一个遗漏表对照，用csv存储历史刷票记录，并标记时间防止丢失¶

In [ ]:

data_levle2[data_levle2['capital'].str.contains('吃|砸')]

In [ ]:

import numpy as np 
import requests
import time
import json
import pandas as pd
timestamp = int(time.time() * 1000)


headers= {
    "Accept": "*/*", 
    "Cookie":'v=AzKBM_yJg0Q6Gb94nm_QJ70Jh3IRwzZdaMcqgfwLXuXQj90t5FOGbThXep7P; hxmPid=sns_index_578803804.user.my.all.yes.show; IFUserCookieKey={"userid":"578803804","escapename":"mx_578803804"}; escapename=mx_578803804; ticket=c1607d10a99760a8b17b29c6384d7c93; u_name=mx_578803804; user=MDpteF81Nzg4MDM4MDQ6Ok5vbmU6NTAwOjU4ODgwMzgwNDo1LDEsNTs2LDEsNTs3LDExMTExMTExMTExMCw1OzgsMTExMTAxMTEwMDAwMTExMTEwMDEwMDEwMDEwMDAwMDAsNTszMywwMDAxMDAwMDAwMDAsNTszNiwxMDAxMTExMTAwMDAxMTAwMTAxMTExMTEsNTs0NiwwMDAwMTExMTEwMDAwMDExMTExMTExMTEsNTs1MSwxMTAwMDAwMDAwMDAwMDAwLDU7NTgsMDAwMDAwMDAwMDAwMDAwMDEsNTs3OCwxLDU7ODcsMDAwMDAwMDAwMDAwMDAwMDAwMDEwMDAwLDU7NDQsMTEsNDA7MSwxMDEsNDA7MiwxLDQwOzMsMSw0MDsxMDIsMSw0MDoxNjo6OjU3ODgwMzgwNDoxNjk1MTM0MjIxOjo6MTYxOTYxNjYwMDo1NTM3OTowOjFmYjMwNTk3YjU5NzhmNDI3ZDYxZjcxMTg3NGQyMjY0NDo6MQ%3D%3D; user_status=0; userid=578803804; utk=4a5aaf35f18c80270571f93e314ba42e',
    "User-Agent":"IHexin/11.30.81 (cn.com.10jqka.IHexin; build:11.30.82; iOS 17.0.0) Alamofire/5.4.0",
    "Accept-Language": "zh-Hans-CN;q=1.0, en-CN;q=0.9, zh-Hans;q=0.8", 
    "Accept-Encoding": "br;q=1.0, gzip;q=0.9, deflate;q=0.8",  
    "Connection":"keep-alive"
    }

data_levle2 = pd.DataFrame()
random_sleep = np.abs(np.random.normal(loc=0.6, scale=0.8, size=(1, 5)))[0]
for i  in range(5):
    url = 'https://dq.10jqka.com.cn/fuyao/b2cweb_l2/l2/v1/l2/dde/stock/monitor?_=%s&page_num={}&page_size=20&reqLockKey=reqMonitorApi'%timestamp
    response = requests.get(url.format(i,),headers=headers)
    rst = pd.DataFrame(json.loads(response.text)['data']['list'])
    #print(random_sleep[i])
    #print(rst)
    
    data_levle2 = pd.concat([data_levle2, rst], ignore_index=True)
    time.sleep(random_sleep[i])
data_levle2
#for i in range(0,5,1):
#     response = requests.get(url,headers=headers)
#     rst = pd.DataFrame(json.loads(response.text)['data']['list'])
#     rst

In [2]:

import datetime
import time
import requests
import numpy as np
import pandas as pd
from pyecharts.charts import *
from pyecharts import options as opts
import json 
def get_dde_hq(stock_id,src='kpl'):
    try:
        if src=='ths':
            headers= {
        "Accept": "*/*", 
        "Cookie":'v=AzKBM_yJg0Q6Gb94nm_QJ70Jh3IRwzZdaMcqgfwLXuXQj90t5FOGbThXep7P; hxmPid=sns_index_578803804.user.my.all.yes.show; IFUserCookieKey={"userid":"578803804","escapename":"mx_578803804"}; escapename=mx_578803804; ticket=c1607d10a99760a8b17b29c6384d7c93; u_name=mx_578803804; user=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%3D%3D; user_status=0; userid=578803804; utk=4a5aaf35f18c80270571f93e314ba42e',
        "User-Agent":"IHexin/11.30.81 (cn.com.10jqka.IHexin; build:11.30.82; iOS 17.0.0) Alamofire/5.4.0",
        "Accept-Language": "zh-Hans-CN;q=1.0, en-CN;q=0.9, zh-Hans;q=0.8", 
        "Accept-Encoding": "br;q=1.0, gzip;q=0.9, deflate;q=0.8",  
        "Connection":"keep-alive"
        }

            data_levle2 = pd.DataFrame()
            random_sleep = np.abs(np.random.normal(loc=0.6, scale=0.8, size=(1, 5)))[0]
            timestamp = str(int(time.time() * 1000))
            url ='https://vaserviece.10jqka.com.cn/Level2/index.php?op=mainMonitorDetail&_={}&stockcode={}&userid=578803804'.format(timestamp,stock_id)
            response = requests.get(url,headers=headers)
            rst = pd.DataFrame(json.loads(response.text)['list'])
            bs_map = {'1':1,'2':-1}
            rst['成交量'] = rst['volume'].apply(lambda x:int(x.replace('手','00')))
            rst = rst[['tradetype','成交量','money','avgprice','ctime']]
            rst.columns = ['买卖类型','成交量','成交金额','价格','时间']
        elif src=='kpl':
            #第一步先获取实时大单条数
            u='https://apphq.longhuvip.com/w1/api/index.php?Order=0&st=20&a=GetMainMonitor_w30&c=StockYiDongKanPan&PhoneOSNew=1&DeviceID=00000000-296c-20ad-0000-00003eb74e84&VerSion=5.7.0.12&Token=4e7fa8458a2add3f14a50ca79e863772&Index=0&Money=2&apiv=w31&StockID=%s&UserID=1973778&IsBS=0&'%stock_id
            rst = requests.get(u).text
            rst = json.loads(rst)
            #第二步，获取当前所有大单数据
            u='https://apphq.longhuvip.com/w1/api/index.php?Order=0&st=%s&a=GetMainMonitor_w30&c=StockYiDongKanPan&PhoneOSNew=1&DeviceID=00000000-296c-20ad-0000-00003eb74e84&VerSion=5.7.0.12&Token=4e7fa8458a2add3f14a50ca79e863772&Index=0&Money=2&apiv=w31&StockID=%s&UserID=1973778&IsBS=0&'%(rst['Total'],stock_id)
            rst = requests.get(u).text
            rst = json.loads(rst)
            # --------大单这里记得要跟下面的   longhu_ss_data = pd.DataFrame(np.array(longhu_ss_data['trend'])[:,:5]).head(2) 是9:31分 相统一 不然会出现未来数据
            rst = pd.DataFrame(rst['List'])
            bs_map = {'1':-1,'2':1,'3':1,'4':-1}
            rst.columns = ['买卖类型','时间戳','成交量','成交金额','价格','时间']
            
        rst['成交金额'] = rst['成交金额'].astype(int)
        rst['成交量'] = rst['成交量'].astype(int)
        rst['价格'] = rst['价格'].astype(float)
        rst['买卖类型'] = rst['买卖类型'].map(bs_map)
        rst['分钟大单净额'] = rst['成交金额']*rst['买卖类型']
        rst['成交量'] = rst['成交量']*rst['买卖类型']
        rst['分钟'] = rst['时间'].apply(lambda x:int(str(x)[-8:-3].replace(':','')))
        rst_buy = rst[rst['买卖类型']==1]
        rst_sell = rst[rst['买卖类型']==-1]

        rst_buyall= rst_buy.groupby('分钟').agg({'买卖类型':'sum','成交金额':'sum','成交量':'sum'}).reset_index()
        rst_sellall = rst_sell.groupby('分钟').agg({'买卖类型':'sum','成交金额':'sum','成交量':'sum'}).reset_index()
        rst = pd.merge(rst_buyall,rst_sellall,on='分钟',how='outer')
        rst = rst.fillna(0)
        rst.columns = ['时间','分钟大单买笔数','分钟大单买额','分钟大单买量','分钟大单卖笔数','分钟大单卖额','分钟大单卖量']
        rst['分钟净流笔数'] = rst['分钟大单买笔数']-rst['分钟大单卖笔数']
        rst['分钟净流金额'] = rst['分钟大单买额']-rst['分钟大单卖额']

        rst_buy300 = rst_buy[rst_buy['成交金额']>=3e6].groupby('分钟').agg({'买卖类型':'sum','成交金额':'sum','成交量':'sum'}).reset_index()
        rst_sell300 = rst_sell[rst_sell['成交金额']>=3e6].groupby('分钟').agg({'买卖类型':'sum','成交金额':'sum','成交量':'sum'}).reset_index()
        rst300 = pd.merge(rst_buy300,rst_sell300,on='分钟',how='outer')
        rst300 = rst300.fillna(0)
        rst300.columns = ['时间','分钟大单买笔数','分钟大单买额','分钟大单买量','分钟大单卖笔数','分钟大单卖额','分钟大单卖量']
        rst300['分钟净流金额'] = rst300['分钟大单买额']-rst300['分钟大单卖额']
        rst300 = rst300[['时间','分钟净流金额']]
        rst300.columns = ['时间','分钟300w净额']
        rst = pd.merge(rst,rst300,on='时间',how='outer')

        rst_buy1000 = rst_buy[rst_buy['成交金额']>=1e7].groupby('分钟').agg({'买卖类型':'sum','成交金额':'sum','成交量':'sum'}).reset_index()
        rst_sell1000 = rst_sell[rst_sell['成交金额']>=1e7].groupby('分钟').agg({'买卖类型':'sum','成交金额':'sum','成交量':'sum'}).reset_index()
        rst1000 = pd.merge(rst_buy1000,rst_sell1000,on='分钟',how='outer')
        rst1000 = rst1000.fillna(0)
        rst1000.columns = ['时间','分钟大单买笔数','分钟大单买额','分钟大单买量','分钟大单卖笔数','分钟大单卖额','分钟大单卖量']
        rst1000['分钟净流金额'] = rst1000['分钟大单买额']-rst1000['分钟大单卖额']
        rst1000 = rst1000[['时间','分钟净流金额']]
        rst1000.columns = ['时间','分钟1000w净额']
        rst = pd.merge(rst,rst1000,on='时间',how='outer')
        rst = rst.fillna(0)
        
        df = rst.iloc[:,1:].cumsum()
        df.columns = ['累计大单买笔数','累计大单买额','累计大单买量','累计大单卖笔数','累计大单卖额','累计大单卖量','累计大单净笔数','累计大单净额','累计300w净额','累计1000w净额']
        df['股票代码'] = stock_id 
        df['时间'] = rst['时间']
        df = pd.merge(df,rst,on=['时间'])
        return df.tail(1)
    except:
        cols = ['累计大单买笔数','累计大单买额','累计大单买量','累计大单卖笔数','累计大单卖额','累计大单卖量','累计大单净笔数','累计大单净额','累计300w净额','累计1000w净额','股票代码','时间','分钟大单买笔数','分钟大单买额','分钟大单买量','分钟大单卖笔数','分钟大单卖额','分钟大单卖量','分钟净流笔数','分钟净流金额', '分钟300w净额','分钟1000w净额']
        df = pd.DataFrame(columns=cols)
        return df.tail(1)

--这里是增加了板块强度过滤的版本¶

In [5]:

import datetime
from joblib import Parallel,delayed
date_end = datetime.datetime.now().strftime("%Y-%m-%d")
print(datetime.datetime.now())

longhu_ss_api = 'https://apphq.longhuvip.com/w1/api/index.php?&Index=0&Order=1&PhoneOSNew=2&Token=4e7fa8458a2add3f14a50ca79e863772&Type=4&UserID=1973778&VerSion=5.7.0.12&a=KanPanNew&apiv=w31&c=YiDongKanPan&st=200'#.format(date)
longhu_ss_data = json.loads(requests.get(longhu_ss_api).text)

# 提取数据并转换为DataFrame
trend_data = longhu_ss_data['List'] # columns=['date', 'hu_gutong_input', 'shen_gutong_input', 'beixiang_cashflow']
longhu_ss_data = pd.DataFrame(trend_data,)
longhu_ss_data = longhu_ss_data.rename(columns={'stock_code': '股票代码', 'stock_name': '股票名称', 'MIcon': '融1/非融0','ZJJE': '主力净流入', 'jiage': '价格', 'zhangfu': '涨幅', 'index': '异动', 'plate': '板块',}, inplace=False)
longhu_ss_data = longhu_ss_data[['股票代码', '股票名称','涨幅', '融1/非融0', '主力净流入','异动', '板块',  '价格', ]]    
#longhu_ss_data=longhu_ss_data[(longhu_ss_data['异动']>=170)&(longhu_ss_data['异动']<=255)]  #index是异动
longhu_ss_data=longhu_ss_data.sort_values(by=['主力净流入','异动',], ascending=[False,False,])#.head(30)
stock_list_yidong=longhu_ss_data['股票代码'].tolist()

#---今天的日期
sorted_dates= date_end #today_date
#这记录要画图的数据
huatu_data_dict= {}
dates = sorted_dates #pd.date_range(start=sentence_day_1, end=sentence_day_2, freq='D')
data = pd.DataFrame()
# 自定义函数来处理股票代码一列的字符串  这里修改位bigquant正确的格式
def add_suffix(code):
    if code.startswith('0') or code.startswith('3'):
        return code + '.SZA'
    elif code.startswith('6'):
        return code + '.SHA'

    elif code.startswith('8'):
        return code + '.BJA'
    else:
        return code
# 显示所有列
pd.set_option('display.max_columns', None)
#---这里是循环遍历功能 用来采集数据

longhu_ss_api = 'https://apphq.longhuvip.com/w1/api/index.php?Order=1&a=RealRankingInfo&st=60&apiv=w26&Type=1&c=ZhiShuRanking&PhoneOSNew=1&DeviceID=20ad85ca-becb-3bed-b3d4-30032a0f5923&Index=0&ZSType=7'#.format(date)

response = requests.get(longhu_ss_api)
#--检查一下 爬取数据的 返回的状态时候为空
if response.status_code == 200:
    try:
        longhu_ss_data = json.loads(requests.get(longhu_ss_api).text)
    except:
        print('数据为空1')
    # 判断字典是否为空
    if not longhu_ss_data['list']:
        # 字典为空，跳过代码块
        print(date,"数据为空2")

#     longhu_ss_data = pd.DataFrame(np.array(longhu_ss_data['list'])[:,:3])
#     longhu_ss_cols = ['板块代码','板块名称','板块强度',]
#     longhu_ss_data

#     longhu_ss_data.columns = longhu_ss_cols

#     longhu_ss_data['板块强度'] = longhu_ss_data['板块强度'].astype(float)
#     longhu_ss_data = longhu_ss_data.sort_values(by='板块强度', ascending=False).head(10)
    

    longhu_ss_data_1=longhu_ss_data
    longhu_ss_data_1 = pd.DataFrame(np.array(longhu_ss_data_1['list'])[:,:7])
    longhu_ss_cols_1 = ['板块代码','板块名称','板块强度','板块涨幅','板块涨速','区间成交','主力买入']
    longhu_ss_data_1.columns = longhu_ss_cols_1

    longhu_ss_data_1['板块强度'] = longhu_ss_data_1['板块强度'].astype(float)
    longhu_ss_data_1['板块涨幅'] = longhu_ss_data_1['板块涨幅'].astype(float)
    longhu_ss_data_1['区间成交'] = longhu_ss_data_1['区间成交'].astype(float)
    longhu_ss_data_1['主力买入'] = longhu_ss_data_1['主力买入'].astype(float)
    
    longhu_ss_data_1['主力占比']=longhu_ss_data_1['主力买入']/longhu_ss_data_1['区间成交']
    longhu_ss_data_1['板块涨速'] = longhu_ss_data_1['板块涨速'].astype(float)
    
    
    
    df=longhu_ss_data_1
#     df=df[(df['区间成交']<=5*10000000000)&(df['区间成交']>=9.15*1000000)&(df['板块强度']>=-50)&(df['板块强度']<=5406)&(df['主力占比']<=16)&(df['主力占比']>=-13)&(df['主力买入']>=-6*100000000)&(df['主力买入']<=3.8*100000000)]  #这个可以有

    #df=df[(df['区间成交']<=4.99*10000000000)&(df['区间成交']>=9.15*1000000)&(df['主力占比']<=7.75)&(df['主力占比']>=-12.1)&(df['主力买入']>=-6.2*100000000)&(df['主力买入']<=2.72*100000000)] 

#     df=df[(df['区间成交']<=5*10000000000)&(df['区间成交']>=9.15*1000000)&(df['板块强度']>=150)&(df['板块强度']<=650)&(df['主力占比']<=16)&(df['主力占比']>=-13)&(df['主力买入']>=-6*100000000)&(df['主力买入']<=3.8*100000000)] #&(df['板块强度']>=-37)&(df['板块强度']<=2882)&
    longhu_ss_data_1=df
    #longhu_ss_data_1 = longhu_ss_data_1[(longhu_ss_data_1['板块涨速']>=-1)&(longhu_ss_data_1['板块涨速']<=5)] #&(longhu_ss_data_1['板块强度']<=1150)&(longhu_ss_data_1['板块强度']>=50)
    longhu_ss_data_1 = longhu_ss_data_1.sort_values(by='板块强度', ascending=False).head(10)    
    
    
    
    
    
    
    
    
    
    
    bankuai_list=longhu_ss_data_1['板块代码'].tolist()
    
    del_blk = ['801314','801071','801027']
    bankuai_list = [blk for blk in bankuai_list if blk not in del_blk][:5]
    print( longhu_ss_data_1[['板块名称','板块强度']])
    #bankuai_list
    today_stock_list =pd.DataFrame()  # 使用df来存储唯一的股票代码
    checked_stocks = []
    #---这里是板块代码 循环抽取
    print(bankuai_list)
    for i in bankuai_list:
        longhu_ss_api = 'https://apphq.longhuvip.com/w1/api/index.php?st=30&Index=0&old=1&IsKZZType=0&Order=1&PhoneOSNew=1&PlateID={}&Token=4e7fa8458a2add3f14a50ca79e863772&a=ZhiShuStockList_W8&UserID=1973778&apiv=w31&Type=6&c=ZhiShuRanking&VerSion=5.7.0.12&DeviceID=00000000-296c-20ad-0000-00003eb74e84&IsZZ=0&'.format(i)
        longhu_ss_data = json.loads(requests.get(longhu_ss_api).text)
        longhu_ss_data = pd.DataFrame(np.array(longhu_ss_data['list'])[:,:25])#   51   20   20是区间涨幅  23连扳数 24 龙头标记  #11是主力买 12 是主力卖  13 
        longhu_ss_data = longhu_ss_data.rename(columns={0: '股票代码', 1: '股票名称', 2: '类型', 4: '概念', 5: '价格', 6: '涨幅', 9: '涨速', 19: '净流入占比',20:'区间涨幅',23:'连扳空间',24:'龙头标记',11:'主力买',13:'区间净额',}, inplace=False)
        longhu_ss_data = longhu_ss_data[['股票代码', '股票名称','涨幅', '涨速', '净流入占比','类型', '概念',  '价格', '区间涨幅','连扳空间','龙头标记','主力买',]]            

        #longhu_ss_data = longhu_ss_data[~longhu_ss_data['龙头标记'].str.contains('破板')]
        longhu_ss_data = longhu_ss_data[~longhu_ss_data['股票名称'].str.contains('ST')]
        #longhu_ss_data = longhu_ss_data[longhu_ss_data['龙头标记'].str.contains('龙一|龙二|龙三|龙四|龙五')]            

        #--只取板块强度前3的 龙 123   用涨速或者涨幅排序
        longhu_ss_data=longhu_ss_data.sort_values(by='主力买',ascending=False)#.head(20)
        longhu_ss_data=longhu_ss_data#.head(20)

        bankuai_stock_list=longhu_ss_data['股票代码'].tolist()
        #---这里循环把 强势板块中的 强势股票的  大单爬取出来 并且合并
        print(bankuai_stock_list)
        #---合并大单的数据和当天  龙头股票的数据    
        # 使用apply方法调用自定义函数处理股票代码一列的字符串
        longhu_ss_data['instrument']=longhu_ss_data['股票代码']     
        longhu_ss_data['instrument'] = longhu_ss_data['股票代码'].apply(add_suffix)
        intersected_df = longhu_ss_data

        today_stock_list = pd.concat([today_stock_list, intersected_df], ignore_index=True)   
        today_stock_list = today_stock_list.drop_duplicates(subset=['股票代码']) 
        #time.sleep(0.3)  # 停顿2秒钟 
    dde_results = Parallel(n_jobs=-1, backend='threading')(delayed(get_dde_hq)(stock,'kpl') for stock in list(set(stock_list_yidong)&set(today_stock_list['股票代码'])))
    data_2 = pd.concat(dde_results)
    today_stock_list=pd.merge(today_stock_list ,data_2,on=['股票代码'],)
else:
    print('数据有错误3')

#---  特征1：'净流入占比'   特征2：'净流笔数' 特征3：'净流入金额'  #  '涨速', '净流笔数',
#today_stock_list['净流入占比']=today_stock_list['净流入占比'].rank(pct=True)*100
#today_stock_list['净流金额']=today_stock_list['净流金额'].rank(pct=True)*100
#today_stock_list['净流笔数']=today_stock_list['净流笔数'].rank(pct=True)*100
data=today_stock_list[['股票代码','股票名称','涨幅','净流入占比','主力买','累计大单净额','累计300w净额','累计1000w净额','连扳空间','龙头标记','概念','累计大单买笔数', '累计大单净笔数']]

#---这里人气榜  实时  对 所选板块强度股票跟  人气榜取交集
longhu_ss_api = 'https://apphq.longhuvip.com/w1/api/index.php?Order=1&a=GetHotPHB&st=200&apiv=w29&Type=1&c=StockBidYiDong&PhoneOSNew=1&DeviceID=20ad85ca-becb-3bed-b3d4-30032a0f5923'#.format(i)
longhu_ss_data = json.loads(requests.get(longhu_ss_api).text)
longhu_ss_data = pd.DataFrame(np.array(longhu_ss_data['List'])[:,:7])#20   20是区间涨幅  23连扳数 24 龙头标记  #11是主力买 12 是主力卖  13 是区间净额 26是换手 21 是量比  50 300w大单净额
longhu_ss_data = longhu_ss_data.rename(columns={0: '股票代码', 1: '股票名称', 2: '涨幅', 3: '排名变化', 4: '人气排名', 5: '人气激增', 6: '持续上榜',}, inplace=False)       


data= data.merge(longhu_ss_data ,on=['股票代码','股票名称',],how='left')

#print(data)
#---这里找到交集股票池子
stock_list_yidong=data['股票代码'].tolist()
duokongbi_list = pd.DataFrame( )

for i in stock_list_yidong:
    try:
        url = "https://apphq.longhuvip.com/w1/api/index.php?PhoneOSNew=2&StockID={}&Time=&VerSion=5.7.0.12&a=GetStockBsvolumeIncremental&apiv=w31&c=StockL2Data".format(i)
        rst=pd.DataFrame(json.loads(requests.get(url).text)['bsentrust'])#.head(5)
        rst.columns =['时间','总买量','总卖量','总买额','总卖额']
        rst['股票代码']=i
#         rst['总买额']=rst['总买额'].astype(float)
#         rst['总卖额']=rst['总卖额'].astype(float)
#         rst['委买差']=(rst['总买额']-rst['总卖额'])#/rst['总卖额']
#         rst['多空比']=rst['委买差'].cumsum()


        rst['总买额']=rst['总买额'].astype(float)
        rst['总卖额']=rst['总卖额'].astype(float)

        rst['总买量']=rst['总买量'].astype(float)
        rst['总卖量']=rst['总卖量'].astype(float)    


        rst['委买差']=rst['总买额']-rst['总卖额']


        rst['委买差额']=(rst['总买额']-rst['总卖额'])/rst['总卖额']
        rst['委买差量']=(rst['总买量']-rst['总卖量'])/rst['总卖量']
        rst['多空额比']=rst['委买差额'].cumsum()
        rst['多空量比']=rst['委买差量'].cumsum()

        # 创建加速度变量
        acceleration_1 = np.gradient(rst['委买差额'])  # 使用numpy库的gradient函数来计算加速度
        acceleration_2 = np.gradient(rst['委买差量']) 
        # 将加速度添加到数据帧中
        rst['加速度_1'] = acceleration_1 
        rst['加速度_2'] = acceleration_2     
            
        
        
        
        
        
        
        
        
        #合并大单数据
        #df_dadan=get_dde_hq(i,src='kpl')
        #now_mintune = int(rst['时间'].iloc[-1].replace(':',''))
        #df_dadan_tail = df_dadan[df_dadan['时间']<=now_mintune].tail(1)
        #rst = pd.merge(rst,df_dadan_tail,on='股票代码',how='left')
        duokongbi_list = pd.concat([duokongbi_list, rst.tail(1)], ignore_index=True)
    except:
        pass

data= data.merge(duokongbi_list ,on=['股票代码',],how='left')
#data=data[(data['累计300w净额']>0)&(data['累计大单净笔数']>-39)&(data['累计1000w净额']>=0)&(data['委买差']>=-5.381851e+08)]     #(data['多空比']>0)&
data=data[(data['累计大单净额']>8*1000000)] #&(data['累计1000w净额']>8*1000000)&(data['累计1000w净额']<=8*1000000)
#stock_list_total=stock_list_total[(stock_list_total["委买差"]>0)&(stock_list_total["红绿差"]>=-1)&(stock_list_total["累计主力净流入"]>=8*1000000  )]
print(datetime.datetime.now())
data=data.sort_values(by=['加速度_1','加速度_2',], ascending=[False,False,]).head(10)  # '累计大单买笔数', #累计大单净笔数
#data=data.sort_values(by=['委买差','主力买',], ascending=[False,False,])#.head(20)  # '累计大单买笔数', #累计大单净笔数
data=data[['股票代码','股票名称','涨幅_x','委买差额','多空量比','加速度_1','加速度_2','委买差','净流入占比','累计大单净额','累计300w净额','累计1000w净额','连扳空间','龙头标记','概念','主力买','时间','人气排名','累计大单买笔数', '累计大单净笔数']] # '溢价5%次数', '涨停次数','次日红盘率','连扳率',
#record_stock_list =pd.concat([record_stock_list , data], axis=0)
#--原来的表  ---跟现在的合并  显示 时间  这样如果有 一开始早盘显示 午盘消失，后面不见的 也可以显示在表中。
data

2023-11-09 00:13:51.373916
    板块名称    板块强度
0   文化传媒  6438.0
1   华为概念  6378.0
2     芯片  5736.0
3  汽车零部件  3476.0
4   人工智能  2854.0
5     医药  2760.0
6     游戏  2472.0
7   消费电子  1928.0
8   数字经济  1353.0
9    地产链  1222.0
['801031', '801218', '801001', '801199', '801085']
['605577', '603533', '300459', '002657', '300063', '603630', '002291', '300133', '002712', '002137', '601928', '002995', '002354', '002517', '603729', '002238', '300571', '300364', '301262', '301052', '601595', '300426', '000892', '300654', '000802', '601599', '300528', '601900', '002181', '300770']
['688258', '300047', '603533', '300494', '300459', '002657', '300063', '300507', '002855', '603158', '000829', '688327', '000925', '300052', '688229', '600071', '300608', '600839', '600960', '603037', '600733', '300364', '300277', '301489', '300735', '002222', '000034', '688383', '301236', '300826']
['688258', '603388', '000056', '002046', '301297', '688102', '688361', '600520', '600641', '300042', '603991', '000925', '603306', '600071', '688072', '002137', '300031', '300554', '002584', '300287', '688037', '300260', '300567', '002222', '301236', '300045', '603726', '688256', '600246', '300429']
['688123', '605258', '300680', '688533', '603586', '300731', '603297', '300507', '688326', '603158', '605555', '605255', '603178', '603306', '300322', '300572', '600960', '002988', '603037', '600733', '605218', '301489', '603390', '300735', '002931', '002222', '300177', '300684', '301488', '603768']
['688095', '300315', '688258', '300047', '603357', '603533', '688102', '002657', '300063', '603203', '000829', '688327', '301208', '000925', '603003', '002712', '300608', '002995', '600839', '002238', '300364', '301262', '301052', '300277', '603390', '000892', '301236', '300045', '688256']
2023-11-09 00:14:25.818966

Out[5]:

	股票代码	股票名称	涨幅_x	委买差额	多空量比	加速度_1	加速度_2	委买差	净流入占比	累计大单净额	累计300w净额	累计1000w净额	连扳空间	龙头标记	概念	主力买	时间	人气排名	累计大单买笔数	累计大单净笔数
41	301297	富乐德	20.01	0.747517	-0.103436	1.017780	1.084915	12931061.0	12.42	336136156.0	242564157.0	0.0	首板	龙四	光刻机、芯片	668749315	09:34	NaN	160.0	237.0
32	603037	凯众股份	10.01	-0.327343	-1.416412	0.221366	0.246047	-11333830.0	0.64	48729277.0	61123350.0	92876869.0	3连板	龙二	华为汽车、华为概念	237807829	09:34	115	31.0	68.0
15	301052	果麦文化	12.27	-0.343879	0.459686	0.218628	0.232840	-6409752.0	2.48	53180101.0	39095329.0	11492392.0		龙八	文化传媒、人工智能	219412629	09:34	NaN	47.0	76.0
10	002354	天娱数科	5.05	-0.086337	-1.013432	0.168058	0.178666	-4037831.0	0.36	41455136.0	19745412.0	0.0			虚拟人、短剧	303079194	09:34	80	73.0	134.0
26	603158	腾龙股份	10.02	-0.045733	0.899553	0.130846	0.139993	-4033078.0	3.05	160785762.0	121184783.0	89255999.0	3天2板	龙三	华为汽车、新能源汽车	503001491	09:34	12	87.0	141.0
6	002712	思美传媒	9.86	-0.367227	-2.399728	0.123352	0.132754	-8756173.0	2.7	39921460.0	10056485.0	0.0	昨日首板	破板	文化传媒、互动真人	352773949	09:34	NaN	73.0	123.0
1	603533	掌阅科技	9.98	-0.061399	0.423347	0.120074	0.138464	-2860147.0	3.92	155496150.0	76248566.0	15112531.0	3天2板	龙四	短剧、网红经济	687178699	09:34	61	159.0	259.0
56	300315	掌趣科技	14.38	-0.360813	-1.683747	0.108291	0.109890	-24155210.0	1.16	101391314.0	0.0	0.0			游戏、AIGC	911415080	09:34	97	250.0	434.0
34	301489	思泉新材	12.93	-0.250843	-0.426038	0.090110	0.109394	-5235562.0	3.29	19750764.0	10534167.0	0.0		龙七	汽车零部件、华为概念	202284274	09:34	NaN	33.0	57.0
46	300045	华力创通	6.89	-0.563132	-2.775636	0.073807	0.080740	-109560323.0	0.53	157215677.0	85286142.0	0.0			卫星导航、人工智能	1353702673	09:34	43	272.0	500.0

In [ ]:

import datetime
from joblib import Parallel,delayed
today_date=['2023-10-27']#datetime.datetime.now().strftime("%Y-%m-%d ")
print(today_date)
print(datetime.datetime.now())

longhu_ss_api = 'https://apphq.longhuvip.com/w1/api/index.php?&Index=0&Order=1&PhoneOSNew=2&Token=4e7fa8458a2add3f14a50ca79e863772&Type=4&UserID=1973778&VerSion=5.7.0.12&a=KanPanNew&apiv=w31&c=YiDongKanPan&st=200'#.format(date)
longhu_ss_data = json.loads(requests.get(longhu_ss_api).text)

# 提取数据并转换为DataFrame
trend_data = longhu_ss_data['List'] # columns=['date', 'hu_gutong_input', 'shen_gutong_input', 'beixiang_cashflow']
longhu_ss_data = pd.DataFrame(trend_data,)
longhu_ss_data = longhu_ss_data.rename(columns={'stock_code': '股票代码', 'stock_name': '股票名称', 'MIcon': '融1/非融0','ZJJE': '主力净流入', 'jiage': '价格', 'zhangfu': '涨幅', 'index': '异动', 'plate': '板块',}, inplace=False)
longhu_ss_data = longhu_ss_data[['股票代码', '股票名称','涨幅', '融1/非融0', '主力净流入','异动', '板块',  '价格', ]]    
#longhu_ss_data=longhu_ss_data[(longhu_ss_data['异动']>=170)&(longhu_ss_data['异动']<=255)]  #index是异动
longhu_ss_data=longhu_ss_data.sort_values(by=['主力净流入','异动',], ascending=[False,False,])#.head(30)
stock_list_yidong=longhu_ss_data['股票代码'].tolist()

#---今天的日期
sorted_dates=today_date
#这记录要画图的数据
huatu_data_dict= {}
dates = sorted_dates #pd.date_range(start=sentence_day_1, end=sentence_day_2, freq='D')
data = pd.DataFrame()
# 自定义函数来处理股票代码一列的字符串  这里修改位bigquant正确的格式
def add_suffix(code):
    if code.startswith('0') or code.startswith('3'):
        return code + '.SZA'
    elif code.startswith('6'):
        return code + '.SHA'

    elif code.startswith('8'):
        return code + '.BJA'
    else:
        return code
# 显示所有列
pd.set_option('display.max_columns', None)
#---这里是循环遍历功能 用来采集数据
for date in dates:

    #date=date.strftime('%Y-%m-%d')
    print(date)
    longhu_ss_api = 'https://apphq.longhuvip.com/w1/api/index.php?Order=1&a=RealRankingInfo&st=60&apiv=w26&Type=1&c=ZhiShuRanking&PhoneOSNew=1&DeviceID=20ad85ca-becb-3bed-b3d4-30032a0f5923&Index=0&ZSType=7'#.format(date)
    
    response = requests.get(longhu_ss_api)
    #--检查一下 爬取数据的 返回的状态时候为空
    if response.status_code == 200:
        try:
            longhu_ss_data = json.loads(requests.get(longhu_ss_api).text)
        except:
            print('数据为空1')
            continue
        #print(longhu_ss_data)
        # 判断字典是否为空
        if not longhu_ss_data['list']:
            # 字典为空，跳过代码块
            print(date,"数据为空2")
            continue
        
        longhu_ss_data = pd.DataFrame(np.array(longhu_ss_data['list'])[:,:3])
        longhu_ss_cols = ['板块代码','板块名称','板块强度',]
        longhu_ss_data

        longhu_ss_data.columns = longhu_ss_cols

        longhu_ss_data['板块强度'] = longhu_ss_data['板块强度'].astype(float)
        longhu_ss_data = longhu_ss_data.sort_values(by='板块强度', ascending=False).head(4)
        bankuai_list=longhu_ss_data['板块代码'].tolist()
        print( longhu_ss_data[['板块名称','板块强度']])
        #bankuai_list
        today_stock_list =pd.DataFrame()  # 使用df来存储唯一的股票代码
        checked_stocks = []
        #---这里是板块代码 循环抽取
        print(bankuai_list)
        for i in bankuai_list:
            longhu_ss_api = 'https://apphq.longhuvip.com/w1/api/index.php?st=30&Index=0&old=1&IsKZZType=0&Order=1&PhoneOSNew=1&PlateID={}&Token=4e7fa8458a2add3f14a50ca79e863772&a=ZhiShuStockList_W8&UserID=1973778&apiv=w31&Type=6&c=ZhiShuRanking&VerSion=5.7.0.12&DeviceID=00000000-296c-20ad-0000-00003eb74e84&IsZZ=0&'.format(i)
            longhu_ss_data = json.loads(requests.get(longhu_ss_api).text)
            longhu_ss_data = pd.DataFrame(np.array(longhu_ss_data['list'])[:,:51])#20   20是区间涨幅  23连扳数 24 龙头标记  #11是主力买 12 是主力卖  13 
            longhu_ss_data = longhu_ss_data.rename(columns={0: '股票代码', 1: '股票名称', 2: '类型', 4: '概念', 5: '价格', 6: '涨幅', 9: '涨速', 19: '净流入占比',20:'区间涨幅',23:'连扳空间',24:'龙头标记',11:'主力买',13:'区间净额',50:'300w大单净额',25:'换手率',21:'量比',10:'实际流通盘'}, inplace=False)
            longhu_ss_data = longhu_ss_data[['股票代码', '股票名称','涨幅', '涨速', '净流入占比','类型', '概念',  '价格', '区间涨幅','连扳空间','龙头标记','主力买','区间净额','300w大单净额','换手率','量比','实际流通盘']]            
            
            #longhu_ss_data = longhu_ss_data[~longhu_ss_data['龙头标记'].str.contains('破板')]
            longhu_ss_data = longhu_ss_data[~longhu_ss_data['股票名称'].str.contains('ST')]
            #longhu_ss_data = longhu_ss_data[longhu_ss_data['龙头标记'].str.contains('龙一|龙二|龙三|龙四|龙五')]            
            
            #--只取板块强度前3的 龙 123   用涨速或者涨幅排序
            longhu_ss_data=longhu_ss_data.sort_values(by='主力买',ascending=False)#.head(10)
            longhu_ss_data=longhu_ss_data#.head(20)
            
            bankuai_stock_list=longhu_ss_data['股票代码'].tolist()
            #---这里循环把 强势板块中的 强势股票的  大单爬取出来 并且合并
            data_2 = pd.DataFrame()
            print(bankuai_stock_list)
            """
            for j in bankuai_stock_list:
                StockID=j
                if StockID in checked_stocks:continue
                try:
                    df_summary = get_dde_hq(StockID,src='kpl')
                    huatu_data_dict[StockID] = df_summary
                    data_2 = pd.concat([data_2, df_summary], ignore_index=True)
                    checked_stocks.append(StockID)
                except:
                    continue
            """ 
            #---合并大单的数据和当天  龙头股票的数据    
            # 使用apply方法调用自定义函数处理股票代码一列的字符串
            longhu_ss_data['instrument']=longhu_ss_data['股票代码']
            if  len(data_2)<=0:
                continue        
            #longhu_ss_data=pd.merge(longhu_ss_data ,data_2,on=['股票代码'])
            longhu_ss_data['instrument'] = longhu_ss_data['股票代码'].apply(add_suffix)
            intersected_df = longhu_ss_data
            
            today_stock_list = pd.concat([today_stock_list, intersected_df], ignore_index=True)   
            today_stock_list = today_stock_list.drop_duplicates(subset=['股票代码']) 
            #time.sleep(0.3)  # 停顿2秒钟 
        dde_results = Parallel(n_jobs=-1, backend='threading')(delayed(get_dde_hq)(stock) for stock in list(set(stock_list_yidong)&set(today_stock_list['股票代码'])))
        print(datetime.datetime.now())
        data_2 = pd.concat(dde_results)
        today_stock_list=pd.merge(today_stock_list ,data_2,on=['股票代码'])
    else:
        print('数据有错误3')
        continue

#---  特征1：'净流入占比'   特征2：'净流笔数' 特征3：'净流入金额'  #  '涨速', '净流笔数',
#today_stock_list['净流入占比']=today_stock_list['净流入占比'].rank(pct=True)*100
#today_stock_list['净流金额']=today_stock_list['净流金额'].rank(pct=True)*100
#today_stock_list['净流笔数']=today_stock_list['净流笔数'].rank(pct=True)*100
today_stock_list=today_stock_list[['股票代码','股票名称','涨幅','净流入占比','主力买','区间净额','累计300w净额','累计1000w净额','换手率','量比','区间涨幅','连扳空间','龙头标记','概念','实际流通盘']]
#获取昨日收盘价
from bigdatasource.api import DataSource
day_close = DataSource("bar1d_CN_STOCK_A").read(start_date=today_date[0], end_date=today_date[0])
day_close['close'] /= day_close['adjust_factor']
day_close['instrument'] = day_close['instrument'].apply(lambda x:x[:-4])
day_close = day_close[['instrument','close']]
day_close.columns = ['股票代码','昨日收盘价']
data = today_stock_list.merge(day_close,on='股票代码')


#---这里人气榜  实时  对 所选板块强度股票跟  人气榜取交集
longhu_ss_api = 'https://apphq.longhuvip.com/w1/api/index.php?Order=1&a=GetHotPHB&st=200&apiv=w29&Type=1&c=StockBidYiDong&PhoneOSNew=1&DeviceID=20ad85ca-becb-3bed-b3d4-30032a0f5923'#.format(i)
            #url = longhu_ss_api.format(s_time, e_time, date)
longhu_ss_data = json.loads(requests.get(longhu_ss_api).text)
longhu_ss_data = pd.DataFrame(np.array(longhu_ss_data['List'])[:,:7])#20   20是区间涨幅  23连扳数 24 龙头标记  #11是主力买 12 是主力卖  13 是区间净额 26是换手 21 是量比  50 300w大单净额
longhu_ss_data = longhu_ss_data.rename(columns={0: '股票代码', 1: '股票名称', 2: '涨幅', 3: '排名变化', 4: '人气排名', 5: '人气激增', 6: '持续上榜',}, inplace=False)        
#longhu_ss_data 
data['实际流通盘']=data['实际流通盘'].astype(float)
data= data.merge(longhu_ss_data ,on=['股票代码','股票名称','涨幅'])
data['实际流通盘'] =(data['实际流通盘']  / 100000000).round(1).astype(str) + '亿'

#---这里创造一个列表用来合并数据
empty_df = pd.DataFrame(columns=['date', '股票代码', '股票名称', '涨停次数', '溢价5%次数', '次日红盘率', '首版封板率', '首日破板率', '连扳率'])
zhangting_jiyin = data['股票代码'].tolist()

for k in zhangting_jiyin:
    longhu_ss_api = 'https://apphq.longhuvip.com/w1/api/index.php?DeviceID=20ad85ca-becb-3bed-b3d4-30032a0f5923&PhoneOSNew=1&State=1&StockID={}&Token=d89167ce77e16e62fd5fceaed32a5f72&Order&UserID=1973778&VerSion=5.7.0.12&a=GetStockPanKou_Narrow&apiv=w31&c=StockL2Data'.format(k)
    longhu_ss_data = json.loads(requests.get(longhu_ss_api).text)
    longhu_ss_data = pd.DataFrame([longhu_ss_data])
    longhu_ss_data = longhu_ss_data[['day', 'code', 'name', 'YJJY']]
    
    yjjy_columns = ['YJJY_{}'.format(i+1) for i in range(len(longhu_ss_data['YJJY'].values[0]))]
    yjjy_df = longhu_ss_data['YJJY'].apply(pd.Series)
    yjjy_df.columns = yjjy_columns
    
    longhu_ss_data = pd.concat([longhu_ss_data, yjjy_df], axis=1)
    longhu_ss_data = longhu_ss_data.drop('YJJY', axis=1)
    longhu_ss_data = longhu_ss_data.rename(columns={'code': '股票代码', 'name': '股票名称', 'day': 'date', 'YJJY_1': '涨停次数', 'YJJY_2': '溢价5%次数', 'YJJY_3': '次日红盘率', 'YJJY_4': '首版封板率', 'YJJY_5': '首日破板率', 'YJJY_6': '连扳率'}, inplace=False)
    
    empty_df = empty_df.append(longhu_ss_data)

data= data.merge(empty_df ,on=['股票代码','股票名称'])


#---这里找到交集股票池子
stock_list_yidong=data['股票代码'].tolist()
duokongbi_list = pd.DataFrame( )

for i in stock_list_yidong:
    try:
        url = "https://apphq.longhuvip.com/w1/api/index.php?PhoneOSNew=2&StockID={}&Time=&VerSion=5.7.0.12&a=GetStockBsvolumeIncremental&apiv=w31&c=StockL2Data".format(i)
        rst=pd.DataFrame(json.loads(requests.get(url).text)['bsentrust']).head(5)
        rst.columns =['时间','总买量','总卖量','总买额','总卖额']
        rst['股票代码']=i
        rst['总买额']=rst['总买额'].astype(float)
        rst['总卖额']=rst['总卖额'].astype(float)
        rst['委买差']=(rst['总买额']-rst['总卖额'])/rst['总卖额']
        rst['多空比']=rst['委买差'].cumsum()
        #合并大单数据
        df_dadan=get_dde_hq(i,src='ths')
        now_mintune = int(rst['时间'].iloc[-1].replace(':',''))
        df_dadan_tail = df_dadan[df_dadan['时间']<=now_mintune].tail()
        rst = pd.merge(rst,df_dadan_tail,on='股票代码',how='left')
        duokongbi_list = pd.concat([duokongbi_list, rst.tail(1)], ignore_index=True)
    except:
        pass

data= duokongbi_list.merge(data ,on=['股票代码',])






data=data[(data['多空比']>0)&(data['累计300w净额']>=0)&(data['主力净流入']>0)&(data['累计1000w净额']>=0)]
data=data[['股票代码','股票名称','涨幅','人气排名','溢价5%次数','净流入占比','累计300w净额','累计1000w净额','实际流通盘','排名变化','人气激增','持续上榜','涨停次数','次日红盘率','连扳率','连扳空间','龙头标记','概念','区间净额','换手率','区间涨幅','主力买','首日破板率','昨日收盘价','date','时间']]
#record_stock_list =pd.concat([record_stock_list , data], axis=0)
#--原来的表  ---跟现在的合并  显示 时间  这样如果有 一开始早盘显示 午盘消失，后面不见的 也可以显示在表中。
data
print(datetime.datetime.now())

In [ ]:

data

In [ ]:

record_stock_list=pd.DataFrame(columns=['股票代码','股票名称','涨幅','累计大单笔数','人气排名','溢价5%次数','净流入占比','实际流通盘','排名变化','人气激增','持续上榜','涨停次数','次日红盘率','连扳率','连扳空间','龙头标记','概念','区间净额','300w大单净额','换手率','量比','净流金额','区间涨幅','主力买','首日破板率','昨日收盘价','date'])
record_stock_list.to_csv("record_stock_list.csv")
record_stock_list

In [ ]:

list_start=pd.read_csv("record_stock_list.csv")


record_stock_list =pd.concat([data , list_start], axis=0)
list_start = list_start.drop_duplicates()
#record_stock_list

record_stock_list.to_csv("record_stock_list.csv",mode="a",header=False)

list_start.sort_values(by='溢价5%次数',ascending=False)#.head(10)

In [ ]:

#record_stock_list = pd.DataFrame(columns=['股票代码','股票名称','涨幅','累计大单笔数','人气排名','溢价5%次数','净流入占比','实际流通盘','排名变化','人气激增','持续上榜','涨停次数','次日红盘率','连扳率','连扳空间','龙头标记','概念','区间净额','300w大单净额','换手率','量比','净流金额','区间涨幅','主力买','首日破板率','昨日收盘价','date'])
record_stock_list.to_csv("record_stock_list.csv",)
##record_stock_list.to_csv("record_stock_list.csv",mode="a",header=False)
#kks=pd.read_csv("record_stock_list.csv")
#kks

In [ ]:

def show_dde_hq(stock_id):
    #下面是画图
    dde_data = huatu_data_dict[stock_id]
    dde_data['昨日收盘价'] = data[data['股票代码']==stock_id]['昨日收盘价'].values[0]
    dde_data['涨跌幅'] =(dde_data['价格']/dde_data['昨日收盘价']-1)*100
    time_series1 = pd.date_range(start='2022-01-01 09:30', end='2022-01-01 11:30', freq='1min')
    time_series2 = pd.date_range(start='2022-01-01 13:00', end='2022-01-01 15:00', freq='1min')
    k_x_data = ['09:25']+[m.strftime('%H:%M') for m in list(time_series1)+list(time_series2)]
    x_data = list(dde_data['分钟'])
    y_data = list(np.round(dde_data['涨跌幅'],2))
    z_data = list(dde_data['净流金额'])
    my_colors = ['red' if i>0 else 'green' for i in z_data]

    markpoints = [
        opts.MarkPointItem(coord=[x_data[i], y_data[i]],value=z_data[i],itemstyle_opts=opts.ItemStyleOpts(color=my_colors[i]))
        for i in range(len(y_data))
    ]
    line = Line(init_opts=opts.InitOpts(theme='light',
                                        width='800px',
                                        height='400px'))
    line.add_xaxis(k_x_data)
    line.add_yaxis('', y_data,markpoint_opts=opts.MarkPointOpts(data=markpoints,symbol_size=25,label_opts = opts.LabelOpts(is_show=False)),
    label_opts = opts.LabelOpts(is_show=False))
    return line
#--循环打印代码和图像
page = Page(layout=Page.SimplePageLayout)
for i in  range(len(data)):
    code = data['股票代码'].values[i]
    name = data['股票名称'].values[i]
    line = show_dde_hq(data['股票代码'].values[i])
    line.set_global_opts(title_opts=opts.TitleOpts(title=name+code))
    page.add(line)
    line.render_notebook()
page.render_notebook()

In [ ]:

def show_dde_hq(stock_id):
    #下面是画图
    dde_data = get_dde_hq(stock_id)
    dde_data['昨日收盘价'] = data[data['股票代码']==stock_id]['昨日收盘价'].values[0]
    dde_data['涨跌幅'] =(dde_data['价格']/dde_data['昨日收盘价']-1)*100
    time_series1 = pd.date_range(start='2022-01-01 09:30', end='2022-01-01 11:30', freq='1min')
    time_series2 = pd.date_range(start='2022-01-01 13:00', end='2022-01-01 15:00', freq='1min')
    k_x_data = ['09:25']+[m.strftime('%H:%M') for m in list(time_series1)+list(time_series2)]
    x_data = list(dde_data['分钟'])
    y_data = list(np.round(dde_data['涨跌幅'],2))
    z_data = list(dde_data['净流金额'])
    my_colors = ['red' if i>0 else 'green' for i in z_data]

    markpoints = [
        opts.MarkPointItem(coord=[x_data[i], y_data[i]],value=z_data[i],itemstyle_opts=opts.ItemStyleOpts(color=my_colors[i]))
        for i in range(len(y_data))
    ]
    line = Line(init_opts=opts.InitOpts(theme='light',
                                        width='1000px',
                                        height='600px'))
    line.add_xaxis(k_x_data)
    line.add_yaxis('', y_data,markpoint_opts=opts.MarkPointOpts(data=markpoints,symbol_size=25,label_opts = opts.LabelOpts(is_show=False)),
    label_opts = opts.LabelOpts(is_show=False))
    return line
#--循环打印代码和图像
#for i in  range(0,5,1):
print(data['股票代码'].values[2])
print(data['股票名称'].values[2])
line = show_dde_hq(data['股票代码'].values[2])
line.render_notebook()
    #show_dde_hq

In [ ]:

import requests
import pandas as pd
import time

import json
import numpy as np
 
# 发送HTTP请求并获取网页内容

date = '2023-09-11'
s_time = '0925'
e_time = '0930'
pd.set_option('display.max_columns', None)
longhu_ss_api = 'https://apphq.longhuvip.com/w1/api/index.php?Order=1&a=RealRankingInfo_W8&st=60&c=NewStockRanking&PhoneOSNew=1&RStart=0925&DeviceID=20ad85ca-becb-3bed-b3d4-30032a0f5923&VerSion=5.8.0.2&index=0&REnd=0930&apiv=w29&Type=1&FilterMotherboard=0&Filter=0&Ratio=6&FilterTIB=0&FilterGem=0&Date={}&index=0'.format(date)
url = longhu_ss_api.format(s_time, e_time, date)
longhu_ss_data = json.loads(requests.get(longhu_ss_api).text)


longhu_ss_data = pd.DataFrame(np.array(longhu_ss_data['list'])[:,:20])
longhu_ss_cols = ['股票代码','股票名称','空行','排序','板块名称','价格','涨幅','未知1','未知2','涨速','未知3','未知4','未知5','未知6','未知7','未知8','未知9','未知10','未知10','未知11',]
longhu_ss_data.columns = longhu_ss_cols
longhu_ss_data

In [ ]:

longhu_ss_api ='https://apphis.longhuvip.com/w1/api/index.php?Order=1&a=HisRankingInfo_W8&st=60&c=HisStockRanking&PhoneOSNew=1&RStart=0925&DeviceID=20ad85ca-becb-3bed-b3d4-30032a0f5923&VerSion=5.8.0.2&index=0&REnd=0930&apiv=w29&Type=1&FilterMotherboard=0&Filter=0&Ratio=6&FilterTIB=0&FilterGem=0&Date=2023-09-07'
longhu_ss_data = json.loads(requests.get(longhu_ss_api).text)
longhu_ss_cols = ['股票代码','股票名称','未知1','未知2','板块','价格','当前涨幅','区间成交金额','实际换手率','未知3','实际流通金额','主力买金额',
 '主力卖金额','区间净额','买成占比','卖成占比','净成占比','买流占比','卖流占比','净流占比','区间涨幅','量比','未知4','涨停天数']
longhu_ss_data = pd.DataFrame(np.array(longhu_ss_data['list'])[:,:24])
longhu_ss_data.columns = longhu_ss_cols
longhu_ss_data=longhu_ss_data.sort_values(by='净成占比',ascending=False)#.head(10)
longhu_ss_data
#today_stock_list=today_stock_list.sort_values(by='累计大单笔数',ascending=False)#.head(10)

In [ ]:

longhu_ss_data['净成占比']=longhu_ss_data['净成占比'].astype(float)
longhu_ss_data['区间净额']=longhu_ss_data['区间净额'].astype(float)
longhu_ss_data['买流占比']=longhu_ss_data['买流占比'].astype(float)
longhu_ss_data=longhu_ss_data.sort_values(by='净成占比',ascending=False)#.head(10)
longhu_ss_data

In [ ]:

# 本代码由可视化策略环境自动生成 2023年11月9日 00:16
# 本代码单元只能在可视化模式下编辑。您也可以拷贝代码，粘贴到新建的代码单元或者策略，然后修改。


# 回测引擎：初始化函数，只执行一次
def m19_initialize_bigquant_run(context):
    import math
    from zipline.finance.commission import PerOrder

    # 加载预测数据
    context.ranker_prediction = context.options['data'].read_df()

    # 系统已经设置了默认的交易手续费和滑点，要修改手续费可使用如下函数
    context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
    # 预测数据，通过options传入进来，使用 read_df 函数，加载到内存 (DataFrame)
    # 设置买入的股票数量，这里买入预测股票列表排名靠前的5只
    stock_count = 5
    # 每只的股票的权重，如下的权重分配会使得靠前的股票分配多一点的资金，[0.339160, 0.213986, 0.169580, ..]
    context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
    # 设置每只股票占用的最大资金比例
    context.max_cash_per_instrument = 0.2
    context.options['hold_days'] = 5

# 回测引擎：每日数据处理函数，每天执行一次
def m19_handle_data_bigquant_run(context, data):
    # 按日期过滤得到今日的预测数据
    ranker_prediction = context.ranker_prediction[
        context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]

    # 1. 资金分配
    # 平均持仓时间是hold_days，每日都将买入股票，每日预期使用 1/hold_days 的资金
    # 实际操作中，会存在一定的买入误差，所以在前hold_days天，等量使用资金；之后，尽量使用剩余资金（这里设置最多用等量的1.5倍）
    is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间（前 hold_days 天）
    cash_avg = context.portfolio.portfolio_value / context.options['hold_days']
    cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
    cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
    positions = {e.symbol: p.amount * p.last_sale_price
                 for e, p in context.portfolio.positions.items()}

    # 2. 生成卖出订单：hold_days天之后才开始卖出；对持仓的股票，按机器学习算法预测的排序末位淘汰
    if not is_staging and cash_for_sell > 0:
        equities = {e.symbol: e for e, p in context.portfolio.positions.items()}
        instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                lambda x: x in equities)])))

        for instrument in instruments:
            context.order_target(context.symbol(instrument), 0)
            cash_for_sell -= positions[instrument]
            if cash_for_sell <= 0:
                break

    # 3. 生成买入订单：按机器学习算法预测的排序，买入前面的stock_count只股票
    buy_cash_weights = context.stock_weights
    buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
    max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
    for i, instrument in enumerate(buy_instruments):
        cash = cash_for_buy * buy_cash_weights[i]
        if cash > max_cash_per_instrument - positions.get(instrument, 0):
            # 确保股票持仓量不会超过每次股票最大的占用资金量
            cash = max_cash_per_instrument - positions.get(instrument, 0)
        if cash > 0:
            context.order_value(context.symbol(instrument), cash)

# 回测引擎：准备数据，只执行一次
def m19_prepare_bigquant_run(context):
    pass


m1 = M.instruments.v2(
    start_date='2018-01-01',
    end_date='2020-12-31',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = M.advanced_auto_labeler.v2(
    instruments=m1.data,
    label_expr="""# #号开始的表示注释
# 0. 每行一个，顺序执行，从第二个开始，可以使用label字段
# 1. 可用数据字段见 https://bigquant.com/docs/develop/datasource/deprecated/history_data.html
#   添加benchmark_前缀，可使用对应的benchmark数据
# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/develop/bigexpr/usage.html>`_

# 计算收益：5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
shift(close, -5) / shift(open, -1)

# 极值处理：用1%和99%分位的值做clip
clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))

# 将分数映射到分类，这里使用20个分类
all_wbins(label, 20)

# 过滤掉一字涨停的情况 (设置label为NaN，在后续处理和训练中会忽略NaN的label)
where(shift(high, -1) == shift(low, -1), NaN, label)
""",
    start_date='',
    end_date='',
    benchmark='000300.HIX',
    drop_na_label=True,
    cast_label_int=True
)

m3 = M.input_features.v1(
    features="""# #号开始的表示注释
# 多个特征，每行一个，可以包含基础特征和衍生特征
return_5
return_10
return_20
avg_amount_0/avg_amount_5
avg_amount_5/avg_amount_20
rank_avg_amount_0/rank_avg_amount_5
rank_avg_amount_5/rank_avg_amount_10
rank_return_0
rank_return_5
rank_return_10
rank_return_0/rank_return_5
rank_return_5/rank_return_10
pe_ttm_0

list_days_0
"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m16 = M.derived_feature_extractor.v3(
    input_data=m15.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m7 = M.join.v3(
    data1=m2.data,
    data2=m16.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m13 = M.dropnan.v1(
    input_data=m7.data
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2021-01-01'),
    end_date=T.live_run_param('trading_date', '2021-12-31'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m17 = M.general_feature_extractor.v7(
    instruments=m9.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m18 = M.derived_feature_extractor.v3(
    input_data=m17.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m14 = M.dropnan.v1(
    input_data=m18.data
)

m5 = M.filter.v3(
    input_data=m14.data,
    expr='list_days_0>30',
    output_left_data=False
)

m4 = M.input_features.v1(
    features="""# #号开始的表示注释
# 多个特征，每行一个，可以包含基础特征和衍生特征
return_5
return_10
return_20
avg_amount_0/avg_amount_5
avg_amount_5/avg_amount_20
rank_avg_amount_0/rank_avg_amount_5
rank_avg_amount_5/rank_avg_amount_10
rank_return_0
rank_return_5
rank_return_10
rank_return_0/rank_return_5
rank_return_5/rank_return_10
pe_ttm_0

"""
)

m6 = M.stock_ranker_train.v6(
    training_ds=m13.data,
    features=m4.data,
    learning_algorithm='排序',
    number_of_leaves=30,
    minimum_docs_per_leaf=1000,
    number_of_trees=20,
    learning_rate=0.1,
    max_bins=1023,
    feature_fraction=1,
    data_row_fraction=1,
    plot_charts=True,
    ndcg_discount_base=1,
    m_lazy_run=False
)

m8 = M.stock_ranker_predict.v5(
    model=m6.model,
    data=m5.data,
    m_lazy_run=False
)

m19 = M.trade.v4(
    instruments=m9.data,
    options_data=m8.predictions,
    start_date='',
    end_date='',
    initialize=m19_initialize_bigquant_run,
    handle_data=m19_handle_data_bigquant_run,
    prepare=m19_prepare_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=1000000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='真实价格',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark='000300.HIX'
)

这个是改进后的score排序刷票 2023.918¶

结合实时人气表 做交集 用 板块强度前3板块的 主力买入 排序前15的股票做池子¶

买入人气靠前的股票¶

不按照人气排名来分类 因为发现 用rank排名 人气 效果很差，直接用人气指数值 效果会更好¶

这里不知道怎么读取个股热度指数的数据 类似于问财的 个股热度 开盘啦没有¶

这里叠加了 涨停 和溢价基因作为股性 ，用溢价>5%排序 其实感觉跟大单差不多，但是我感觉比单纯大单更好，这个有一个复盘了历时数据K线的功能，可以看到这个股票的股性如何，坑不坑人¶

有一定参考意义，¶

总结 这个版本实时刷票就是 大单+ 人气+股性 还缺一个五线谱 和K线形态¶

---- 去除了 st 并且做了一个遗漏表对照， 用csv存储 历史 刷票记录 ，并标记时间 防止丢失¶

--这里是增加了 板块强度过滤的版本¶

结合实时人气表做交集用板块强度前3板块的主力买入排序前15的股票做池子¶

不按照人气排名来分类因为发现用rank排名人气效果很差，直接用人气指数值效果会更好¶

这里不知道怎么读取个股热度指数的数据类似于问财的个股热度开盘啦没有¶

这里叠加了涨停和溢价基因作为股性，用溢价>5%排序其实感觉跟大单差不多，但是我感觉比单纯大单更好，这个有一个复盘了历时数据K线的功能，可以看到这个股票的股性如何，坑不坑人¶

总结这个版本实时刷票就是大单+ 人气+股性还缺一个五线谱和K线形态¶

---- 去除了 st 并且做了一个遗漏表对照，用csv存储历史刷票记录，并标记时间防止丢失¶

--这里是增加了板块强度过滤的版本¶