繁簡切換您正在訪問的是FX168財經網，本網站所提供的內容及信息均遵守中華人民共和國香港特別行政區當地法律法規。

FX168财经网>人物频道>帖子

每日复盘

作者/专门套利 2019-05-10 07:18 0 来源: FX168财经网人物频道

主要统计当日市场指数基本情况、涨停板的情况，空间板数，个股所属行业情况，当前市场的主线，市场成交的基本情况等等，一目了然，看完之后对当前市场有个基本的把控，非常有利于复盘。欢迎高手批评指正，有啥好的建议或者意见可以提出来，我再加进去

#分析指数近期走势
import pandas as pd
zhishu_list=['000001.XSHG','000016.XSHG','000300.XSHG','000905.XSHG','399001.XSHE','399005.XSHE','399006.XSHE','399678.XSHE']
zhishu_close=[]
zhishu_pct=[]
zhishu_pct5=[]
zhishu_pct3=[]
zhishu_money=[]
zhishu_money3=[]
zhishu_money5=[]
zhishu_volume=[]
zhishu_volume3=[]
zhishu_volume5=[]
for zhishu in zhishu_list:
    zhishu_price=get_price(zhishu,end_date='2019-01-08',count=6,fields=['close','volume','money'])
    #指数值
    zhishu_close.append(zhishu_price['close'][-1])
    #涨跌幅度%
    zhishu_pct.append(round((zhishu_price['close'][-1]/zhishu_price['close'][-2]-1)*100,2))
    zhishu_pct3.append(round((zhishu_price['close'][-1]/zhishu_price['close'][-3]-1)*100,2))
    zhishu_pct5.append(round((zhishu_price['close'][-1]/zhishu_price['close'][-6]-1)*100,2))
    #成交量(亿)
    zhishu_volume.append(round(zhishu_price['volume'][-1]/(10**8),4))
    zhishu_volume3.append(round((zhishu_price['volume'][-3:].mean())/(10**8),4))
    zhishu_volume5.append(round((zhishu_price['volume'][-5:].mean())/(10**8),4))
    #成交额
    zhishu_money.append(round(zhishu_price['money'][-1]/(10**8),2))
    zhishu_money3.append(round(zhishu_price['money'][-3:].mean()/(10**8),2))
    zhishu_money5.append(round(zhishu_price['money'][-5:].mean()/(10**8),2))
df_zhishu=pd.DataFrame(index=['上证指数','上证50','沪深300','中证500','深成指','中小板指','创业板指','深次新股'],\
                       columns=['指数值','涨跌幅%','3日涨跌幅%','5日涨跌幅%','成交量(亿)','3日成交均量(亿)',\
                                '5日成交均量(亿)','成交额(亿)','3日成交均额(亿)','5日成交均额(亿)'])
df_zhishu['指数值']=zhishu_close
df_zhishu['涨跌幅%']=zhishu_pct
df_zhishu['3日涨跌幅%']=zhishu_pct3
df_zhishu['5日涨跌幅%']=zhishu_pct5
df_zhishu['成交量(亿)']=zhishu_volume
df_zhishu['3日成交均量(亿)']=zhishu_volume3
df_zhishu['5日成交均量(亿)']=zhishu_volume5
df_zhishu['成交额(亿)']=zhishu_money
df_zhishu['3日成交均额(亿)']=zhishu_money3
df_zhishu['5日成交均额(亿)']=zhishu_money5
df_zhishu
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; }
指数值 涨跌幅% 3日涨跌幅% 5日涨跌幅% 成交量(亿) 3日成交均量(亿) 5日成交均量(亿) 成交额(亿) 3日成交均额(亿) 5日成交均额(亿)
上证指数 2526.46 -0.26 0.46 1.31 158.0992 168.0940 147.7223 1233.79 1360.64 1225.41
上证50 2305.17 -0.40 -0.41 0.53 18.1312 23.1036 21.8173 223.29 295.34 282.52
沪深300 3047.70 -0.22 0.39 1.23 86.1706 96.8846 86.0368 796.29 974.91 890.49
中证500 4277.30 -0.26 1.58 2.62 69.5569 74.7197 65.4290 497.00 537.21 475.68
深成指 7391.65 -0.12 1.47 2.10 92.4488 100.7327 87.4080 804.72 912.41 814.75
中小板指 4813.60 -0.01 2.06 2.35 22.6168 24.4821 21.8523 243.22 266.88 238.57
创业板指 1263.51 -0.36 1.47 1.04 12.7616 14.7083 13.5662 162.41 187.66 173.55
深次新股 709.36 -0.14 1.68 0.95 4.4667 5.1428 4.5069 105.73 108.09 99.47 
import pandas as pd
pd.set_option('precision', 8)
pd.set_option('display.width',1000)
import matplotlib.pyplot as plt
plt.style.use('ggplot')
df=get_all_securities(types=['stock'],date='2019-01-08')
#df=get_fundamentals(query(valuation.code))
#print (len(df['code']))  #两市股票数量
u10=[]
u7=[]
u5=[]
u3=[]
u0=[]
u_0=[]
u_3=[]
u_5=[]
u_7=[]
u_10=[]
uuv=[]
uus=[]
uust=[]
uuzhang=[]
uuzhang2=[]
uunost=[]
uu_5=[]
for stock in list(df.index):
    df2=get_price(stock,end_date='2019-01-08',count=6,frequency='daily',\
                  fields=['open','close','volume','high','low','money','high_limit','low_limit'],\
                  fq='pre')
    if df2['close'][-1]==df2['high_limit'][-1]: #没有排除掉当前已经停牌的票，包含ST个股
        u10.append(stock)
    elif df2['close'][-1]>=1.07*df2['close'][-2] and df2['close'][-1]<df2['high_limit'][-1]:
        u7.append(stock)  #7个点以上涨幅个股
    elif df2['close'][-1]>=1.05*df2['close'][-2] and df2['close'][-1]<1.07*df2['close'][-2]:
        u5.append(stock) #5个点以上涨幅个股
    elif df2['close'][-1]>=1.03*df2['close'][-2] and df2['close'][-1]<1.05*df2['close'][-2]:
        u3.append(stock) #3个点以上涨幅个股
    elif df2['close'][-1]>=df2['close'][-2] and df2['close'][-1]<1.03*df2['close'][-2]:
        u0.append(stock) #0个点以上涨幅个股数
    elif df2['close'][-1]==df2['low_limit'][-1]:
        u_10.append(stock)  #跌停个股数
    elif df2['close'][-1]>df2['low_limit'][-1] and df2['close'][-1]<=0.93*df2['close'][-2]:
        u_7.append(stock)   #7个点以上跌幅个股数
    elif df2['close'][-1]>0.93*df2['close'][-2] and df2['close'][-1]<=0.95*df2['close'][-2]:
        u_5.append(stock)   #5个点以上跌幅个股数
    elif df2['close'][-1]>0.95*df2['close'][-2] and df2['close'][-1]<=0.97*df2['close'][-2]:
        u_3.append(stock)   #3个点以上跌幅个股数
    elif df2['close'][-1]>0.97*df2['close'][-2] and df2['close'][-1]<df2['close'][-2]:
        u_0.append(stock)  #0个点以上跌幅个股数
    if df2['close'][-1]<df2['high_limit'][-1] and df2['high'][-1]==df2['high_limit'][-1]: #当日炸板股票
            uuv.append(stock)
    if df2['volume'][-1]==df2['money'][-1]==0:   #当日停盘股票
        uus.append(stock)
    #涨停票和停牌票合在一起处理
    if df2['close'][-1]==df2['high_limit'][-1] and df2['volume'][-1]>0 and df2['money'][-1]>0:
        uuzhang.append(stock)  #得到今日涨停个股
    if df2['close'][-1]==df2['high_limit'][-1] and df2['close'][-1]<1.07*df2['close'][-2]:
        uust.append(stock)   #st涨停个股
    if df2['close'][-2]==df2['high_limit'][-2] and df2['volume'][-2]>0 and df2['money'][-2]>0 \
    and df2['close'][-2]>1.06*df2['close'][-3]:
        uuzhang2.append(stock)  #昨日涨停个股
    if df2['close'][-1]==df2['high_limit'][-1] and df2['volume'][-1]>0 and df2['money'][-1]>0 \
    and df2['close'][-1]>1.07*df2['close'][-2]:  #得到涨停且非st个股
        uunost.append(stock)
    #5个点以上个股，用于分析5个点以上个股所属行业
    if df2['close'][-1]>=1.05*df2['close'][-2] and df2['volume'][-1]>0 and df2['money'][-1]>0:
        uu_5.append(stock)
#print (len(uunost))        
#当日涨停个股数(包含st)
uuust=len(u10)-len(uus)
#当日涨停个股(不包含st)
uuu=len(u10)-len(uus)-len(uust)
#当日触板个股总数
uuc=len(uuzhang)+len(uuv)
#当日炸板率
uuz=round((len(uuv)/uuc*100),2)
df_zhangfu=pd.DataFrame(index=['涨停个股（包含ST）','炸板个股','7个点以上涨幅','5-7个点涨幅','3-5个点涨幅','0-3个点涨幅','跌停个股数(包含ST)','0-3个点跌幅','3-5个点跌幅','5-7个点跌幅','7个点以上跌幅'],\
                        columns=['个数统计'])
df_zhangfu['个数统计']=[len(uuzhang),len(uuv),len(u7),len(u5),len(u3),len(u0),len(u_10),len(u_0),len(u_3),len(u_5),len(u_7)]
df_zhangfu
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; }
个数统计
涨停个股（包含ST） 55
炸板个股 44
7个点以上涨幅 12
5-7个点涨幅 42
3-5个点涨幅 99
0-3个点涨幅 1338
跌停个股数(包含ST) 2
0-3个点跌幅 1866
3-5个点跌幅 121
5-7个点跌幅 15
7个点以上跌幅 5 
#今日涨停个股板数统计
uuu1=[]
uuu2=[]
uuu3=[]
uuu4=[]
uuu5=[]
uuu6=[]
uuu7=[]
uuu8=[]
uuu9=[]
uuu10=[]
for stock in uunost:
    df2_1=get_price(stock,end_date='2019-01-08',count=10,frequency='daily',\
                    fields=['open','close','volume','high','low','money','high_limit','low_limit'],\
                    fq='pre')
    if df2_1['close'][-2]<df2_1['high_limit'][-2]:
        uuu1.append(stock)
    if df2_1['close'][-2]==df2_1['high_limit'][-2] and df2_1['close'][-3]<df2_1['high_limit'][-3]:
        uuu2.append(stock)
    if df2_1['close'][-2]==df2_1['high_limit'][-2] and df2_1['close'][-3]==df2_1['high_limit'][-3] \
    and df2_1['close'][-4]<df2_1['high_limit'][-4]:
        uuu3.append(stock)
    if df2_1['close'][-2]==df2_1['high_limit'][-2] and df2_1['close'][-3]==df2_1['high_limit'][-3] and \
    df2_1['close'][-4]==df2_1['high_limit'][-4] and df2_1['close'][-5]<df2_1['high_limit'][-5]:
        uuu4.append(stock)
    if df2_1['close'][-2]==df2_1['high_limit'][-2] and df2_1['close'][-3]==df2_1['high_limit'][-3] and \
    df2_1['close'][-4]==df2_1['high_limit'][-4] and df2_1['close'][-5]==df2_1['high_limit'][-5] and \
    df2_1['close'][-6]<df2_1['high_limit'][-6]:
        uuu5.append(stock)
    if df2_1['close'][-2]==df2_1['high_limit'][-2] and df2_1['close'][-3]==df2_1['high_limit'][-3] and \
    df2_1['close'][-4]==df2_1['high_limit'][-4] and df2_1['close'][-5]==df2_1['high_limit'][-5] and \
    df2_1['close'][-6]==df2_1['high_limit'][-6] and df2_1['close'][-7]<df2_1['high_limit'][-7]:
        uuu6.append(stock)
    if df2_1['close'][-2]==df2_1['high_limit'][-2] and df2_1['close'][-3]==df2_1['high_limit'][-3] and \
    df2_1['close'][-4]==df2_1['high_limit'][-4] and df2_1['close'][-5]==df2_1['high_limit'][-5] and \
    df2_1['close'][-6]==df2_1['high_limit'][-6] and df2_1['close'][-7]==df2_1['high_limit'][-7] and \
    df2_1['close'][-8]<df2_1['high_limit'][-8]:
        uuu7.append(stock)
    if df2_1['close'][-2]==df2_1['high_limit'][-2] and df2_1['close'][-3]==df2_1['high_limit'][-3] and \
    df2_1['close'][-4]==df2_1['high_limit'][-4] and df2_1['close'][-5]==df2_1['high_limit'][-5] and \
    df2_1['close'][-6]==df2_1['high_limit'][-6] and df2_1['close'][-7]==df2_1['high_limit'][-7] and \
    df2_1['close'][-8]==df2_1['high_limit'][-8] and df2_1['close'][-9]<df2_1['high_limit'][-9]:
        uuu8.append(stock)
    if df2_1['close'][-2]==df2_1['high_limit'][-2] and df2_1['close'][-3]==df2_1['high_limit'][-3] and \
    df2_1['close'][-4]==df2_1['high_limit'][-4] and df2_1['close'][-5]==df2_1['high_limit'][-5] and \
    df2_1['close'][-6]==df2_1['high_limit'][-6] and df2_1['close'][-7]==df2_1['high_limit'][-7] and \
    df2_1['close'][-8]==df2_1['high_limit'][-8] and df2_1['close'][-9]==df2_1['high_limit'][-9] and \
    df2_1['close'][-10]<df2_1['high_limit'][-10]:
        uuu9.append(stock)
    if df2_1['close'][-2]==df2_1['high_limit'][-2] and df2_1['close'][-3]==df2_1['high_limit'][-3] and \
    df2_1['close'][-4]==df2_1['high_limit'][-4] and df2_1['close'][-5]==df2_1['high_limit'][-5] and \
    df2_1['close'][-6]==df2_1['high_limit'][-6] and df2_1['close'][-7]==df2_1['high_limit'][-7] and \
    df2_1['close'][-8]==df2_1['high_limit'][-8] and df2_1['close'][-9]==df2_1['high_limit'][-9] and \
    df2_1['close'][-10]==df2_1['high_limit'][-10] and df2_1['close'][-11]<df2_1['high_limit'][-11]:
        uuu10.append(stock)
df_zhangtingtongji=pd.DataFrame(index=['今日涨停个股数','首板个股数','二板个股数','三板个股数','四板个股数','五板个股数',\
                                       '六板个股数','七板个股数','八板个股数',\
                                       '九板个股数','十板个股数'],columns=['个数统计'])
df_zhangtingtongji['个数统计']=[len(uunost),len(uuu1),len(uuu2),len(uuu3),len(uuu4),\
                            len(uuu5),len(uuu6),len(uuu7),len(uuu8),\
                           len(uuu9),len(uuu10)]
df_zhangtingtongji
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; }
个数统计
今日涨停个股数 46
首板个股数 27
二板个股数 10
三板个股数 3
四板个股数 1
五板个股数 2
六板个股数 1
七板个股数 0
八板个股数 1
九板个股数 0
十板个股数 0 
#涨停个股行业统计，扣除掉刚上市的新股吧，不然拉不出来新股的行业
import pandas as pd
industry_name=[]
code=[]
#print (uunost)
#uunost=uunost.remove('600310.XSHG')
#print (uunost)
for stock in uunost:  
    df3=get_industry(stock,date='2019-01-08')
    code.append(stock)
    industry_name.append(df3[stock]['zjw']['industry_name'])
    #print (df3[stock])
    #print (df3[stock]['sw_l2']['industry_name'],stock)
#print (pd.Series(code))
#print (pd.Series(industry_name))
#加一列，便于个数统计
df_1=[]
for i in range(len(uunost)):
    df_1.append(1)
#print (df_1)
df_industry=pd.DataFrame({'股票代码':pd.Series(code),'涨停个股所属行业':pd.Series(industry_name),'计数器':pd.Series(df_1)}) #顺利合成两列数据，便于分析涨停板所属行业
#print (df_industry)
df_industry_name=df_industry.groupby(df_industry['涨停个股所属行业']).sum().sort_values('计数器',ascending=False).head(10)  #可以直接看出那个行业比较强势
df_industry_name  #用软件来分类和处理数据吧
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; }
计数器
涨停个股所属行业
计算机、通信和其他电子设备制造业 6
电气机械和器材制造业 5
金属制品业 4
专用设备制造业 4
商务服务业 3
燃气生产和供应业 2
化学原料和化学制品制造业 2
软件和信息技术服务业 2
橡胶和塑料制品业 2
电力、热力生产和供应业 2 
#昨日涨停个股今日表现统计
gaokai=[]
dikai=[]
jinrilianban=[]
shangzhang=[]
xiadie=[]
jinridieting=[]
jinrizhaban=[]
for stock in uuzhang2:
    df2_2=get_price(stock,end_date='2019-01-08',count=6,frequency='daily',\
                    fields=['open','close','volume','high','low','money','high_limit','low_limit'],\
                    fq='pre')
    if df2_2['open'][-1]>df2_2['close'][-2]:
        gaokai.append(stock)
    elif df2_2['open'][-1]<=df2_2['close'][-2]:
        dikai.append(stock)
    if df2_2['close'][-1]==df2_2['high_limit'][-1] and df2_2['volume'][-1]>0 and df2_2['money'][-1]>0:
        jinrilianban.append(stock)
    if df2_2['high'][-1]==df2_2['high_limit'][-1] and df2_2['close'][-1]<df2_2['high_limit'][-1]:
        jinrizhaban.append(stock)
    if df2_2['close'][-1]>df2_2['close'][-2]:
        shangzhang.append(stock)
    elif df2_2['close'][-1]<=df2_2['close'][-2]:
        xiadie.append(stock)
    if df2_2['close'][-1]==df2_2['low_limit'][-1]:
        jinridieting.append(stock)
df_zhangting=pd.DataFrame(index=['昨日涨停个股数','高开率','低开率','连板个股','炸板个股','上涨','下跌','跌停',\
                                 '上涨概率','下跌概率'],columns=['个数统计'])
df_zhangting['个数统计']=[len(uuzhang2),round(len(gaokai)/len(uuzhang2),2),round(len(dikai)/len(uuzhang2),2),len(jinrilianban),\
                      len(jinrizhaban),len(shangzhang),len(xiadie),len(jinridieting),round(len(shangzhang)/len(uuzhang2),2),\
                      round(len(xiadie)/len(uuzhang2),2)]
df_zhangting
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; }
个数统计
昨日涨停个股数 89.00
高开率 0.80
低开率 0.20
连板个股 19.00
炸板个股 16.00
上涨 44.00
下跌 45.00
跌停 1.00
上涨概率 0.49
下跌概率 0.51 
#昨日涨停个股今日收盘表现
uuz10=[]
uuz7=[]
uuz5=[]
uuz3=[]
uuz0=[]
uuz_0=[]
uuz_3=[]
uuz_5=[]
uuz_7=[]
uuz_10=[]
for stock in uuzhang2:
    df2_3=get_price(stock,end_date='2019-01-08',count=6,frequency='daily',\
                    fields=['open','close','volume','high','low','money','high_limit','low_limit'],\
                    fq='pre')
    if df2_3['close'][-1]==df2_3['high_limit'][-1]:
        uuz10.append(stock)
    if df2_3['close'][-1]<df2_3['high_limit'][-1] and df2_3['close'][-1]>=1.07*df2_3['close'][-2]:
        uuz7.append(stock)
    if df2_3['close'][-1]<1.07*df2_3['close'][-2] and df2_3['close'][-1]>=1.05*df2_3['close'][-2]:
        uuz5.append(stock)
    if df2_3['close'][-1]<1.05*df2_3['close'][-2] and df2_3['close'][-1]>=1.03*df2_3['close'][-2]:
        uuz3.append(stock)
    if df2_3['close'][-1]<1.03*df2_3['close'][-2] and df2_3['close'][-1]>=1.00*df2_3['close'][-2]:
        uuz0.append(stock)
    if df2_3['close'][-1]<df2_3['close'][-2] and df2_3['close'][-1]>=0.97*df2_3['close'][-2]:
        uuz_0.append(stock)
    if df2_3['close'][-1]<0.97*df2_3['close'][-2] and df2_3['close'][-1]>=0.95*df2_3['close'][-2]:
        uuz_3.append(stock)
    if df2_3['close'][-1]<0.95*df2_3['close'][-2] and df2_3['close'][-1]>=0.93*df2_3['close'][-2]:
        uuz_5.append(stock)
    if df2_3['close'][-1]<0.93*df2_3['close'][-2] and df2_3['close'][-1]>df2_3['low_limit'][-1]:
        uuz_7.append(stock)
    if df2_3['close'][-1]==df2_3['low_limit'][-1]:
        uuz_10.append(stock)
df_zuorizhangting=pd.DataFrame(index=['昨日涨停个股数','今日连板','7个点以上涨幅','5-7个点涨幅','3-5个点涨幅','0-3个点涨幅',\
                                 '0-3个点跌幅','3-5个点跌幅','5-7个点跌幅','7个点以上跌幅','今日跌停'],columns=['个数统计'])
df_zuorizhangting['个数统计']=[len(uuzhang2),len(uuz10),len(uuz7),len(uuz5),len(uuz3),len(uuz0),len(uuz_0),len(uuz_3),len(uuz_5),\
                     len(uuz_7),len(uuz_10)]
df_zuorizhangting
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; }
个数统计
昨日涨停个股数 89
今日连板 19
7个点以上涨幅 1
5-7个点涨幅 4
3-5个点涨幅 6
0-3个点涨幅 16
0-3个点跌幅 17
3-5个点跌幅 21
5-7个点跌幅 4
7个点以上跌幅 0
今日跌停 1 
#寻找5个点以上涨幅个股所属行业
import pandas as pd
industry_name=[]
code=[]
for stock in uu_5:  
    df3=get_industry(stock,date='2019-01-08')
    code.append(stock)
    industry_name.append(df3[stock]['zjw']['industry_name'])
    #print (df3[stock]['zjw']['industry_name'])
#print (pd.Series(code))
#print (pd.Series(industry_name))
#加一列，便于个数统计
df_1=[]
for i in range(len(uu_5)):
    df_1.append(1)
#print (df_1)
df_industry=pd.DataFrame({'股票代码':pd.Series(code),'5个点以上涨幅个股所属行业':pd.Series(industry_name),'计数器':pd.Series(df_1)}) #顺利合成两列数据，便于分析个股所属行业
#print (df_industry)
df_industry_name=df_industry.groupby(df_industry['5个点以上涨幅个股所属行业']).sum().sort_values('计数器',ascending=False).head(10)  #可以直接看出那个行业比较强势
df_industry_name  #用软件来分类和处理数据吧
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; }
计数器
5个点以上涨幅个股所属行业
计算机、通信和其他电子设备制造业 19
电气机械和器材制造业 16
专用设备制造业 10
燃气生产和供应业 6
金属制品业 4
化学原料和化学制品制造业 4
橡胶和塑料制品业 3
商务服务业 3
软件和信息技术服务业 3
汽车制造业 3 
import pandas as pd
import numpy as np
df=get_all_securities(types=['stock'],date='2019-01-08')
#df['code']
newhigh_20=[]   #20日新高
newhigh_60=[]    #60日新高
newhigh_250=[]   #250日新高，也就是一年新高
newhigh_10years=[]  #10年新高，一般也就意味着是历史新高了
for stock in list(df.index):
    df4=get_price(stock,end_date='2019-01-07',count=3000,frequency='daily',fields=['close','money'],fq='pre')
    if df4['close'].max()==df4['close'][-1] and df4['money'][-1]>0:   #10年新高个股,扣除停牌股
        newhigh_10years.append(stock)
    if df4['close'][-20:].max()==df4['close'][-1] and df4['money'][-1]>0:
        newhigh_20.append(stock)
    if df4['close'][-60:].max()==df4['close'][-1] and df4['money'][-1]>0:
        newhigh_60.append(stock)
    if df4['close'][-250:].max()==df4['close'][-1] and df4['money'][-1]>0:
        newhigh_250.append(stock)
df_newhigh_20=pd.DataFrame({'20日新高':newhigh_20})
df_xingao=pd.DataFrame(index=['20日新高','60日新高','一年新高','历史新高'],columns=['个数统计'])
df_xingao['个数统计']=[len(newhigh_20),len(newhigh_60),len(newhigh_250),len(newhigh_10years)]
df_xingao
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; }
个数统计
20日新高 511
60日新高 234
一年新高 21
历史新高 4 
df_newhigh_250=pd.DataFrame({'一年新高':newhigh_250})
df_newhigh_250
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; }
一年新高
0 000429.XSHE
1 000750.XSHE
2 002017.XSHE
3 002115.XSHE
4 002124.XSHE
5 002414.XSHE
6 002776.XSHE
7 002811.XSHE
8 002941.XSHE
9 300087.XSHE
10 300265.XSHE
11 300498.XSHE
12 300615.XSHE
13 300628.XSHE
14 300751.XSHE
15 600242.XSHG
16 600406.XSHG
17 600548.XSHG
18 600677.XSHG
19 600776.XSHG
20 601860.XSHG 
df_newhigh_20=pd.DataFrame({'20日新高':newhigh_20})
df_newhigh_20
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; }
20日新高
0 000026.XSHE
1 000043.XSHE
2 000070.XSHE
3 000099.XSHE
4 000100.XSHE
5 000400.XSHE
6 000426.XSHE
7 000429.XSHE
8 000519.XSHE
9 000520.XSHE
10 000537.XSHE
11 000543.XSHE
12 000547.XSHE
13 000550.XSHE
14 000551.XSHE
15 000561.XSHE
16 000563.XSHE
17 000587.XSHE
18 000590.XSHE
19 000600.XSHE
20 000608.XSHE
21 000625.XSHE
22 000633.XSHE
23 000655.XSHE
24 000682.XSHE
25 000685.XSHE
26 000712.XSHE
27 000718.XSHE
28 000719.XSHE
29 000720.XSHE
... ...
481 603200.XSHG
482 603238.XSHG
483 603278.XSHG
484 603298.XSHG
485 603303.XSHG
486 603313.XSHG
487 603319.XSHG
488 603326.XSHG
489 603380.XSHG
490 603383.XSHG
491 603500.XSHG
492 603556.XSHG
493 603567.XSHG
494 603577.XSHG
495 603606.XSHG
496 603609.XSHG
497 603618.XSHG
498 603636.XSHG
499 603663.XSHG
500 603665.XSHG
501 603688.XSHG
502 603698.XSHG
503 603709.XSHG
504 603712.XSHG
505 603738.XSHG
506 603823.XSHG
507 603855.XSHG
508 603879.XSHG
509 603900.XSHG
510 603920.XSHG

511 rows × 1 columns

df_newhigh_60=pd.DataFrame({'60日新高':newhigh_60})
df_newhigh_60
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; }
60日新高
0 000070.XSHE
1 000100.XSHE
2 000400.XSHE
3 000426.XSHE
4 000429.XSHE
5 000519.XSHE
6 000543.XSHE
7 000547.XSHE
8 000550.XSHE
9 000561.XSHE
10 000625.XSHE
11 000682.XSHE
12 000719.XSHE
13 000735.XSHE
14 000738.XSHE
15 000750.XSHE
16 000777.XSHE
17 000797.XSHE
18 000823.XSHE
19 000851.XSHE
20 000876.XSHE
21 000890.XSHE
22 000895.XSHE
23 000899.XSHE
24 000901.XSHE
25 002017.XSHE
26 002023.XSHE
27 002039.XSHE
28 002064.XSHE
29 002091.XSHE
... ...
204 601700.XSHG
205 601860.XSHG
206 601890.XSHG
207 601900.XSHG
208 601901.XSHG
209 601919.XSHG
210 601965.XSHG
211 601989.XSHG
212 603005.XSHG
213 603016.XSHG
214 603017.XSHG
215 603043.XSHG
216 603050.XSHG
217 603118.XSHG
218 603200.XSHG
219 603238.XSHG
220 603313.XSHG
221 603326.XSHG
222 603500.XSHG
223 603567.XSHG
224 603577.XSHG
225 603606.XSHG
226 603618.XSHG
227 603636.XSHG
228 603663.XSHG
229 603665.XSHG
230 603688.XSHG
231 603709.XSHG
232 603712.XSHG
233 603823.XSHG

234 rows × 1 columns

#统计出今日市场成交情况所占比例
import pandas as pd
def get_money_statistics(date):
    money_500=[]
    money_1000=[]
    money_3000=[]
    money_5000=[]
    money_10000=[]
    money_50000=[]
    money_5up=[]
    money_10up=[]
    df=get_all_securities(types=['stock'],date=date)
    for stock in list(df.index):
        df5=get_price(stock,end_date=date,count=2,frequency='daily',fields=['money'],fq='pre')
        if df5['money'][-1]<5*10**6:
            money_500.append(stock)
        if df5['money'][-1]<10**7 and df5['money'][-1]>=5*10**6:
            money_1000.append(stock)
        if df5['money'][-1]<3*10**7 and df5['money'][-1]>=10**7:
            money_3000.append(stock)
        if df5['money'][-1]<5*10**7 and df5['money'][-1]>=3*10**7:
            money_5000.append(stock)
        if df5['money'][-1]<10**8 and df5['money'][-1]>=5*10**7:
            money_10000.append(stock)
        if df5['money'][-1]<5*10**8 and df5['money'][-1]>=10**8:
            money_50000.append(stock)
        if df5['money'][-1]<10**9 and df5['money'][-1]>=5*10**8:
            money_5up.append(stock)
        if df5['money'][-1]>=10**9:
            money_10up.append(stock)
    df_money=pd.DataFrame(index=['500万以下','500-1000万','1000万-3000万','3000万-5000万','5000万-1亿','1亿-5亿','5亿-10亿','10亿以上'],\
                      columns=['成交额个股数','个股所占比例'])
    df_money['成交额个股数']=[len(money_500),len(money_1000),len(money_3000),len(money_5000),len(money_10000),len(money_50000),len(money_5up),len(money_10up)]
    df_money['个股所占比例']=round(df_money['成交额个股数']/len(df.index),4)
    return df_money
get_money_statistics('2019-01-08')
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; }
成交额个股数 个股所占比例
500万以下 100 0.0280
500-1000万 341 0.0955
1000万-3000万 1160 0.3249
3000万-5000万 567 0.1588
5000万-1亿 680 0.1905
1亿-5亿 635 0.1779
5亿-10亿 69 0.0193
10亿以上 18 0.0050 
#今日涨停个股价位区间分布
import pandas as pd
uz10=[]
uz20=[]
uz30=[]
uz40=[]
for stock in uunost:
    df2_3=get_price(stock,end_date='2019-01-08',count=10,frequency='daily',\
                    fields=['open','close'],\
                    fq='pre')
    if df2_3['close'][-1]<=10:
        uz10.append(stock)
    if df2_3['close'][-1]<=20 and df2_3['close'][-1]>10:
        uz20.append(stock)
    if df2_3['close'][-1]<=30 and df2_3['close'][-1]>20:
        uz30.append(stock)
    if df2_3['close'][-1]>30:
        uz40.append(stock)
df_closefenbu=pd.DataFrame(index=['10元以内','20元以内','30元以内','30元以上'],columns=['涨停个股数','个股所占总涨停数的比例'])
df_closefenbu['涨停个股数']=[len(uz10),len(uz20),len(uz30),len(uz40)]
df_closefenbu['个股所占总涨停数的比例']=round(df_closefenbu['涨停个股数']/len(uunost),4)
df_closefenbu
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; }
涨停个股数 个股所占总涨停数的比例
10元以内 31 0.6739
20元以内 10 0.2174
30元以内 4 0.0870
30元以上 1 0.0217 
#截止到当前日期的交易日列表，涨停个股数，反应的是当前市场的情绪
def get_trade_days(date):
    df=get_price('000001.XSHG',end_date=date,count=30,frequency='daily',fields=['close'],fq='pre')
    return df['close'].index
#截止到当前日期的涨停个股走势图，看出市场的情绪
import pandas as pd
def get_zhangting(date):
    uuzzz=[]
    for i in get_trade_days(date):
        df2=get_all_securities(types=['stock'],date=i)
        uuzz=[]
        for stock in list(df2.index):
            df3=get_price(stock,end_date=i,count=1,frequency='daily',fields=['close','high_limit','money'],fq='pre')
            if df3['close'][-1]==df3['high_limit'][-1] and df3['money'][-1]>0:
                uuzz.append(stock)
        uuzzz.append(len(uuzz))
    df_uuzz=pd.DataFrame(index=get_trade_days(date),columns=['涨停个股数'])
    df_uuzz['涨停个股数']=uuzzz
    return df_uuzz.plot()
get_zhangting('2019-01-08')  #市场情绪指标走势图

聲明：本文為入駐FX168財經網人物頻道的作者發布，不代表FX168財經網的觀點。文中觀點僅供參考，投資有風險，入市需謹慎

分享到：
       
举报财经168客户端下载

全部回复

0/140

专门套利

0关注1粉丝37帖子

热门文章最新文章

投稿 您想发表你的观点和看法？

热门标签

原油 黄金 技术分析 市场热点 市场分析 白银 环球财经 美元 经纪商

更多人气分析师

  • 张亦巧

    人气2208文章4145粉丝45

    暂无个人简介信息

  • 张迎妤

    人气1904文章3305粉丝34

    个人专注于行情技术分析，消息面解读剖析，给予您第一时间方向...

  • 指导老师

    人气1864文章4423粉丝52

    暂无个人简介信息

  • 李冉晴

    人气2320文章3821粉丝34

    李冉晴，专业现贷实盘分析师。

  • 梁孟梵

    人气2184文章3177粉丝39

    qq:2294906466 了解群指导添加微信mfmacd

  • 王启蒙现货黄金

    人气320文章3490粉丝8

    本人做分析师以来，并专注于贵金属投资市场，尤其是在现货黄金...

  • 金泰铬J

    人气2328文章3925粉丝51

    投资问答解咨询金泰铬V/信tgtg67即可获取每日的实时资讯、行情...

  • 金算盘

    人气2696文章7761粉丝125

    高级分析师，混过名校，厮杀于股市和期货、证券市场多年，专注...

  • 金帝财神

    人气4760文章8329粉丝119

    本文由资深分析师金帝财神微信：934295330，指导黄金，白银，...
FX168 99期货 瑞财网
FX168财经客户端

FX168财经

FX168财经学院

FX168财经

FX168北美