所谓十字星，就是指实体柱很短、上下影线较长的蜡烛图形态。这里认为是多空双方争夺激烈、僵持不下，前期趋势已成强弩之末，趋势反转即将来临。以底部十字星为例，在股票价格经历较长下跌之后，如果出现十字星形态，表明市场空方力量已开始衰竭，多方在逐步蓄积反攻的力量，多空博弈从空方占优演变为势均力敌，股价走势转向的可能性也迅速升高。

超额十字星的计算方法

为了定义超额十字星，我们首先需计算每只股票的超额蜡烛图（或称超额K线）。对于特定股票第T日的超额蜡烛图，其计算步骤如下：
（1）以股票第T-1日的收盘价P(T-1,Close)为基准，计算股票第T日中截止至第t分钟的累积收益率：Rs(T,t) = P(T,t)/P(T-1,Close)-1；
（2）以指数第T-1日的收盘价Q(T-1,Close)为基准，计算指数第T日中截止至第t分钟的累积收益率：Ri(T,t) = Q(T,t)/Q(T-1,Close)-1；
（3）基于上述两个变量，计算股票第T日中第t分钟的累积超额收益：Re(T,t)= Rs(T,t)-Ri_(T,t)；
（4）取第T日累积超额收益的开盘值、最高值、最低值、收盘值，构成第T日的超额蜡烛图。

#库导入
import pandas as pd
import numpy as np
import time
import datetime
import seaborn as sns

#计算一支股票在一定时间内每一天的超额收益十字星
def abnormal_return(stk,index,startdate,enddate):
    tradingdates = get_trading_dates(start_date=startdate, end_date=enddate)
    for date in tradingdates:
        Tday= get_price(stk, start_date=date, end_date=date, frequency='1m', fields='ClosingPx')
        lastday = get_price(stk, start_date=get_previous_trading_date(date), end_date=get_previous_trading_date(date), frequency='1d', fields='ClosingPx')
        Rs = Tday/lastday.values-1
        index_Tday= get_price(index, start_date=date,end_date=date, frequency='1m', fields='ClosingPx')
        index_lastday = get_price(index, start_date=get_previous_trading_date(date), end_date=get_previous_trading_date(date), frequency='1d', fields='ClosingPx')
        Ri =index_Tday/index_lastday.values-1
        Re = Rs - Ri
        
        if date == tradingdates[0]:
            dates = pd.date_range(date,periods=1)
            df = pd.DataFrame(index=dates,columns=['open','close','high','low','bar','upline','downline'])
            df['high'] =  Re.max()
            df['low'] = Re.min()
            df['open'] = Re.ix[0]
            df['close'] = Re.ix[-1]
            df.ix[dates,'bar'] = abs(Re.ix[0]-Re.ix[-1])
            if Re.ix[0]>Re.ix[-1]:
                df.ix[dates,'upline'] = Re.max()-Re.ix[0]
                df.ix[dates,'downline'] = Re.ix[-1] - Re.min()
            else:
                df.ix[dates,'upline'] = Re.max()-Re.ix[-1]
                df.ix[dates,'downline'] = Re.ix[0] - Re.min()
                
        else:
            dates = pd.date_range(date,periods=1)
            df_1 = pd.DataFrame(index=dates,columns=['open','close','high','low','bar','upline','downline'])
            df_1['high'] =  Re.max()
            df_1['low'] = Re.min()
            df_1['open'] = Re.ix[0]
            df_1['close'] = Re.ix[-1]
            df_1.ix[dates,'bar'] = abs(Re.ix[0]-Re.ix[-1])
            df = pd.concat([df,df_1],axis=0)
            if Re.ix[0]>Re.ix[-1]:
                df.ix[dates,'upline'] = Re.max()-Re.ix[0]
                df.ix[dates,'downline'] = Re.ix[-1] - Re.min()
            else:
                df.ix[dates,'upline'] = Re.max()-Re.ix[-1]
                df.ix[dates,'downline'] = Re.ix[0] - Re.min()
            
    return df
        

#识别个股十字星
def distinguish_star(df):
    #方便计算前后收益
    df['No'] = np.arange(1, len(df)+1)
    #识别符合条件的十字星
    df_eligible = df[df.bar<0.001]
    df_eligible = df_eligible[df_eligible.upline>df_eligible.bar*3]
    df_eligible = df_eligible[df_eligible.downline>df_eligible.bar*3]
    #前20日超额涨幅
    for i in df_eligible.index:
        b=df_eligible.loc[i,'No']
        if b > 20 :
            i_20 = df[df.No == b - 20].index
            a_0 = df.loc[i,'close']
            a_20 = df.loc[i_20,'close'].values
            df_eligible.loc[i,'pre20'] = a_0 - a_20
    #前20日超额涨幅
    for i in df_eligible.index:
        b=df_eligible.loc[i,'No']
        if  b < df.ix[-1,'No']-20:
            i_20 = df[df.No == b + 20].index
            a_0 = df.loc[i,'close']
            a_20 = df.loc[i_20,'close'].values
            df_eligible.loc[i,'next20'] = a_0 - a_20
    #前后都缺一个数据，砍头去尾
    #df_eligible = df_eligible.ix[1:-1]
    return df_eligible

stocks = index_components('399905.XSHE')
index =  '399905.XSHE'#中证500
startdate = '2016-9-1'
enddate = '2016-11-25'
df = pd.DataFrame()
for stk in stocks:
    a = abnormal_return(stk, index,startdate,enddate)
    a = distinguish_star(a)
    df = pd.concat([df, a], axis=0)
df

	No	bar	close	downline	high	low	next20	open	pre20	upline
2016-10-18	27	0.00079774	-0.003770	0.00249208	0.008187	-0.006262	-0.005002	-0.002972	0.001894	0.0111595
2016-10-21	30	0.000970781	0.001413	0.00504358	0.006563	-0.003631	0.010432	0.002383	-0.010142	0.00418013
2016-10-27	34	0.000818754	0.004157	0.00332732	0.012912	0.000829	0.013692	0.004975	0.007205	0.00793644
2016-11-15	47	0.000267991	0.001232	0.00740224	0.002138	-0.006438	NaN	0.000964	0.005002	0.000906188
2016-09-12	8	0.000636764	0.001410	0.012914	0.005389	-0.011504	-0.000744	0.002047	NaN	0.00334192
2016-09-21	13	0.000564488	-0.003969	0.00486459	0.000500	-0.008833	-0.030573	-0.003404	NaN	0.00390405
2016-10-18	27	0.000214608	0.003807	0.00628861	0.007978	-0.002696	0.011139	0.003592	0.007759	0.00417093
2016-11-01	37	0.000138128	-0.001252	0.00456401	0.005113	-0.005816	NaN	-0.001114	0.015419	0.00622715
2016-11-02	38	0.000211409	-0.002675	0.0071915	-0.000543	-0.009867	NaN	-0.002464	0.004020	0.00192073
2016-11-07	41	0.000305295	0.000848	0.00701669	0.005692	-0.006168	NaN	0.001154	-0.001203	0.0045387
2016-10-14	25	0.000845177	-0.000488	0.00356247	0.009963	-0.004050	0.030127	0.000357	-0.011280	0.00960517
2016-10-20	29	0.000700113	-0.003276	0.0155403	0.002995	-0.018816	-0.006919	-0.002576	0.016067	0.00557018
2016-11-24	54	0.000586006	-0.004261	0.00522788	0.001193	-0.009489	NaN	-0.003675	0.018970	0.0048682
2016-09-08	6	0.000105506	-0.000450	0.00607526	0.007156	-0.006525	0.007919	-0.000344	NaN	0.00750053
2016-09-21	13	0.000564488	-0.003377	0.00545054	0.005326	-0.008828	0.010758	-0.002813	NaN	0.00813856
2016-10-14	25	0.000369214	0.004267	0.00896215	0.025011	-0.004696	-0.011524	0.004636	-0.003037	0.0203752
2016-10-20	29	0.00023063	0.000511	0.0135073	0.013321	-0.012996	-0.067991	0.000742	-0.048191	0.0125796
2016-10-24	31	0.000727063	-0.005178	0.00838323	0.001363	-0.014288	-0.005292	-0.005905	-0.036011	0.00654016
2016-10-12	23	3.48944e-05	0.000568	0.00690284	0.003820	-0.006369	-0.010141	0.000533	0.006049	0.00325187
2016-11-02	38	0.000659388	0.000575	0.00797619	0.003527	-0.008061	NaN	-0.000085	0.004066	0.00295236
2016-11-11	45	0.000193605	-0.001089	0.00256766	0.003306	-0.003851	NaN	-0.001283	0.007974	0.0043954
2016-11-18	50	0.000641578	0.000647	0.00764976	0.006352	-0.007644	NaN	0.000005	0.002954	0.00570539
2016-11-25	55	0.000100626	-0.004495	0.00260213	0.007634	-0.007097	NaN	-0.004394	0.005382	0.0120279
2016-09-05	3	0.00020701	-0.003016	0.0043616	-0.000573	-0.007378	-0.008153	-0.002809	NaN	0.00223569
2016-09-14	10	0.000829046	0.002123	0.00548877	0.008352	-0.003366	0.006859	0.002952	NaN	0.00540016
2016-10-19	28	0.000776833	0.000612	0.00469935	0.005156	-0.004864	-0.006480	-0.000164	-0.003246	0.00454413
2016-11-07	41	0.000305295	-0.002535	0.0112942	0.003538	-0.013829	NaN	-0.002229	0.034357	0.00576718
2016-11-08	42	0.000579561	-0.002164	0.00544842	0.003421	-0.007612	NaN	-0.001584	0.001299	0.00500517
2016-11-09	43	0.000404826	-0.000740	0.00124617	0.010240	-0.001986	NaN	-0.000335	-0.005877	0.0105754
2016-11-17	49	0.000716673	-0.002889	0.00742067	0.002564	-0.010310	NaN	-0.002172	0.001174	0.00473643
…	…	…	…	…	…	…	…	…	…	…
2016-09-29	19	0.000405036	0.005248	0.00857541	0.009771	-0.003732	0.002637	0.004843	NaN	0.00452285
2016-10-10	21	0.000353476	-0.000871	0.00492891	0.001998	-0.005800	-0.012189	-0.000518	0.005951	0.00251583
2016-10-11	22	0.000893326	-0.002983	0.00294019	0.005045	-0.006817	0.006228	-0.003877	-0.005929	0.00802837
2016-11-01	37	0.000513696	0.003787	0.00332305	0.008657	-0.000049	NaN	0.003274	0.007069	0.0048698
2016-11-04	40	0.000221849	-0.000236	0.00261349	0.002844	-0.003072	NaN	-0.000458	-0.002671	0.00308066
2016-11-21	51	0.000401302	-0.000547	0.00694253	0.003594	-0.007489	NaN	-0.000146	-0.006729	0.00374009
2016-09-29	19	4.13928e-05	-0.002787	0.00617661	0.003703	-0.008964	-0.014448	-0.002746	NaN	0.00644853
2016-10-10	21	0.000586688	-0.002809	0.00825505	0.003330	-0.011651	-0.001846	-0.003396	-0.003616	0.00613935
2016-10-31	36	4.9729e-05	-0.001628	0.00403167	0.013651	-0.005709	NaN	-0.001678	-0.015054	0.0152788
2016-11-01	37	0.00053519	0.002917	0.00402879	0.014210	-0.001112	NaN	0.003452	0.005603	0.0107579
2016-09-02	2	0.000854168	0.000524	0.00902901	0.004768	-0.009359	0.001063	-0.000330	NaN	0.0042435
2016-09-21	13	0.000257833	0.000035	0.00455663	0.004762	-0.004522	-0.007117	0.000293	NaN	0.00446928
2016-09-22	14	0.000494773	-0.001697	0.00362974	0.007605	-0.005327	-0.000656	-0.001202	NaN	0.00880745
2016-09-28	18	0.000565111	0.000581	0.00406779	0.005105	-0.003486	0.003188	0.001147	NaN	0.00395864
2016-09-29	19	4.13928e-05	-0.000279	0.00292331	0.009352	-0.003203	-0.002042	-0.000238	NaN	0.0095905
2016-10-10	21	0.000575985	0.002825	0.00304836	0.007299	-0.000800	0.000411	0.002249	0.014439	0.00447417
2016-10-24	31	0.000760739	-0.003781	0.00282309	0.000378	-0.007365	-0.019246	-0.004542	0.003395	0.00415886
2016-10-27	34	0.000895743	-0.001041	0.00390836	0.003683	-0.004949	-0.004304	-0.000145	0.000656	0.00382803
2016-11-03	39	0.000534562	0.001763	0.00312974	0.008293	-0.001367	NaN	0.002297	0.002042	0.00599582
2016-11-24	54	0.000213575	0.003263	0.00553358	0.005765	-0.002271	NaN	0.003477	0.004304	0.00228796
2016-09-12	8	5.17113e-05	-0.007713	0.00675064	0.005134	-0.014464	-0.047022	-0.007661	NaN	0.0127952
2016-09-19	11	0.00021113	0.002253	0.0101472	0.007446	-0.008105	-0.001974	0.002042	NaN	0.00519277
2016-09-27	17	0.000428801	-0.005663	0.00498969	0.001271	-0.011081	-0.001716	-0.006092	NaN	0.00693369
2016-09-12	8	0.000539117	-0.011235	0.00905839	-0.007475	-0.020293	-0.001961	-0.010696	NaN	0.0032207
2016-09-14	10	0.000328796	-0.001067	0.0096075	0.004199	-0.010674	0.003107	-0.000738	NaN	0.00493743
2016-10-26	33	0.000189334	0.001176	0.00405326	0.006450	-0.002877	0.010020	0.001365	0.012747	0.00508488
2016-11-01	37	0.000489511	0.001344	0.0048214	0.006146	-0.003967	NaN	0.000854	-0.013650	0.00480187
2016-11-10	44	0.000318871	-0.002922	0.00446798	0.001373	-0.007390	NaN	-0.002603	-0.033727	0.00397611
2016-11-11	45	0.000504047	0.012624	0.00349084	0.038857	0.008629	NaN	0.012120	0.015919	0.0262325
2016-11-18	50	0.000674624	-0.002607	0.00560334	0.002171	-0.008211	NaN	-0.001933	0.001567	0.00410374

2003 rows × 10 columns

len(df)

22

#把数据存起来
df.to_csv('十字星蜡烛图.csv')

#读取数据
from six import StringIO
from six import BytesIO
body = get_file('十字星蜡烛图.csv')
data=pd.read_csv(BytesIO(body))
data

	Unnamed: 0	No	bar	close	downline	high	low	next20	open	pre20	upline
0	2016-10-18	27	0.000798	-0.003770	0.002492	0.008187	-0.006262	-0.005002	-0.002972	0.001894	0.011159
1	2016-10-21	30	0.000971	0.001413	0.005044	0.006563	-0.003631	0.010432	0.002383	-0.010142	0.004180
2	2016-10-27	34	0.000819	0.004157	0.003327	0.012912	0.000829	0.013692	0.004975	0.007205	0.007936
3	2016-11-15	47	0.000268	0.001232	0.007402	0.002138	-0.006438	NaN	0.000964	0.005002	0.000906
4	2016-09-12	8	0.000637	0.001410	0.012914	0.005389	-0.011504	-0.000744	0.002047	NaN	0.003342
5	2016-09-21	13	0.000564	-0.003969	0.004865	0.000500	-0.008833	-0.030573	-0.003404	NaN	0.003904
6	2016-10-18	27	0.000215	0.003807	0.006289	0.007978	-0.002696	0.011139	0.003592	0.007759	0.004171
7	2016-11-01	37	0.000138	-0.001252	0.004564	0.005113	-0.005816	NaN	-0.001114	0.015419	0.006227
8	2016-11-02	38	0.000211	-0.002675	0.007191	-0.000543	-0.009867	NaN	-0.002464	0.004020	0.001921
9	2016-11-07	41	0.000305	0.000848	0.007017	0.005692	-0.006168	NaN	0.001154	-0.001203	0.004539
10	2016-10-14	25	0.000845	-0.000488	0.003562	0.009963	-0.004050	0.030127	0.000357	-0.011280	0.009605
11	2016-10-20	29	0.000700	-0.003276	0.015540	0.002995	-0.018816	-0.006919	-0.002576	0.016067	0.005570
12	2016-11-24	54	0.000586	-0.004261	0.005228	0.001193	-0.009489	NaN	-0.003675	0.018970	0.004868
13	2016-09-08	6	0.000106	-0.000450	0.006075	0.007156	-0.006525	0.007919	-0.000344	NaN	0.007501
14	2016-09-21	13	0.000564	-0.003377	0.005451	0.005326	-0.008828	0.010758	-0.002813	NaN	0.008139
15	2016-10-14	25	0.000369	0.004267	0.008962	0.025011	-0.004696	-0.011524	0.004636	-0.003037	0.020375
16	2016-10-20	29	0.000231	0.000511	0.013507	0.013321	-0.012996	-0.067991	0.000742	-0.048191	0.012580
17	2016-10-24	31	0.000727	-0.005178	0.008383	0.001363	-0.014288	-0.005292	-0.005905	-0.036011	0.006540
18	2016-10-12	23	0.000035	0.000568	0.006903	0.003820	-0.006369	-0.010141	0.000533	0.006049	0.003252
19	2016-11-02	38	0.000659	0.000575	0.007976	0.003527	-0.008061	NaN	-0.000085	0.004066	0.002952
20	2016-11-11	45	0.000194	-0.001089	0.002568	0.003306	-0.003851	NaN	-0.001283	0.007974	0.004395
21	2016-11-18	50	0.000642	0.000647	0.007650	0.006352	-0.007644	NaN	0.000005	0.002954	0.005705
22	2016-11-25	55	0.000101	-0.004495	0.002602	0.007634	-0.007097	NaN	-0.004394	0.005382	0.012028
23	2016-09-05	3	0.000207	-0.003016	0.004362	-0.000573	-0.007378	-0.008153	-0.002809	NaN	0.002236
24	2016-09-14	10	0.000829	0.002123	0.005489	0.008352	-0.003366	0.006859	0.002952	NaN	0.005400
25	2016-10-19	28	0.000777	0.000612	0.004699	0.005156	-0.004864	-0.006480	-0.000164	-0.003246	0.004544
26	2016-11-07	41	0.000305	-0.002535	0.011294	0.003538	-0.013829	NaN	-0.002229	0.034357	0.005767
27	2016-11-08	42	0.000580	-0.002164	0.005448	0.003421	-0.007612	NaN	-0.001584	0.001299	0.005005
28	2016-11-09	43	0.000405	-0.000740	0.001246	0.010240	-0.001986	NaN	-0.000335	-0.005877	0.010575
29	2016-11-17	49	0.000717	-0.002889	0.007421	0.002564	-0.010310	NaN	-0.002172	0.001174	0.004736
…	…	…	…	…	…	…	…	…	…	…	…
1973	2016-09-29	19	0.000405	0.005248	0.008575	0.009771	-0.003732	0.002637	0.004843	NaN	0.004523
1974	2016-10-10	21	0.000353	-0.000871	0.004929	0.001998	-0.005800	-0.012189	-0.000518	0.005951	0.002516
1975	2016-10-11	22	0.000893	-0.002983	0.002940	0.005045	-0.006817	0.006228	-0.003877	-0.005929	0.008028
1976	2016-11-01	37	0.000514	0.003787	0.003323	0.008657	-0.000049	NaN	0.003274	0.007069	0.004870
1977	2016-11-04	40	0.000222	-0.000236	0.002613	0.002844	-0.003072	NaN	-0.000458	-0.002671	0.003081
1978	2016-11-21	51	0.000401	-0.000547	0.006943	0.003594	-0.007489	NaN	-0.000146	-0.006729	0.003740
1979	2016-09-29	19	0.000041	-0.002787	0.006177	0.003703	-0.008964	-0.014448	-0.002746	NaN	0.006449
1980	2016-10-10	21	0.000587	-0.002809	0.008255	0.003330	-0.011651	-0.001846	-0.003396	-0.003616	0.006139
1981	2016-10-31	36	0.000050	-0.001628	0.004032	0.013651	-0.005709	NaN	-0.001678	-0.015054	0.015279
1982	2016-11-01	37	0.000535	0.002917	0.004029	0.014210	-0.001112	NaN	0.003452	0.005603	0.010758
1983	2016-09-02	2	0.000854	0.000524	0.009029	0.004768	-0.009359	0.001063	-0.000330	NaN	0.004243
1984	2016-09-21	13	0.000258	0.000035	0.004557	0.004762	-0.004522	-0.007117	0.000293	NaN	0.004469
1985	2016-09-22	14	0.000495	-0.001697	0.003630	0.007605	-0.005327	-0.000656	-0.001202	NaN	0.008807
1986	2016-09-28	18	0.000565	0.000581	0.004068	0.005105	-0.003486	0.003188	0.001147	NaN	0.003959
1987	2016-09-29	19	0.000041	-0.000279	0.002923	0.009352	-0.003203	-0.002042	-0.000238	NaN	0.009590
1988	2016-10-10	21	0.000576	0.002825	0.003048	0.007299	-0.000800	0.000411	0.002249	0.014439	0.004474
1989	2016-10-24	31	0.000761	-0.003781	0.002823	0.000378	-0.007365	-0.019246	-0.004542	0.003395	0.004159
1990	2016-10-27	34	0.000896	-0.001041	0.003908	0.003683	-0.004949	-0.004304	-0.000145	0.000656	0.003828
1991	2016-11-03	39	0.000535	0.001763	0.003130	0.008293	-0.001367	NaN	0.002297	0.002042	0.005996
1992	2016-11-24	54	0.000214	0.003263	0.005534	0.005765	-0.002271	NaN	0.003477	0.004304	0.002288
1993	2016-09-12	8	0.000052	-0.007713	0.006751	0.005134	-0.014464	-0.047022	-0.007661	NaN	0.012795
1994	2016-09-19	11	0.000211	0.002253	0.010147	0.007446	-0.008105	-0.001974	0.002042	NaN	0.005193
1995	2016-09-27	17	0.000429	-0.005663	0.004990	0.001271	-0.011081	-0.001716	-0.006092	NaN	0.006934
1996	2016-09-12	8	0.000539	-0.011235	0.009058	-0.007475	-0.020293	-0.001961	-0.010696	NaN	0.003221
1997	2016-09-14	10	0.000329	-0.001067	0.009608	0.004199	-0.010674	0.003107	-0.000738	NaN	0.004937
1998	2016-10-26	33	0.000189	0.001176	0.004053	0.006450	-0.002877	0.010020	0.001365	0.012747	0.005085
1999	2016-11-01	37	0.000490	0.001344	0.004821	0.006146	-0.003967	NaN	0.000854	-0.013650	0.004802
2000	2016-11-10	44	0.000319	-0.002922	0.004468	0.001373	-0.007390	NaN	-0.002603	-0.033727	0.003976
2001	2016-11-11	45	0.000504	0.012624	0.003491	0.038857	0.008629	NaN	0.012120	0.015919	0.026232
2002	2016-11-18	50	0.000675	-0.002607	0.005603	0.002171	-0.008211	NaN	-0.001933	0.001567	0.004104

2003 rows × 11 columns

data = data[data.pre20 < 0]
data

	Unnamed: 0	No	bar	close	downline	high	low	next20	open	pre20	upline
1	2016-10-21	30	0.000971	0.001413	0.005044	0.006563	-0.003631	0.010432	0.002383	-0.010142	0.004180
9	2016-11-07	41	0.000305	0.000848	0.007017	0.005692	-0.006168	NaN	0.001154	-0.001203	0.004539
10	2016-10-14	25	0.000845	-0.000488	0.003562	0.009963	-0.004050	0.030127	0.000357	-0.011280	0.009605
15	2016-10-14	25	0.000369	0.004267	0.008962	0.025011	-0.004696	-0.011524	0.004636	-0.003037	0.020375
16	2016-10-20	29	0.000231	0.000511	0.013507	0.013321	-0.012996	-0.067991	0.000742	-0.048191	0.012580
17	2016-10-24	31	0.000727	-0.005178	0.008383	0.001363	-0.014288	-0.005292	-0.005905	-0.036011	0.006540
25	2016-10-19	28	0.000777	0.000612	0.004699	0.005156	-0.004864	-0.006480	-0.000164	-0.003246	0.004544
28	2016-11-09	43	0.000405	-0.000740	0.001246	0.010240	-0.001986	NaN	-0.000335	-0.005877	0.010575
32	2016-11-16	48	0.000515	0.002750	0.007597	0.011324	-0.004846	NaN	0.003265	-0.001860	0.008059
33	2016-10-18	27	0.000583	-0.012786	0.002551	0.001859	-0.015338	-0.014888	-0.012203	-0.035546	0.014062
40	2016-11-16	48	0.000469	-0.002171	0.002159	0.002625	-0.004330	NaN	-0.001702	-0.005952	0.004327
45	2016-10-31	36	0.000909	-0.001988	0.009070	0.003731	-0.011059	NaN	-0.001080	-0.014830	0.004811
47	2016-10-17	26	0.000013	-0.003381	0.002299	0.002578	-0.005693	-0.080159	-0.003393	-0.011641	0.005958
48	2016-11-08	42	0.000626	0.001609	0.006969	0.004473	-0.005986	NaN	0.000983	-0.002819	0.002864
49	2016-11-07	41	0.000305	-0.000963	0.002803	0.003681	-0.003767	NaN	-0.000658	-0.037370	0.004339
51	2016-11-02	38	0.000897	0.004901	0.011086	0.012621	-0.007082	NaN	0.004004	-0.046719	0.007720
52	2016-11-03	39	0.000489	-0.005506	0.014324	-0.001247	-0.020319	NaN	-0.005995	-0.038351	0.004260
54	2016-11-18	50	0.000249	-0.002399	0.007338	0.004994	-0.009737	NaN	-0.002150	-0.012383	0.007144
58	2016-10-21	30	0.000465	-0.007672	0.007212	-0.002406	-0.014884	-0.004436	-0.007206	-0.017141	0.004800
65	2016-11-10	44	0.000187	-0.001195	0.008200	0.004600	-0.009395	NaN	-0.001008	-0.002904	0.005608
67	2016-11-21	51	0.000617	-0.010909	0.002643	0.000278	-0.013552	NaN	-0.010292	-0.031027	0.010570
71	2016-11-24	54	0.000018	-0.013320	0.002637	-0.006773	-0.015957	NaN	-0.013302	-0.008206	0.006529
81	2016-11-03	39	0.000377	-0.002766	0.005016	0.004506	-0.007781	NaN	-0.002389	-0.004435	0.006894
82	2016-11-14	46	0.000374	-0.002282	0.016615	-0.000118	-0.019271	NaN	-0.002655	-0.012659	0.002163
83	2016-11-22	52	0.000475	-0.001931	0.006439	0.000791	-0.008846	NaN	-0.002407	-0.021723	0.002723
92	2016-11-02	38	0.000197	0.000635	0.008563	0.002303	-0.008125	NaN	0.000438	-0.000260	0.001668
95	2016-11-21	51	0.000668	0.000946	0.003725	0.012448	-0.003446	NaN	0.000279	-0.001767	0.011502
96	2016-11-22	52	0.000439	0.001828	0.005100	0.009302	-0.003272	NaN	0.002266	-0.004495	0.007035
97	2016-11-25	55	0.000080	0.000211	0.010224	0.006349	-0.010094	NaN	0.000131	-0.001785	0.006138
103	2016-10-28	35	0.000133	0.005344	0.013268	0.011861	-0.007923	NaN	0.005477	-0.029642	0.006384
…	…	…	…	…	…	…	…	…	…	…	…
1878	2016-10-17	26	0.000861	0.008709	0.007887	0.021834	0.000822	-0.085675	0.009570	-0.005388	0.012264
1880	2016-11-21	51	0.000807	0.003567	0.012202	0.017646	-0.009442	NaN	0.002760	-0.037314	0.014079
1882	2016-11-07	41	0.000305	0.001248	0.002353	0.011023	-0.001105	NaN	0.001553	-0.002848	0.009470
1883	2016-11-14	46	0.000424	0.003397	0.009806	0.008098	-0.006833	NaN	0.002973	-0.005312	0.004700
1888	2016-11-03	39	0.000666	0.000192	0.004033	0.006258	-0.004508	NaN	-0.000475	-0.007908	0.006067
1894	2016-11-23	53	0.000488	-0.003042	0.008321	-0.001185	-0.011851	NaN	-0.003530	-0.020281	0.001857
1896	2016-10-14	25	0.000811	0.002365	0.003132	0.009537	-0.000767	-0.014723	0.003177	-0.019420	0.006360
1900	2016-10-11	22	0.000039	0.004456	0.013314	0.008419	-0.008857	-0.037308	0.004495	-0.000872	0.003923
1905	2016-10-13	24	0.000241	-0.002483	0.005701	0.001448	-0.008184	-0.002806	-0.002242	-0.029952	0.003691
1908	2016-10-13	24	0.000241	-0.000323	0.002504	0.002217	-0.002827	0.012599	-0.000083	-0.011070	0.002300
1915	2016-10-25	32	0.000507	-0.002262	0.004349	0.002844	-0.006611	-0.003734	-0.001755	-0.021364	0.004599
1929	2016-11-18	50	0.000070	-0.000559	0.002831	0.005383	-0.003460	NaN	-0.000628	-0.007454	0.005942
1938	2016-11-02	38	0.000197	-0.003543	0.002251	0.002900	-0.005794	NaN	-0.003346	-0.000717	0.006246
1941	2016-10-20	29	0.000665	0.003019	0.002862	0.007607	0.000157	0.012531	0.003685	-0.023562	0.003922
1943	2016-10-12	23	0.000007	-0.003739	0.003059	0.007798	-0.006806	-0.019094	-0.003747	-0.022097	0.011537
1946	2016-11-15	47	0.000720	-0.003210	0.004632	0.003058	-0.007841	NaN	-0.002489	-0.002776	0.005547
1948	2016-10-10	21	0.000376	0.002255	0.003476	0.015210	-0.001596	0.004594	0.001880	-0.007343	0.012955
1949	2016-10-18	27	0.000876	-0.001043	0.005166	0.004163	-0.006209	-0.032124	-0.000167	-0.005427	0.004330
1950	2016-10-25	32	0.000711	-0.010319	0.006101	-0.003082	-0.017132	-0.005389	-0.011031	-0.017904	0.007237
1951	2016-10-26	33	0.000370	-0.004374	0.007323	-0.001106	-0.011697	0.001434	-0.004004	-0.012735	0.002898
1957	2016-10-20	29	0.000231	-0.000459	0.001089	0.003078	-0.001548	-0.001621	-0.000228	-0.000899	0.003306
1965	2016-11-22	52	0.000138	-0.002269	0.007186	0.003832	-0.009592	NaN	-0.002407	-0.007784	0.006101
1970	2016-11-04	40	0.000206	-0.001971	0.005055	-0.000329	-0.007026	NaN	-0.001765	-0.003460	0.001436
1975	2016-10-11	22	0.000893	-0.002983	0.002940	0.005045	-0.006817	0.006228	-0.003877	-0.005929	0.008028
1977	2016-11-04	40	0.000222	-0.000236	0.002613	0.002844	-0.003072	NaN	-0.000458	-0.002671	0.003081
1978	2016-11-21	51	0.000401	-0.000547	0.006943	0.003594	-0.007489	NaN	-0.000146	-0.006729	0.003740
1980	2016-10-10	21	0.000587	-0.002809	0.008255	0.003330	-0.011651	-0.001846	-0.003396	-0.003616	0.006139
1981	2016-10-31	36	0.000050	-0.001628	0.004032	0.013651	-0.005709	NaN	-0.001678	-0.015054	0.015279
1999	2016-11-01	37	0.000490	0.001344	0.004821	0.006146	-0.003967	NaN	0.000854	-0.013650	0.004802
2000	2016-11-10	44	0.000319	-0.002922	0.004468	0.001373	-0.007390	NaN	-0.002603	-0.033727	0.003976

565 rows × 11 columns

sns.jointplot(data['pre20'], data['next20'], kind='reg', size=12)

<seaborn.axisgrid.JointGrid at 0x7fb1604b9eb8>

不太明显，只有两个月的数据样本，但是回归明显向上偏，可见因子还是有效果
下面我选择了中证500成分股中的20支来计算

stocks_1 = ['601886.XSHG','601126.XSHG','000099.XSHE','600466.XSHG','600587.XSHG','002353.XSHE','002293.XSHE', '002489.XSHE', '002050.XSHE', '000592.XSHE', '000550.XSHE', '002161.XSHE', '601717.XSHG', '002368.XSHE', '300043.XSHE', '300244.XSHE', '002640.XSHE','000829.XSHE','300072.XSHE',
 '002063.XSHE']
index =  '399905.XSHE'#中证500
startdate_1 = '2014-1-1'
enddate_1 = '2016-11-25'
df_1 = pd.DataFrame()
for stk in stocks_1:
    a = abnormal_return(stk, index,startdate_1,enddate_1)
    a = distinguish_star(a)
    df_1 = pd.concat([df_1, a], axis=0)
df_1

	open	close	high	low	bar	upline	downline	No	pre20	next20
2014-01-07	-0.001897	-0.001943	0.006458	-0.006651	4.55517e-05	0.00835467	0.00470884	4	NaN	-0.001856
2014-02-07	-0.005527	-0.006007	0.013378	-0.009961	0.000480204	0.0189052	0.00395399	22	0.007638	-0.000179
2014-03-05	0.000421	0.001246	0.012940	-0.006317	0.000824954	0.0116936	0.00673872	40	0.015679	-0.003833
2014-03-25	-0.009716	-0.009470	0.013240	-0.010665	0.000246501	0.0227101	0.000949013	54	-0.025961	-0.018466
2014-04-11	0.001609	0.001562	0.007111	-0.001851	4.68696e-05	0.00550171	0.00341342	66	0.006432	0.004684
2014-04-29	-0.008551	-0.008279	0.006034	-0.012187	0.000271631	0.0143131	0.00363623	78	-0.013229	-0.004456
2014-05-12	0.002395	0.002756	0.017588	-0.004310	0.000360935	0.0148322	0.00670472	85	0.012006	0.001380
2014-05-14	0.001632	0.002367	0.006712	-0.002074	0.000734458	0.00434597	0.00370598	87	0.016891	0.009744
2014-05-23	-0.004080	-0.004345	0.000119	-0.008800	0.000265041	0.00419809	0.00445587	94	-0.013341	-0.000664
2014-05-30	0.005732	0.006018	0.016081	-0.000791	0.00028621	0.0100633	0.00652331	99	0.010092	0.015166
2014-06-03	0.001756	0.001703	0.006032	-0.003826	5.31727e-05	0.00427542	0.00552878	100	-0.018557	0.003737
2014-06-11	-0.003509	-0.003131	0.000217	-0.008075	0.000377309	0.00334814	0.00456638	106	-0.000009	0.008957
2014-07-23	-0.002892	-0.002879	0.006577	-0.005467	1.26583e-05	0.00945605	0.00257562	136	-0.009436	-0.012740
2014-11-12	-0.004968	-0.004038	0.002243	-0.008986	0.000930737	0.00628091	0.00401753	210	-0.008778	-0.024373
2014-11-13	0.020005	0.019190	0.025552	0.014111	0.000815201	0.00554659	0.00507876	211	0.028630	0.031774
2014-11-21	-0.004224	-0.004553	-0.001483	-0.009746	0.000329642	0.00274062	0.00519264	217	-0.003224	0.000472
2015-01-06	0.000965	-0.000034	0.011175	-0.012660	0.000998943	0.0102104	0.0126261	247	0.019187	0.002940
2015-01-07	-0.004445	-0.003814	0.009787	-0.012481	0.000631839	0.0136004	0.00803581	248	-0.009749	-0.002055
2015-01-12	-0.001993	-0.001395	0.003410	-0.008329	0.000597669	0.00480551	0.00633593	251	0.011189	-0.008026
2015-01-21	-0.003244	-0.004131	0.002246	-0.008659	0.000887216	0.00548921	0.00452844	258	-0.006634	-0.003735
2015-10-27	-0.030401	-0.030548	-0.003477	-0.061991	0.000146632	0.0269246	0.0314432	442	-0.038208	-0.068074
2015-12-25	-0.003435	-0.004276	0.011034	-0.009270	0.000841036	0.014469	0.00499451	485	-0.050652	-0.064786
2016-02-16	0.008519	0.009004	0.013755	-0.016159	0.000484969	0.00475163	0.0246778	516	0.019557	0.025104
2016-02-18	-0.000986	-0.000500	0.014788	-0.012698	0.000485495	0.0152881	0.0117118	518	-0.042430	-0.016038
2016-08-04	-0.002302	-0.001616	0.009708	-0.005003	0.000686598	0.0113239	0.00270023	634	-0.016891	-0.009812
2016-08-05	0.000088	0.000812	0.007674	-0.006572	0.000723881	0.00686153	0.00666059	635	0.006899	-0.003935
2016-08-12	-0.002550	-0.003317	0.015110	-0.006441	0.000767386	0.0176602	0.00312398	640	-0.022925	-0.012306
2016-08-18	0.000405	0.000585	0.028629	-0.007071	0.000179878	0.0280438	0.00747644	644	0.014234	0.007435
2016-08-24	-0.001738	-0.000990	0.002079	-0.005246	0.000748208	0.00306858	0.00350864	648	0.036781	-0.001524
2016-09-19	-0.006974	-0.006850	0.008454	-0.010255	0.00012408	0.0153043	0.00328066	664	-0.007435	-0.019789
…	…	…	…	…	…	…	…	…	…	…
2014-03-14	0.005339	0.004651	0.011939	-0.001552	0.000688738	0.00659919	0.00620248	47	-0.034051	0.012145
2014-03-26	-0.001957	-0.002595	0.000969	-0.004665	0.000638664	0.00292573	0.00206933	55	-0.018953	-0.013884
2014-04-02	-0.001095	-0.000655	0.001874	-0.005735	0.000440058	0.00252931	0.0046401	60	-0.002772	0.073600
2014-06-27	-0.009957	-0.009092	0.000361	-0.014798	0.000865306	0.00945291	0.00484102	118	0.046355	-0.017387
2014-07-30	0.005364	0.006136	0.008639	-0.012577	0.000771817	0.00250293	0.0179418	141	0.035309	0.054842
2014-08-18	0.001273	0.001144	0.008594	-0.005845	0.00012896	0.00732149	0.00698941	154	0.013711	0.015067
2014-09-11	0.004093	0.003650	0.014485	0.000533	0.000443532	0.010391	0.00311663	171	0.017122	-0.009726
2014-09-25	-0.004175	-0.003376	0.010172	-0.009249	0.000799809	0.0135475	0.00507392	181	0.045331	0.019990
2014-11-28	-0.004615	-0.003657	0.011943	-0.016064	0.000958095	0.0156003	0.0114492	222	0.009065	0.011809
2015-01-27	-0.006059	-0.005392	0.004997	-0.023593	0.000667311	0.0103882	0.017534	262	0.010074	0.010936
2015-04-16	-0.021033	-0.021159	0.023717	-0.039796	0.000125134	0.0447502	0.018637	313	-0.048245	-0.027740
2015-05-12	-0.011428	-0.012081	0.046723	-0.014717	0.000653123	0.0581516	0.00263569	330	-0.001851	0.011615
2015-07-23	-0.012344	-0.011370	0.000708	-0.025381	0.000973568	0.0120778	0.0130374	381	0.033617	-0.004172
2015-08-13	0.011248	0.011441	0.020079	-0.001688	0.000193306	0.00863767	0.0129358	396	-0.040084	0.042951
2015-09-02	-0.013187	-0.012656	0.027920	-0.036755	0.0005314	0.0405758	0.0235673	410	0.003805	-0.061268
2015-10-13	-0.012480	-0.012406	0.004185	-0.018233	7.38099e-05	0.016591	0.00575265	432	-0.067521	0.010086
2015-12-17	0.006922	0.007816	0.015884	-0.001925	0.000894005	0.00806864	0.00884642	479	-0.010617	0.014929
2016-02-04	0.003553	0.004552	0.014893	-0.002413	0.000998968	0.0103405	0.00596647	513	0.018235	-0.017088
2016-03-03	-0.001259	-0.001851	0.020796	-0.006639	0.00059226	0.0220553	0.0047874	528	0.003676	0.005921
2016-03-25	0.003776	0.003463	0.006021	-0.007714	0.000312261	0.00224559	0.0111773	544	0.046411	0.011860
2016-04-14	0.004278	0.003922	0.012181	-0.003184	0.000355497	0.00790288	0.00710585	557	0.005424	0.016256
2016-04-15	-0.004720	-0.004970	-0.002328	-0.011955	0.000249798	0.00239256	0.00698465	558	-0.025893	-0.013047
2016-04-26	-0.004821	-0.004057	-0.000586	-0.012377	0.000763958	0.00347106	0.00755643	565	0.005957	-0.059779
2016-06-01	-0.006262	-0.005929	0.003318	-0.013084	0.00033289	0.0092476	0.00682147	590	-0.001883	0.014125
2016-07-11	-0.001556	-0.001341	0.013781	-0.003138	0.000214684	0.0151221	0.00158217	616	-0.021395	0.000101
2016-08-18	0.000405	-0.000315	0.003329	-0.005805	0.000720212	0.00292416	0.00548998	644	-0.012813	0.001219
2016-08-30	-0.002213	-0.002587	0.006481	-0.004237	0.000373993	0.00869423	0.00165023	652	0.000442	-0.005860
2016-09-28	-0.002059	-0.002126	0.001013	-0.004687	6.68724e-05	0.00307291	0.00256019	671	-0.004542	0.009323
2016-10-12	-0.000907	-0.001598	0.004475	-0.004918	0.000690257	0.00538284	0.00332033	676	0.002437	0.004999
2016-10-13	0.000641	0.000400	0.005512	-0.003841	0.000240639	0.00487045	0.00424137	677	-0.014095	-0.008323

602 rows × 10 columns

df_1 = df_1[df_1.pre20<0]
df_1.head()

	open	close	high	low	bar	upline	downline	No	pre20	next20
2014-03-25	-0.009716	-0.009470	0.013240	-0.010665	0.000246501	0.0227101	0.000949013	54	-0.025961	-0.018466
2014-04-29	-0.008551	-0.008279	0.006034	-0.012187	0.000271631	0.0143131	0.00363623	78	-0.013229	-0.004456
2014-05-23	-0.004080	-0.004345	0.000119	-0.008800	0.000265041	0.00419809	0.00445587	94	-0.013341	-0.000664
2014-06-03	0.001756	0.001703	0.006032	-0.003826	5.31727e-05	0.00427542	0.00552878	100	-0.018557	0.003737
2014-06-11	-0.003509	-0.003131	0.000217	-0.008075	0.000377309	0.00334814	0.00456638	106	-0.000009	0.008957

sns.jointplot(df_1['pre20'], df_1['next20'], kind='reg', size=12)

<seaborn.axisgrid.JointGrid at 0x7fbf5d5265c0>

虽然样本还是少了一些，但是应该已经足够说明问题了
这个因子是比较有效的