涨停效应研究¶
In [7]:
#开始时间
startdate = '20140101'
#结束时间
enddate = '20190227'
#全市场股票获取
stock = list(get_all_securities('stock',enddate).index)
#数据获取
data = get_price(stock,startdate,enddate,'1d',['close','low','open','high'],True,'pre',is_panel=1)
close = data['close']
low = data['low']
opens = data['open']
In [9]:
#最低价非涨停价
limit_down = close-low
limit_down = limit_down[limit_down!=0]
#开盘价为涨停价
limit_open = round(close.shift()*1.1,2)-opens
limit_open = limit_open[limit_open==0]
#开板
df = limit_open+limit_down
df = df.T
non_limit = {}
num = 0
for s in list(df.columns):
day = s.strftime('%Y-%m-%d')
dt = df[s]
dt = list(dt[dt>0].index)
if len(dt)>0:
num +=len(dt)
non_limit[day]=dt
else:
pass
non_limit
print('开板数:{}'.format(num))
limitdt = {}
num = 0
tradeday = list(get_trade_days(startdate, '20200202', count=None).strftime('%Y-%m-%d'))
for d in tradeday:
print(tradeday.index(d),len(tradeday))
if d in list(non_limit.keys()):
day = tradeday[tradeday.index(d)+1]
stockdata = get_price(non_limit[d],None,day,'1m',['open','high','close','low'],True,'pre',bar_count=241,is_panel=1)
pc = stockdata['close'].iloc[0]
highlimit = round(pc*1.1,2)
c = stockdata['close'].iloc[-240:]
h = stockdata['high'].iloc[-240:]
l = stockdata['low'].iloc[-240:]
for t in list(range(1,240)):
p1 = h.iloc[t]-highlimit
p1 = list(p1[p1==0].index)
p2 = c.iloc[t-1]-highlimit
p2 = list(p2[p2<0].index)
stock = list((set(p1)&set(p2)))
if d in list(limitdt.keys()):
limitdt[d] = list(set(limitdt[d]+stock))
else:
limitdt[d] = list(stock)
limitdt
开板数:1958 0 1480 1 1480 2 1480 3 1480 4 1480 5 1480 6 1480 7 1480 8 1480 9 1480 10 1480 11 1480 12 1480 13 1480 14 1480 15 1480 16 1480 17 1480 18 1480 19 1480 20 1480 21 1480 22 1480 23 1480 24 1480 25 1480 26 1480 27 1480 28 1480 29 1480 30 1480 31 1480 32 1480 33 1480 34 1480 35 1480 36 1480 37 1480 38 1480 39 1480 40 1480 41 1480 42 1480 43 1480 44 1480 45 1480 46 1480 47 1480 48 1480 49 1480 50 1480 51 1480 52 1480 53 1480 54 1480 55 1480 56 1480 57 1480 58 1480 59 1480 60 1480 61 1480 62 1480 63 1480 64 1480 65 1480 66 1480 67 1480 68 1480 69 1480 70 1480 71 1480 72 1480 73 1480 74 1480 75 1480 76 1480 77 1480 78 1480 79 1480 80 1480 81 1480 82 1480 83 1480 84 1480 85 1480 86 1480 87 1480 88 1480 89 1480 90 1480 91 1480 92 1480 93 1480 94 1480 95 1480 96 1480 97 1480 98 1480 99 1480 100 1480 101 1480 102 1480 103 1480 104 1480 105 1480 106 1480 107 1480 108 1480 109 1480 110 1480 111 1480 112 1480 113 1480 114 1480 115 1480 116 1480 117 1480 118 1480 119 1480 120 1480 121 1480 122 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Out[9]:
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'2018-07-03': ['601330.SH', '002374.SZ', '002211.SZ'],
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'300163.SZ',
'600106.SH',
'002021.SZ',
'000955.SZ',
'600255.SH',
'600186.SH'],
'2018-07-30': ['000509.SZ', '600225.SH'],
'2018-07-31': ['603713.SH'],
'2018-08-01': ['002401.SZ'],
'2018-08-02': ['600853.SH', '002400.SZ', '600278.SH'],
'2018-08-03': ['300240.SZ', '000633.SZ'],
'2018-08-06': ['600281.SH'],
'2018-08-07': ['300392.SZ'],
'2018-08-08': ['600470.SH'],
'2018-08-09': ['002315.SZ', '300392.SZ', '603657.SH'],
'2018-08-10': ['600986.SH', '002691.SZ', '002523.SZ'],
'2018-08-14': ['603557.SH', '002331.SZ'],
'2018-08-15': ['600455.SH', '002193.SZ'],
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'2018-08-20': ['300724.SZ'],
'2018-08-21': [],
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'2018-08-24': [],
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'2018-09-04': [],
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'2018-09-07': ['002893.SZ', '300688.SZ'],
'2018-09-10': ['002906.SZ', '002143.SZ', '002443.SZ', '600312.SH'],
'2018-09-12': ['002259.SZ'],
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'2018-09-17': ['000911.SZ'],
'2018-09-18': ['603703.SH'],
'2018-09-19': ['603843.SH'],
'2018-09-20': ['000806.SZ', '000586.SZ', '300483.SZ'],
'2018-09-21': ['300247.SZ', '002002.SZ'],
'2018-09-25': ['600746.SH', '600297.SH'],
'2018-09-26': ['000593.SZ', '300104.SZ'],
'2018-09-27': ['600746.SH'],
'2018-09-28': [],
'2018-10-08': [],
'2018-10-10': [],
'2018-10-11': ['300749.SZ', '300216.SZ'],
'2018-10-12': ['002758.SZ', '002937.SZ', '002827.SZ'],
'2018-10-15': ['300131.SZ'],
'2018-10-16': ['603936.SH', '002528.SZ', '600239.SH'],
'2018-10-17': ['600555.SH', '002054.SZ', '300266.SZ', '300392.SZ'],
'2018-10-18': ['300508.SZ', '300748.SZ', '300635.SZ', '300736.SZ'],
'2018-10-19': [],
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'000622.SZ',
'300694.SZ',
'600143.SH',
'600816.SH',
'601990.SH',
'601377.SH'],
'2018-10-24': ['002654.SZ', '002575.SZ', '002112.SZ'],
'2018-10-25': ['002232.SZ', '002451.SZ'],
'2018-10-26': ['603999.SH'],
'2018-10-29': ['300279.SZ', '300492.SZ', '600173.SH'],
'2018-10-31': ['300492.SZ'],
'2018-11-01': ['300748.SZ', '300222.SZ', '603996.SH', '000633.SZ'],
'2018-11-02': ['000559.SZ', '002689.SZ', '000150.SZ', '000633.SZ'],
'2018-11-05': ['300724.SZ', '600319.SH', '300748.SZ', '002575.SZ'],
'2018-11-06': ['000917.SZ',
'002660.SZ',
'600462.SH',
'300274.SZ',
'300122.SZ'],
'2018-11-07': ['600290.SH',
'600796.SH',
'300688.SZ',
'600635.SH',
'002328.SZ',
'300618.SZ',
'600210.SH',
'600053.SH',
'603366.SH'],
'2018-11-08': ['002575.SZ'],
'2018-11-09': ['600624.SH',
'002054.SZ',
'002622.SZ',
'000966.SZ',
'300464.SZ',
'002420.SZ',
'600128.SH'],
'2018-11-12': ['600683.SH', '300151.SZ'],
'2018-11-13': ['600463.SH'],
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'002054.SZ',
'000068.SZ',
'002571.SZ',
'002295.SZ',
'000533.SZ',
'002584.SZ',
'600235.SH',
'002114.SZ',
'002660.SZ'],
'2018-11-15': ['000610.SZ',
'300689.SZ',
'000068.SZ',
'300751.SZ',
'002708.SZ'],
'2018-11-16': ['603101.SH',
'000058.SZ',
'300236.SZ',
'002177.SZ',
'600461.SH'],
'2018-11-19': ['000566.SZ',
'300234.SZ',
'600761.SH',
'600053.SH',
'600119.SH',
'002026.SZ'],
'2018-11-20': ['002141.SZ', '000856.SZ'],
'2018-11-21': ['600825.SH', '002337.SZ', '300674.SZ'],
'2018-11-22': ['002591.SZ', '300682.SZ'],
'2018-11-23': ['601319.SH', '002676.SZ', '300449.SZ'],
'2018-11-26': ['603220.SH'],
'2018-11-27': ['000948.SZ'],
'2018-11-28': ['300240.SZ', '603017.SH', '002288.SZ'],
'2018-11-29': ['300343.SZ', '600936.SH'],
'2018-12-03': ['601858.SH', '300698.SZ', '002103.SZ', '000722.SZ'],
'2018-12-04': ['603335.SH'],
'2018-12-05': [],
'2018-12-06': ['300006.SZ'],
'2018-12-07': [],
'2018-12-12': ['002076.SZ'],
'2018-12-13': ['300585.SZ'],
'2018-12-18': ['002243.SZ'],
'2018-12-19': ['300091.SZ', '002845.SZ'],
'2018-12-21': [],
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'2018-12-27': ['300635.SZ', '300407.SZ', '300265.SZ'],
'2018-12-28': ['300545.SZ', '601619.SH'],
'2019-01-02': [],
'2019-01-03': [],
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'600677.SH',
'002498.SZ',
'600353.SH',
'300351.SZ',
'002356.SZ',
'002130.SZ'],
'2019-01-09': ['600452.SH'],
'2019-01-10': ['300076.SZ', '300328.SZ'],
'2019-01-11': ['002941.SZ', '300096.SZ'],
'2019-01-14': ['300111.SZ',
'002548.SZ',
'601068.SH',
'300694.SZ',
'600218.SH',
'603220.SH',
'000018.SZ'],
'2019-01-15': [],
'2019-01-16': ['600446.SH', '002063.SZ'],
'2019-01-18': ['600470.SH', '300116.SZ', '300648.SZ', '600589.SH'],
'2019-01-21': ['300693.SZ'],
'2019-01-22': ['002755.SZ', '002170.SZ', '300503.SZ'],
'2019-01-23': [],
'2019-01-24': ['002481.SZ', '002170.SZ', '601811.SH'],
'2019-01-25': ['600721.SH'],
'2019-01-28': ['300547.SZ', '000637.SZ', '300096.SZ'],
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'2019-01-31': ['300250.SZ'],
'2019-02-01': ['002011.SZ'],
'2019-02-11': ['600532.SH', '000802.SZ'],
'2019-02-12': ['600318.SH'],
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'000536.SZ',
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'300256.SZ',
'300566.SZ',
'300128.SZ',
'300263.SZ'],
'2019-02-15': [],
'2019-02-18': ['002045.SZ', '002099.SZ', '300512.SZ', '601208.SH'],
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'002348.SZ',
'002426.SZ',
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'300545.SZ',
'300066.SZ',
'600584.SH',
'002006.SZ',
'002660.SZ',
'300466.SZ'],
'2019-02-20': ['600135.SH', '600393.SH', '600095.SH'],
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'600226.SH',
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'600446.SH',
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'600776.SH',
'000536.SZ',
'600509.SH',
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'601860.SH',
'002423.SZ',
'300211.SZ',
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'300638.SZ',
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In [28]:
tradeday = list(get_trade_days(startdate, '20200202', count=None).strftime('%Y-%m-%d'))
for d in tradeday[:200]:
if d in list(limitdt.keys()):
if len(limitdt[d])==0:
pass
else:
day = tradeday[tradeday.index(d)+1]
stockdata = get_price(limitdt[d],None,day,'1m',['close'],True,'pre',bar_count=241,is_panel=1)
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
plt.style.use('seaborn')
for s in list(stockdata['close'].columns):
pc = stockdata['close'][s].iloc[0]
highlimit = round(pc*1.1,2)
lowlimit = round(pc*0.9,2)
fig = plt.figure()
axes = fig.add_axes([0.1, 0.1, 1, 0.5]) #插入面板
x1_list=[highlimit]+list(stockdata['close'][s])[-240:]
y=np.array(x1_list)
x=np.array(range(0,len(x1_list)))
axes.plot(x, y, 'y')
axes.set_ylabel('price',fontsize=15)
axes.set_title('{} Price trend - {} '.format(day,s),fontsize=18)
#设置X轴
axes.set_xticks([0,30,60,90,120,150,180,210,240])
axes.set_xticklabels(['open','10:00','11:00','11:30','13:00','13:30','14:00','14:30','15:00'], fontsize=15)
#设置y轴
axes.set_yticks([lowlimit,highlimit])
In [29]:
datadf = pd.DataFrame(columns=['stock','date'])
for d in list(limitdt.keys()):
for stock in limitdt[d]:
datadf.loc[d]=[stock,d]
In [30]:
startdate = '20140101'
enddate = '20190227'
stock = list(get_all_securities('stock',enddate).index)
alldata = get_price(stock,startdate,enddate,'1d',['close','low','open','high'],True,'pre',is_panel=1)
closedf = alldata['close']
highdf = alldata['high']
lowdf = alldata['low']
opendf = alldata['open']
In [31]:
datadf['buyprice'] = datadf['date'].apply(lambda x:highdf.loc[x][datadf['stock'][x]])
datadf['当日收盘价'] = datadf['date'].apply(lambda x:closedf.loc[x][datadf['stock'][x]])
datadf = datadf[datadf['date']!='2019-02-25']
datadf = datadf[datadf['date']!='2019-02-26']
datadf = datadf[datadf['date']!='2019-02-27']#没有足够数据算第三天
for t in range(1,4):
txt = str(t)+str('日收盘价')
datadf[txt] = datadf['date'].apply(lambda x:closedf.loc[list(highdf.index)[list(highdf.index.strftime('%Y-%m-%d')).index(x)+t]][datadf['stock'][x]])/datadf['buyprice']-1
txt = str(t)+str('日开盘价')
datadf[txt] = datadf['date'].apply(lambda x:opendf.loc[list(highdf.index)[list(highdf.index.strftime('%Y-%m-%d')).index(x)+t]][datadf['stock'][x]])/datadf['buyprice']-1
txt = str(t)+str('日最高价')
datadf[txt] = datadf['date'].apply(lambda x:highdf.loc[list(highdf.index)[list(highdf.index.strftime('%Y-%m-%d')).index(x)+t]][datadf['stock'][x]])/datadf['buyprice']-1
txt = str(t)+str('日最低价')
datadf[txt] = datadf['date'].apply(lambda x:lowdf.loc[list(highdf.index)[list(highdf.index.strftime('%Y-%m-%d')).index(x)+t]][datadf['stock'][x]])/datadf['buyprice']-1
datadf
Out[31]:
| stock | date | buyprice | 当日收盘价 | 1日收盘价 | 1日开盘价 | 1日最高价 | 1日最低价 | 2日收盘价 | 2日开盘价 | 2日最高价 | 2日最低价 | 3日收盘价 | 3日开盘价 | 3日最高价 | 3日最低价 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2018-04-10 | 300589.SZ | 2018-04-10 | 14.94 | 14.09 | 0.038153 | -0.072289 | 0.038153 | -0.113119 | 0.141901 | 0.037483 | 0.141901 | -0.013387 | 0.027443 | 0.099732 | 0.140562 | 0.027443 |
| 2015-02-04 | 000875.SZ | 2015-02-04 | 5.28 | 5.28 | 0.100379 | 0.041667 | 0.100379 | 0.009470 | 0.090909 | 0.115530 | 0.181818 | 0.062500 | 0.009470 | 0.045455 | 0.077652 | -0.017045 |
| 2015-07-14 | 000969.SZ | 2015-07-14 | 12.88 | 12.88 | -0.100155 | -0.011646 | -0.010093 | -0.100155 | -0.068323 | -0.103261 | -0.049689 | -0.190217 | -0.010870 | -0.067547 | -0.004658 | -0.078416 |
| 2018-11-20 | 000856.SZ | 2018-11-20 | 14.07 | 14.07 | 0.055437 | -0.026297 | 0.100213 | -0.050462 | -0.001421 | 0.016347 | 0.041222 | -0.021322 | 0.014925 | -0.014215 | 0.073205 | -0.015636 |
| 2014-04-03 | 000918.SZ | 2014-04-03 | 3.11 | 3.03 | -0.045016 | -0.054662 | -0.032154 | -0.083601 | -0.080386 | -0.057878 | -0.045016 | -0.086817 | -0.093248 | -0.077170 | -0.070740 | -0.102894 |
| 2018-09-27 | 600746.SH | 2018-09-27 | 6.67 | 6.18 | 0.019490 | -0.058471 | 0.019490 | -0.058471 | -0.041979 | 0.038981 | 0.106447 | -0.041979 | -0.079460 | -0.046477 | -0.016492 | -0.092954 |
| 2018-05-16 | 300175.SZ | 2018-05-16 | 6.18 | 6.18 | 0.006472 | 0.100324 | 0.100324 | 0.003236 | -0.056634 | -0.045307 | -0.019417 | -0.063107 | -0.072816 | -0.087379 | -0.066343 | -0.098706 |
| 2017-05-02 | 000158.SZ | 2017-05-02 | 11.84 | 11.84 | 0.036318 | 0.041385 | 0.089527 | 0.023649 | 0.007601 | -0.008446 | 0.069257 | -0.028716 | -0.069257 | -0.034628 | -0.007601 | -0.070946 |
| 2016-11-16 | 002564.SZ | 2016-11-16 | 10.43 | 10.43 | -0.033557 | 0.006711 | 0.029722 | -0.040268 | 0.006711 | -0.018217 | 0.054650 | -0.030681 | 0.018217 | 0.014382 | 0.063279 | -0.012464 |
| 2018-04-02 | 002712.SZ | 2018-04-02 | 12.77 | 12.59 | -0.054033 | -0.057948 | -0.033673 | -0.066562 | -0.071261 | -0.054033 | -0.043070 | -0.071261 | -0.067345 | -0.069695 | -0.067345 | -0.089272 |
| 2015-06-24 | 300126.SZ | 2015-06-24 | 45.05 | 45.05 | -0.100111 | -0.002220 | 0.002220 | -0.100111 | -0.190233 | -0.190233 | -0.190233 | -0.190233 | -0.271254 | -0.271254 | -0.271254 | -0.271254 |
| 2018-12-19 | 002845.SZ | 2018-12-19 | 15.77 | 15.77 | 0.020926 | 0.014585 | 0.093849 | 0.000000 | -0.020292 | -0.015853 | 0.006975 | -0.047559 | 0.016487 | -0.024731 | 0.056436 | -0.034242 |
| 2017-08-29 | 603042.SH | 2017-08-29 | 33.56 | 33.56 | 0.100417 | -0.021454 | 0.100417 | -0.027414 | 0.076281 | 0.031585 | 0.103397 | 0.008641 | 0.030691 | 0.056317 | 0.093564 | 0.027712 |
| 2015-10-27 | 600207.SH | 2015-10-27 | 9.56 | 9.56 | 0.100418 | 0.047071 | 0.100418 | 0.035565 | 0.210251 | 0.210251 | 0.210251 | 0.155858 | 0.331590 | 0.315900 | 0.331590 | 0.284519 |
| 2015-07-21 | 000638.SZ | 2015-07-21 | 15.32 | 15.32 | 0.099869 | 0.054178 | 0.099869 | 0.037859 | 0.174935 | 0.168407 | 0.204308 | 0.095953 | 0.127285 | 0.155352 | 0.207572 | 0.096606 |
| 2018-01-17 | 300698.SZ | 2018-01-17 | 29.44 | 29.44 | 0.029891 | 0.045177 | 0.096128 | -0.035326 | 0.133152 | -0.000679 | 0.133152 | -0.003057 | 0.019701 | 0.068954 | 0.082541 | 0.019701 |
| 2017-05-18 | 603728.SH | 2017-05-18 | 24.21 | 24.21 | -0.099959 | -0.036349 | -0.019827 | -0.099959 | -0.190004 | -0.168938 | -0.154895 | -0.190004 | -0.235440 | -0.211070 | -0.202396 | -0.252375 |
| 2018-11-29 | 600936.SH | 2018-11-29 | 5.10 | 4.77 | -0.154902 | -0.109804 | -0.107843 | -0.158824 | -0.117647 | -0.158824 | -0.090196 | -0.162745 | -0.133333 | -0.133333 | -0.129412 | -0.150980 |
| 2015-04-22 | 000410.SZ | 2015-04-22 | 27.34 | 27.34 | 0.033650 | 0.023409 | 0.100219 | 0.015728 | 0.087052 | 0.051939 | 0.118508 | 0.034748 | 0.039868 | 0.087052 | 0.096562 | 0.024872 |
| 2017-08-04 | 000616.SZ | 2017-08-04 | 4.87 | 4.87 | -0.016427 | 0.004107 | 0.016427 | -0.024641 | -0.053388 | -0.045175 | -0.024641 | -0.061602 | -0.063655 | -0.057495 | -0.049281 | -0.067762 |
| 2015-09-25 | 600876.SH | 2015-09-25 | 28.18 | 28.18 | 0.100071 | 0.069198 | 0.100071 | 0.013485 | 0.210078 | 0.112136 | 0.210078 | 0.112136 | 0.090490 | 0.270405 | 0.282825 | 0.089070 |
| 2014-12-24 | 002239.SZ | 2014-12-24 | 2.34 | 2.34 | 0.106838 | 0.008547 | 0.106838 | 0.000000 | 0.059829 | 0.158120 | 0.179487 | 0.055556 | -0.008547 | 0.034188 | 0.034188 | -0.025641 |
| 2015-10-19 | 600397.SH | 2015-10-19 | 7.40 | 7.40 | -0.005405 | 0.047297 | 0.075676 | -0.020270 | -0.105405 | -0.033784 | 0.032432 | -0.105405 | -0.035135 | -0.114865 | -0.016216 | -0.116216 |
| 2016-12-14 | 600722.SH | 2016-12-14 | 12.08 | 12.08 | 0.100166 | 0.084437 | 0.100166 | 0.039735 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2015-01-30 | 002740.SZ | 2015-01-30 | 13.87 | 13.87 | -0.090123 | 0.006489 | 0.059841 | -0.091565 | -0.123288 | -0.132660 | -0.106705 | -0.144196 | -0.090844 | -0.122567 | -0.073540 | -0.137707 |
| 2018-07-04 | 000802.SZ | 2018-07-04 | 13.33 | 13.33 | 0.089272 | 0.079520 | 0.099775 | 0.043511 | 0.198050 | 0.079520 | 0.198050 | 0.060765 | 0.140285 | 0.288822 | 0.288822 | 0.110278 |
| 2015-07-16 | 600695.SH | 2015-07-16 | 7.63 | 7.63 | 0.099607 | 0.022280 | 0.099607 | 0.006553 | 0.166448 | 0.133683 | 0.195282 | 0.099607 | 0.254260 | 0.166448 | 0.263434 | 0.108781 |
| 2015-01-14 | 000498.SZ | 2015-01-14 | 5.55 | 5.55 | 0.000000 | 0.070270 | 0.100901 | -0.036036 | -0.019820 | -0.050450 | -0.003604 | -0.059459 | -0.106306 | -0.055856 | -0.052252 | -0.118919 |
| 2018-09-20 | 300483.SZ | 2018-09-20 | 28.86 | 27.41 | -0.039848 | -0.084893 | -0.013514 | -0.084893 | -0.055787 | -0.060984 | -0.032225 | -0.071033 | -0.063756 | -0.060638 | -0.040194 | -0.068607 |
| 2018-05-04 | 002889.SZ | 2018-05-04 | 33.28 | 33.28 | 0.027644 | -0.003305 | 0.066707 | -0.014123 | 0.130409 | 0.010216 | 0.130409 | 0.010216 | 0.039062 | 0.081731 | 0.111478 | 0.030048 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2018-10-11 | 300216.SZ | 2018-10-11 | 5.40 | 5.40 | -0.068519 | 0.007407 | 0.007407 | -0.083333 | -0.092593 | -0.101852 | -0.012963 | -0.131481 | -0.001852 | -0.092593 | -0.001852 | -0.118519 |
| 2015-10-26 | 000019.SZ | 2015-10-26 | 8.18 | 7.97 | -0.057457 | -0.108802 | -0.035452 | -0.113692 | -0.015892 | -0.079462 | 0.036675 | -0.083130 | 0.084352 | -0.013447 | 0.084352 | -0.028117 |
| 2017-09-01 | 300348.SZ | 2017-09-01 | 23.38 | 23.38 | -0.012404 | 0.010265 | 0.049615 | -0.014115 | 0.020958 | -0.028657 | 0.066724 | -0.028657 | 0.023952 | 0.002994 | 0.049615 | -0.006843 |
| 2014-08-14 | 300208.SZ | 2014-08-14 | 4.08 | 4.08 | -0.009804 | 0.031863 | 0.053922 | -0.024510 | 0.088235 | -0.004902 | 0.088235 | -0.022059 | 0.110294 | 0.053922 | 0.156863 | 0.053922 |
| 2018-09-06 | 002848.SZ | 2018-09-06 | 10.01 | 10.01 | 0.099900 | 0.064935 | 0.099900 | 0.002997 | -0.009990 | 0.093906 | 0.144855 | -0.009990 | 0.088911 | -0.046953 | 0.088911 | -0.076923 |
| 2019-02-13 | 300566.SZ | 2019-02-13 | 14.67 | 14.67 | 0.050443 | 0.100204 | 0.100204 | 0.049761 | 0.085890 | -0.007498 | 0.143149 | -0.010225 | 0.098160 | 0.060668 | 0.102931 | 0.039536 |
| 2018-10-15 | 300131.SZ | 2018-10-15 | 4.73 | 4.70 | -0.080338 | -0.023256 | -0.010571 | -0.088795 | 0.012685 | -0.090909 | 0.012685 | -0.090909 | 0.010571 | 0.046512 | 0.114165 | 0.000000 |
| 2018-05-21 | 002828.SZ | 2018-05-21 | 17.92 | 17.92 | 0.100446 | 0.046875 | 0.100446 | 0.019531 | 0.210937 | 0.210937 | 0.210937 | 0.155692 | NaN | NaN | NaN | NaN |
| 2014-07-11 | 002629.SZ | 2014-07-11 | 7.43 | 7.23 | 0.034993 | -0.026918 | 0.069987 | -0.040377 | -0.018843 | 0.051144 | 0.061911 | -0.033647 | -0.033647 | -0.021534 | 0.017497 | -0.069987 |
| 2014-12-16 | 002392.SZ | 2014-12-16 | 6.94 | 6.94 | 0.007205 | 0.000000 | 0.034582 | -0.012968 | -0.004323 | -0.012968 | 0.004323 | -0.027378 | -0.043228 | -0.005764 | 0.014409 | -0.070605 |
| 2015-01-06 | 601918.SH | 2015-01-06 | 6.92 | 6.92 | -0.065029 | -0.052023 | -0.036127 | -0.073699 | 0.028902 | -0.062139 | 0.028902 | -0.072254 | -0.030347 | 0.004335 | 0.010116 | -0.037572 |
| 2018-12-25 | 000531.SZ | 2018-12-25 | 5.48 | 5.48 | -0.023723 | 0.005474 | 0.054745 | -0.025547 | -0.051095 | -0.032847 | 0.031022 | -0.063869 | -0.076642 | -0.054745 | -0.041971 | -0.078467 |
| 2017-01-13 | 600576.SH | 2017-01-13 | 22.24 | 22.24 | 0.038219 | -0.060252 | 0.097122 | -0.073291 | 0.076439 | 0.018885 | 0.124101 | -0.007194 | 0.041367 | 0.067896 | 0.094874 | 0.022032 |
| 2015-07-20 | 002176.SZ | 2015-07-20 | 9.21 | 9.21 | 0.099891 | 0.003257 | 0.099891 | 0.000000 | 0.158523 | 0.131379 | 0.191097 | 0.074919 | 0.183496 | 0.131379 | 0.205212 | 0.121607 |
| 2015-06-03 | 000558.SZ | 2015-06-03 | 23.96 | 23.96 | -0.020451 | 0.065526 | 0.081803 | -0.096828 | -0.109766 | -0.039649 | -0.038397 | -0.112688 | -0.173623 | -0.126461 | -0.126461 | -0.192821 |
| 2014-02-28 | 600680.SH | 2014-02-28 | 13.26 | 13.26 | 0.100302 | 0.045249 | 0.100302 | 0.045249 | 0.210407 | 0.118401 | 0.210407 | 0.105581 | 0.331825 | 0.272247 | 0.331825 | 0.272247 |
| 2015-07-15 | 000837.SZ | 2015-07-15 | 12.77 | 12.23 | -0.027408 | -0.103367 | 0.008614 | -0.136257 | 0.069695 | -0.027408 | 0.069695 | -0.035239 | 0.112764 | 0.084573 | 0.151135 | 0.049334 |
| 2018-08-01 | 002401.SZ | 2018-08-01 | 11.21 | 11.21 | 0.035682 | 0.001784 | 0.064228 | -0.014273 | -0.012489 | 0.008029 | 0.045495 | -0.013381 | -0.111508 | -0.045495 | -0.040143 | -0.111508 |
| 2018-08-29 | 603050.SH | 2018-08-29 | 13.64 | 13.64 | 0.079912 | 0.025660 | 0.099707 | -0.019062 | -0.027859 | 0.013930 | 0.021994 | -0.027859 | -0.062317 | -0.066716 | -0.050587 | -0.096041 |
| 2014-10-20 | 000018.SZ | 2014-10-20 | 3.92 | 3.92 | -0.045918 | -0.017857 | -0.012755 | -0.045918 | -0.084184 | -0.045918 | -0.028061 | -0.084184 | -0.109694 | -0.096939 | -0.079082 | -0.130102 |
| 2015-07-30 | 300368.SZ | 2015-07-30 | 9.81 | 9.81 | 0.100917 | -0.002039 | 0.100917 | -0.040775 | 0.211009 | 0.113150 | 0.211009 | 0.076453 | 0.332314 | 0.332314 | 0.332314 | 0.298675 |
| 2015-03-17 | 000752.SZ | 2015-03-17 | 18.10 | 17.65 | -0.020994 | -0.049171 | -0.018785 | -0.054696 | -0.028729 | -0.032044 | -0.022099 | -0.044751 | -0.025414 | -0.029834 | -0.018785 | -0.039779 |
| 2018-07-27 | 600186.SH | 2018-07-27 | 2.49 | 2.34 | -0.136546 | -0.092369 | -0.048193 | -0.152610 | -0.148594 | -0.128514 | -0.116466 | -0.156627 | -0.136546 | -0.144578 | -0.116466 | -0.156627 |
| 2018-03-16 | 002264.SZ | 2018-03-16 | 8.95 | 8.95 | 0.100559 | 0.049162 | 0.100559 | 0.039106 | 0.128492 | 0.098324 | 0.173184 | 0.061453 | 0.241341 | 0.092737 | 0.241341 | 0.092737 |
| 2018-09-10 | 600312.SH | 2018-09-10 | 5.46 | 5.46 | 0.051282 | 0.007326 | 0.089744 | -0.014652 | 0.075092 | 0.016484 | 0.120879 | 0.000000 | 0.117216 | 0.067766 | 0.117216 | 0.056777 |
| 2017-01-24 | 000605.SZ | 2017-01-24 | 16.59 | 16.59 | -0.050030 | 0.009042 | 0.037975 | -0.076552 | -0.054852 | -0.072333 | -0.018083 | -0.081374 | -0.068113 | -0.059675 | -0.048825 | -0.089210 |
| 2015-06-12 | 300459.SZ | 2015-06-12 | 8.75 | 8.75 | -0.100571 | -0.100571 | -0.100571 | -0.100571 | -0.192000 | -0.192000 | -0.192000 | -0.192000 | -0.238857 | -0.273143 | -0.195429 | -0.273143 |
| 2014-07-16 | 000868.SZ | 2014-07-16 | 5.92 | 5.92 | -0.096284 | -0.052365 | -0.042230 | -0.099662 | -0.141892 | -0.116554 | -0.092905 | -0.141892 | -0.130068 | -0.148649 | -0.128378 | -0.162162 |
| 2015-12-01 | 600165.SH | 2015-12-01 | 26.61 | 26.61 | -0.099962 | -0.092446 | -0.092446 | -0.099962 | -0.065765 | -0.189778 | -0.034949 | -0.189778 | -0.090192 | -0.087561 | -0.022924 | -0.094325 |
| 2018-12-13 | 300585.SZ | 2018-12-13 | 11.25 | 10.80 | -0.103111 | -0.100444 | -0.073778 | -0.112000 | -0.114667 | -0.111111 | -0.098667 | -0.134222 | -0.133333 | -0.130667 | -0.119111 | -0.148444 |
508 rows × 16 columns
In [32]:
dataclose = datadf[['1日收盘价','2日收盘价','3日收盘价']]
dataclose.describe()
Out[32]:
| 1日收盘价 | 2日收盘价 | 3日收盘价 | |
|---|---|---|---|
| count | 500.000000 | 493.000000 | 499.000000 |
| mean | -0.001599 | -0.004058 | -0.005465 |
| std | 0.070997 | 0.101881 | 0.123285 |
| min | -0.250679 | -0.310462 | -0.338995 |
| 25% | -0.054269 | -0.070144 | -0.085253 |
| 50% | -0.001160 | -0.014862 | -0.021176 |
| 75% | 0.050653 | 0.059829 | 0.060438 |
| max | 0.106838 | 0.215162 | 0.338628 |
In [33]:
dataopen = datadf[['1日开盘价','2日开盘价','3日开盘价']]
dataopen.describe()
Out[33]:
| 1日开盘价 | 2日开盘价 | 3日开盘价 | |
|---|---|---|---|
| count | 500.000000 | 493.000000 | 499.000000 |
| mean | -0.008970 | -0.010114 | -0.010622 |
| std | 0.051738 | 0.094574 | 0.117361 |
| min | -0.177438 | -0.275815 | -0.307745 |
| 25% | -0.040579 | -0.069217 | -0.085949 |
| 50% | -0.003126 | -0.020443 | -0.027778 |
| 75% | 0.021702 | 0.031447 | 0.041880 |
| max | 0.101010 | 0.211526 | 0.332314 |
In [34]:
datahigh = datadf[['1日最高价','2日最高价','3日最高价']]
datahigh.describe()
Out[34]:
| 1日最高价 | 2日最高价 | 3日最高价 | |
|---|---|---|---|
| count | 500.000000 | 493.000000 | 499.000000 |
| mean | 0.038950 | 0.034591 | 0.030486 |
| std | 0.056496 | 0.098113 | 0.124961 |
| min | -0.151495 | -0.247962 | -0.303668 |
| 25% | -0.002902 | -0.032316 | -0.054733 |
| 50% | 0.048349 | 0.018853 | 0.010163 |
| 75% | 0.099398 | 0.100202 | 0.096591 |
| max | 0.106838 | 0.215162 | 0.338710 |
In [35]:
datalow = datadf[['1日最低价','2日最低价','3日最低价']]
datalow.describe()
Out[35]:
| 1日最低价 | 2日最低价 | 3日最低价 | |
|---|---|---|---|
| count | 500.000000 | 493.000000 | 499.000000 |
| mean | -0.044232 | -0.042589 | -0.041151 |
| std | 0.054473 | 0.090011 | 0.110356 |
| min | -0.250679 | -0.323370 | -0.357337 |
| 25% | -0.087772 | -0.094737 | -0.112568 |
| 50% | -0.040260 | -0.049157 | -0.054870 |
| 75% | -0.008182 | 0.001862 | 0.014093 |
| max | 0.100851 | 0.211173 | 0.331852 |
In [36]:
dataclosedf = datadf[['stock','date','1日收盘价','2日收盘价','3日收盘价']]
dataclosedf = dataclosedf.sort_values(by='1日收盘价',ascending=False)
# dataclosedf = dataclosedf.dropna(axis=0,how='any')
dataclosedf
Out[36]:
| stock | date | 1日收盘价 | 2日收盘价 | 3日收盘价 | |
|---|---|---|---|---|---|
| 2014-12-24 | 002239.SZ | 2014-12-24 | 0.106838 | 0.059829 | -0.008547 |
| 2015-01-12 | 002738.SZ | 2015-01-12 | 0.102527 | 0.215162 | 0.338628 |
| 2015-07-13 | 002685.SZ | 2015-07-13 | 0.101179 | -0.009823 | -0.008841 |
| 2018-11-28 | 002288.SZ | 2018-11-28 | 0.100939 | 0.211268 | 0.333333 |
| 2015-07-30 | 300368.SZ | 2015-07-30 | 0.100917 | 0.211009 | 0.332314 |
| 2018-08-08 | 600470.SH | 2018-08-08 | 0.100858 | -0.006438 | -0.062232 |
| 2015-07-10 | 002446.SZ | 2015-07-10 | 0.100851 | 0.211526 | 0.089064 |
| 2014-07-28 | 600157.SH | 2014-07-28 | 0.100806 | 0.213710 | 0.266129 |
| 2018-11-01 | 000633.SZ | 2018-11-01 | 0.100791 | 0.128458 | 0.015810 |
| 2015-04-02 | 002006.SZ | 2015-04-02 | 0.100775 | 0.136090 | 0.090439 |
| 2014-08-22 | 300356.SZ | 2014-08-22 | 0.100775 | 0.075305 | 0.160576 |
| 2018-08-07 | 300392.SZ | 2018-08-07 | 0.100741 | 0.210370 | 0.331852 |
| 2017-03-15 | 002850.SZ | 2017-03-15 | 0.100696 | 0.054946 | 0.039382 |
| 2015-06-01 | 300149.SZ | 2015-06-01 | 0.100665 | 0.211530 | 0.101109 |
| 2015-10-09 | 002684.SZ | 2015-10-09 | 0.100629 | 0.139064 | 0.150943 |
| 2015-03-16 | 600207.SH | 2015-03-16 | 0.100559 | 0.150838 | 0.113128 |
| 2018-11-15 | 002708.SZ | 2018-11-15 | 0.100559 | 0.211173 | 0.331844 |
| 2018-03-16 | 002264.SZ | 2018-03-16 | 0.100559 | 0.128492 | 0.241341 |
| 2015-04-15 | 002044.SZ | 2015-04-15 | 0.100550 | 0.044776 | 0.147683 |
| 2014-09-12 | 300161.SZ | 2014-09-12 | 0.100540 | 0.089744 | 0.057355 |
| 2016-11-01 | 300044.SZ | 2016-11-01 | 0.100524 | 0.067016 | 0.086911 |
| 2018-05-21 | 002828.SZ | 2018-05-21 | 0.100446 | 0.210937 | NaN |
| 2015-08-26 | 000628.SZ | 2015-08-26 | 0.100437 | 0.210480 | 0.089083 |
| 2014-09-19 | 000852.SZ | 2014-09-19 | 0.100434 | 0.115933 | 0.115313 |
| 2015-10-27 | 600207.SH | 2015-10-27 | 0.100418 | 0.210251 | 0.331590 |
| 2017-08-29 | 603042.SH | 2017-08-29 | 0.100417 | 0.076281 | 0.030691 |
| 2015-02-04 | 000875.SZ | 2015-02-04 | 0.100379 | 0.090909 | 0.009470 |
| 2017-03-13 | 601212.SH | 2017-03-13 | 0.100375 | 0.211069 | 0.332083 |
| 2015-12-02 | 000668.SZ | 2015-12-02 | 0.100338 | 0.066892 | 0.071778 |
| 2014-02-28 | 600680.SH | 2014-02-28 | 0.100302 | 0.210407 | 0.331825 |
| ... | ... | ... | ... | ... | ... |
| 2018-11-27 | 000948.SZ | 2018-11-27 | -0.114783 | -0.160870 | -0.149565 |
| 2016-03-08 | 600882.SH | 2016-03-08 | -0.115694 | -0.170020 | -0.195171 |
| 2017-10-13 | 300062.SZ | 2017-10-13 | -0.116814 | -0.122124 | -0.132743 |
| 2015-02-03 | 000586.SZ | 2015-02-03 | -0.117314 | -0.149676 | -0.064725 |
| 2018-12-27 | 300265.SZ | 2018-12-27 | -0.118421 | -0.072368 | 0.020833 |
| 2017-03-30 | 002040.SZ | 2017-03-30 | -0.122374 | -0.159602 | -0.136380 |
| 2015-05-14 | 002625.SZ | 2015-05-14 | -0.123490 | -0.044954 | -0.101812 |
| 2015-10-23 | 300081.SZ | 2015-10-23 | -0.125994 | -0.099694 | -0.185321 |
| 2018-06-15 | 002423.SZ | 2018-06-15 | -0.133379 | -0.112859 | -0.178523 |
| 2015-07-27 | 600728.SH | 2015-07-27 | -0.135387 | -0.049427 | -0.114613 |
| 2018-07-27 | 600186.SH | 2018-07-27 | -0.136546 | -0.148594 | -0.136546 |
| 2018-07-03 | 002211.SZ | 2018-07-03 | -0.141892 | -0.201351 | -0.229730 |
| 2017-04-14 | 000616.SZ | 2017-04-14 | -0.145425 | -0.145425 | -0.156863 |
| 2015-04-27 | 300188.SZ | 2015-04-27 | -0.150334 | NaN | -0.149220 |
| 2018-12-06 | 300006.SZ | 2018-12-06 | -0.154472 | -0.203252 | -0.199187 |
| 2015-10-30 | 000019.SZ | 2015-10-30 | -0.154713 | -0.211066 | -0.163934 |
| 2018-11-29 | 600936.SH | 2018-11-29 | -0.154902 | -0.117647 | -0.133333 |
| 2018-04-11 | 300705.SZ | 2018-04-11 | -0.164298 | -0.224263 | -0.236742 |
| 2018-10-26 | 603999.SH | 2018-10-26 | -0.177391 | -0.160000 | -0.161739 |
| 2014-12-22 | 600169.SH | 2014-12-22 | -0.181612 | -0.205448 | -0.125993 |
| 2018-10-25 | 002451.SZ | 2018-10-25 | -0.183314 | -0.212691 | -0.203290 |
| 2019-01-28 | 300096.SZ | 2019-01-28 | -0.250679 | -0.310462 | -0.338995 |
| 2016-02-17 | 600234.SH | 2016-02-17 | NaN | NaN | NaN |
| 2017-04-11 | 002774.SZ | 2017-04-11 | NaN | NaN | -0.100352 |
| 2014-02-12 | 000971.SZ | 2014-02-12 | NaN | NaN | -0.027473 |
| 2015-03-24 | 300310.SZ | 2015-03-24 | NaN | -0.071142 | -0.044088 |
| 2018-01-10 | 600652.SH | 2018-01-10 | NaN | NaN | -0.083893 |
| 2016-03-03 | 000982.SZ | 2016-03-03 | NaN | NaN | NaN |
| 2017-04-12 | 000605.SZ | 2017-04-12 | NaN | NaN | -0.100194 |
| 2018-05-14 | 002930.SZ | 2018-05-14 | NaN | NaN | 0.099928 |
508 rows × 5 columns
In [37]:
dt = dataclosedf
dt = dt.sort_values(by='date')
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
plt.style.use('seaborn')
fig = plt.figure()
axes = fig.add_axes([0.1, 0.1, 1, 0.618]) #插入面板
x1_list=list(dt['1日收盘价'])
y=np.array(x1_list)
x=np.array(range(0,len(x1_list)))
axes.scatter(x,y,c='tomato')
axes.set_xlabel('time',fontsize=15)
axes.set_ylabel('down_up',fontsize=15)
axes.set_title(' one day distribution',fontsize=18)
# #设置X轴
axes.set_xticks([0,100,200,300,400,500])
axes.set_xticklabels([list(dt.index)[0],list(dt.index)[100],list(dt.index)[200],list(dt.index)[300],list(dt.index)[400],list(dt.index)[500]],fontsize=15)
plt.style.use('seaborn')
fig = plt.figure()
axes = fig.add_axes([0.1, 0.1, 1, 0.618]) #插入面板
x1_list=list(dt['2日收盘价'])
y=np.array(x1_list)
x=np.array(range(0,len(x1_list)))
axes.scatter(x,y,c='tomato')
axes.set_xlabel('time',fontsize=15)
axes.set_ylabel('down_up',fontsize=15)
axes.set_title(' two day distribution',fontsize=18)
# #设置X轴
axes.set_xticks([0,100,200,300,400,500])
axes.set_xticklabels([list(dt.index)[0],list(dt.index)[100],list(dt.index)[200],list(dt.index)[300],list(dt.index)[400],list(dt.index)[500]],fontsize=15)
plt.style.use('seaborn')
fig = plt.figure()
axes = fig.add_axes([0.1, 0.1, 1, 0.618]) #插入面板
x1_list=list(dt['3日收盘价'])
y=np.array(x1_list)
x=np.array(range(0,len(x1_list)))
axes.scatter(x,y,c='tomato')
axes.set_xlabel('time',fontsize=15)
axes.set_ylabel('down_up',fontsize=15)
axes.set_title(' three day distribution',fontsize=18)
# #设置X轴
axes.set_xticks([0,100,200,300,400,500])
axes.set_xticklabels([list(dt.index)[0],list(dt.index)[100],list(dt.index)[200],list(dt.index)[300],list(dt.index)[400],list(dt.index)[500]],fontsize=15)
Out[37]:
[<matplotlib.text.Text at 0x7efb3f5bbc50>, <matplotlib.text.Text at 0x7efb445ddba8>, <matplotlib.text.Text at 0x7efb448cacc0>, <matplotlib.text.Text at 0x7efb448ca3c8>, <matplotlib.text.Text at 0x7efb4450a080>, <matplotlib.text.Text at 0x7efb443d3ac8>]
In [38]:
labeldt = dataclosedf
labeldt['1日收盘价'] = labeldt['1日收盘价'].apply(lambda x:1 if x>0 else -1)
labeldt['2日收盘价'] = labeldt['2日收盘价'].apply(lambda x:1 if x>0 else -1)
labeldt['3日收盘价'] = labeldt['3日收盘价'].apply(lambda x:1 if x>0 else -1)
labeldt = labeldt.sort_values(by='date')
labeldt
Out[38]:
| stock | date | 1日收盘价 | 2日收盘价 | 3日收盘价 | |
|---|---|---|---|---|---|
| 2014-01-16 | 000767.SZ | 2014-01-16 | -1 | -1 | -1 |
| 2014-02-12 | 000971.SZ | 2014-02-12 | -1 | -1 | -1 |
| 2014-02-21 | 000554.SZ | 2014-02-21 | -1 | -1 | -1 |
| 2014-02-28 | 600680.SH | 2014-02-28 | 1 | 1 | 1 |
| 2014-03-10 | 300116.SZ | 2014-03-10 | -1 | -1 | -1 |
| 2014-03-13 | 000923.SZ | 2014-03-13 | -1 | 1 | 1 |
| 2014-03-27 | 000687.SZ | 2014-03-27 | -1 | -1 | -1 |
| 2014-04-03 | 000918.SZ | 2014-04-03 | -1 | -1 | -1 |
| 2014-04-10 | 300278.SZ | 2014-04-10 | 1 | 1 | 1 |
| 2014-04-30 | 601001.SH | 2014-04-30 | 1 | -1 | -1 |
| 2014-05-21 | 002660.SZ | 2014-05-21 | 1 | 1 | 1 |
| 2014-06-04 | 600601.SH | 2014-06-04 | -1 | -1 | -1 |
| 2014-06-05 | 600074.SH | 2014-06-05 | -1 | -1 | -1 |
| 2014-06-06 | 002535.SZ | 2014-06-06 | 1 | 1 | 1 |
| 2014-06-16 | 300198.SZ | 2014-06-16 | -1 | -1 | -1 |
| 2014-06-30 | 002093.SZ | 2014-06-30 | -1 | -1 | -1 |
| 2014-07-02 | 300288.SZ | 2014-07-02 | 1 | 1 | 1 |
| 2014-07-04 | 002070.SZ | 2014-07-04 | -1 | -1 | -1 |
| 2014-07-07 | 600568.SH | 2014-07-07 | -1 | 1 | 1 |
| 2014-07-11 | 002629.SZ | 2014-07-11 | 1 | -1 | -1 |
| 2014-07-16 | 000868.SZ | 2014-07-16 | -1 | -1 | -1 |
| 2014-07-25 | 600319.SH | 2014-07-25 | -1 | 1 | 1 |
| 2014-07-28 | 600157.SH | 2014-07-28 | 1 | 1 | 1 |
| 2014-08-07 | 600556.SH | 2014-08-07 | -1 | -1 | -1 |
| 2014-08-11 | 600222.SH | 2014-08-11 | -1 | -1 | -1 |
| 2014-08-14 | 300208.SZ | 2014-08-14 | -1 | 1 | 1 |
| 2014-08-18 | 002125.SZ | 2014-08-18 | 1 | -1 | 1 |
| 2014-08-21 | 000922.SZ | 2014-08-21 | 1 | 1 | 1 |
| 2014-08-22 | 300356.SZ | 2014-08-22 | 1 | 1 | 1 |
| 2014-09-02 | 600691.SH | 2014-09-02 | 1 | 1 | 1 |
| ... | ... | ... | ... | ... | ... |
| 2018-12-19 | 002845.SZ | 2018-12-19 | 1 | -1 | 1 |
| 2018-12-25 | 000531.SZ | 2018-12-25 | -1 | -1 | -1 |
| 2018-12-26 | 002927.SZ | 2018-12-26 | -1 | -1 | -1 |
| 2018-12-27 | 300265.SZ | 2018-12-27 | -1 | -1 | 1 |
| 2018-12-28 | 601619.SH | 2018-12-28 | -1 | -1 | -1 |
| 2019-01-04 | 300125.SZ | 2019-01-04 | 1 | 1 | 1 |
| 2019-01-07 | 600571.SH | 2019-01-07 | -1 | -1 | -1 |
| 2019-01-08 | 002130.SZ | 2019-01-08 | -1 | -1 | -1 |
| 2019-01-09 | 600452.SH | 2019-01-09 | 1 | 1 | 1 |
| 2019-01-10 | 300328.SZ | 2019-01-10 | -1 | -1 | -1 |
| 2019-01-11 | 300096.SZ | 2019-01-11 | -1 | -1 | -1 |
| 2019-01-14 | 000018.SZ | 2019-01-14 | -1 | -1 | -1 |
| 2019-01-16 | 002063.SZ | 2019-01-16 | 1 | 1 | 1 |
| 2019-01-18 | 600589.SH | 2019-01-18 | 1 | -1 | -1 |
| 2019-01-21 | 300693.SZ | 2019-01-21 | -1 | 1 | 1 |
| 2019-01-22 | 300503.SZ | 2019-01-22 | -1 | -1 | -1 |
| 2019-01-24 | 601811.SH | 2019-01-24 | 1 | -1 | -1 |
| 2019-01-25 | 600721.SH | 2019-01-25 | 1 | -1 | -1 |
| 2019-01-28 | 300096.SZ | 2019-01-28 | -1 | -1 | -1 |
| 2019-01-31 | 300250.SZ | 2019-01-31 | 1 | 1 | 1 |
| 2019-02-01 | 002011.SZ | 2019-02-01 | -1 | -1 | -1 |
| 2019-02-11 | 000802.SZ | 2019-02-11 | -1 | -1 | -1 |
| 2019-02-12 | 600318.SH | 2019-02-12 | 1 | -1 | -1 |
| 2019-02-13 | 300566.SZ | 2019-02-13 | 1 | 1 | 1 |
| 2019-02-14 | 300263.SZ | 2019-02-14 | -1 | 1 | 1 |
| 2019-02-18 | 601208.SH | 2019-02-18 | -1 | -1 | -1 |
| 2019-02-19 | 300466.SZ | 2019-02-19 | -1 | -1 | -1 |
| 2019-02-20 | 600095.SH | 2019-02-20 | -1 | -1 | -1 |
| 2019-02-21 | 002435.SZ | 2019-02-21 | -1 | 1 | -1 |
| 2019-02-22 | 600572.SH | 2019-02-22 | 1 | 1 | 1 |
508 rows × 5 columns
In [39]:
tradeday = list(get_trade_days(startdate, '20200202', count=None).strftime('%Y-%m-%d'))
timelist = []
trlist= []
lplist =[]
q_trlist = []
for d in tradeday:
time = 0
tr = 0
lp = 0
q_tr = 0
if d in list(labeldt['date']):
stock = labeldt['stock'][d]
day = tradeday[tradeday.index(d)+1]
stockdata = get_price(stock,None,day,'1m',['close','high','low','open','turnover_rate'],True,'pre',bar_count=241,is_panel=0)
pc = stockdata.iloc[0].close
highlimit = round(pc*1.1,2)
stockdata = stockdata.iloc[-240:]
for m in list(range(0,240)):
rc = stockdata.iloc[m].close
h = stockdata.iloc[(m+1)].high
if rc == highlimit:
q_tr +=stockdata.iloc[m].turnover_rate
if rc<highlimit:
time += 1
tr +=stockdata.iloc[m].turnover_rate
lp = min(stockdata.iloc[m].low/highlimit-1,lp)
if rc<highlimit and h == highlimit:
break
if time>0:
timelist.append(time)
trlist.append(round(tr,2))
lplist.append(round(lp*100,2))
q_trlist.append(round(q_tr,2))
timelist
Out[39]:
[2, 209, 1, 1, 1, 1, 25, 1, 2, 1, 2, 1, 1, 3, 1, 5, 228, 19, 11, 60, 1, 1, 1, 6, 95, 3, 1, 211, 1, 6, 2, 1, 6, 11, 102, 1, 4, 135, 69, 2, 1, 1, 236, 149, 52, 6, 3, 2, 2, 1, 6, 113, 1, 1, 1, 4, 38, 1, 6, 1, 14, 180, 1, 1, 1, 42, 1, 41, 2, 1, 7, 33, 31, 2, 43, 26, 1, 1, 126, 1, 5, 30, 132, 1, 5, 65, 4, 2, 5, 5, 20, 22, 2, 6, 166, 1, 2, 6, 1, 1, 4, 26, 3, 2, 4, 1, 3, 1, 4, 25, 2, 2, 83, 1, 5, 7, 3, 3, 1, 1, 8, 184, 19, 2, 1, 187, 1, 2, 4, 3, 56, 10, 2, 15, 2, 1, 1, 3, 1, 1, 1, 1, 3, 1, 1, 2, 1, 8, 51, 2, 2, 1, 2, 2, 1, 1, 3, 1, 60, 2, 1, 1, 3, 1, 2, 1, 2, 228, 5, 27, 179, 1, 1, 21, 8, 1, 1, 17, 1, 82, 1, 1, 1, 2, 84, 2, 1, 148, 2, 1, 1, 1, 1, 2, 1, 2, 3, 3, 1, 2, 8, 3, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 47, 2, 171, 1, 1, 1, 2, 4, 1, 1, 2, 2, 120, 1, 30, 3, 1, 1, 1, 1, 1, 1, 106, 183, 1, 1, 26, 1, 127, 18, 1, 2, 4, 1, 1, 2, 1, 1, 2, 1, 16, 201, 3, 88, 1, 1, 1, 53, 1, 1, 2, 1, 1, 1, 2, 1, 5, 1, 9, 8, 28, 1, 156, 1, 43, 5, 24, 16, 2, 1, 5, 1, 10, 1, 1, 33, 203, 1, 1, 7, 31, 55, 229, 2, 1, 1, 1, 118, 45, 2, 2, 2, 1, 77, 2, 120, 2, 45, 58, 2, 2, 22, 1, 1, 1, 4, 1, 6, 3, 1, 140, 6, 65, 40, 1, 43, 23, 1, 5, 10, 1, 1, 1, 6, 1, 181, 5, 1, 1, 1, 34, 3, 7, 1, 86, 1, 1, 1, 10, 1, 2, 1, 1, 10, 1, 10, 66, 1, 114, 1, 1, 1, 1, 12, 189, 1, 1, 7, 1, 11, 1, 114, 1, 12, 1, 2, 1, 17, 1, 1, 42, 1, 1, 2, 31, 1, 2, 1, 1, 6, 2, 4, 1, 13, 1, 1, 75, 15, 1, 21, 3, 1, 27, 13, 1, 6, 2, 10, 1, 1, 1, 1, 1, 2, 1, 46, 1, 2, 1, 1, 3, 125, 1, 1, 40, 1, 47, 1, 2, 1, 5, 1, 1, 2, 26, 1, 1, 22, 1, 30, 25, 3, 4, 2, 5, 4, 1, 108, 23, 1, 1, 1, 3, 2, 1, 1, 113, 1, 1, 1, 1, 2, 1, 1, 27, 1, 1, 11, 154, 1, 58, 1, 1, 42, 84, 5, 2, 1, 2, 3, 11, 6, 1, 1, 27, 9, 7, 6, 2, 1, 1, 1, 1, 175, 1, 15, 1, 2, 1, 2, 91, 1, 1, 1, 2, 5]
In [40]:
labeldt['time'] = timelist
labeldt['Change'] = trlist
labeldt['drop range'] = lplist
labeldt['limit Change'] = q_trlist
labeldt
Out[40]:
| stock | date | 1日收盘价 | 2日收盘价 | 3日收盘价 | time | Change | drop range | limit Change | |
|---|---|---|---|---|---|---|---|---|---|
| 2014-01-16 | 000767.SZ | 2014-01-16 | -1 | -1 | -1 | 2 | 0.23 | -2.27 | 0.00 |
| 2014-02-12 | 000971.SZ | 2014-02-12 | -1 | -1 | -1 | 209 | 10.74 | -7.14 | 0.00 |
| 2014-02-21 | 000554.SZ | 2014-02-21 | -1 | -1 | -1 | 1 | 3.99 | -1.19 | 9.58 |
| 2014-02-28 | 600680.SH | 2014-02-28 | 1 | 1 | 1 | 1 | 0.79 | -0.60 | 1.95 |
| 2014-03-10 | 300116.SZ | 2014-03-10 | -1 | -1 | -1 | 1 | 1.16 | -1.14 | 15.01 |
| 2014-03-13 | 000923.SZ | 2014-03-13 | -1 | 1 | 1 | 1 | 1.42 | -1.99 | 2.48 |
| 2014-03-27 | 000687.SZ | 2014-03-27 | -1 | -1 | -1 | 25 | 8.88 | -7.78 | 1.11 |
| 2014-04-03 | 000918.SZ | 2014-04-03 | -1 | -1 | -1 | 1 | 0.68 | -4.82 | 0.43 |
| 2014-04-10 | 300278.SZ | 2014-04-10 | 1 | 1 | 1 | 2 | 4.56 | -2.59 | 3.06 |
| 2014-04-30 | 601001.SH | 2014-04-30 | 1 | -1 | -1 | 1 | 0.17 | -0.81 | 0.64 |
| 2014-05-21 | 002660.SZ | 2014-05-21 | 1 | 1 | 1 | 2 | 2.72 | -1.87 | 0.88 |
| 2014-06-04 | 600601.SH | 2014-06-04 | -1 | -1 | -1 | 1 | 1.00 | -2.28 | 0.00 |
| 2014-06-05 | 600074.SH | 2014-06-05 | -1 | -1 | -1 | 1 | 1.00 | -1.17 | 2.63 |
| 2014-06-06 | 002535.SZ | 2014-06-06 | 1 | 1 | 1 | 3 | 2.15 | -2.86 | 1.05 |
| 2014-06-16 | 300198.SZ | 2014-06-16 | -1 | -1 | -1 | 1 | 1.01 | -3.84 | 1.28 |
| 2014-06-30 | 002093.SZ | 2014-06-30 | -1 | -1 | -1 | 5 | 3.30 | -3.93 | 0.00 |
| 2014-07-02 | 300288.SZ | 2014-07-02 | 1 | 1 | 1 | 228 | 20.97 | -4.61 | 4.82 |
| 2014-07-04 | 002070.SZ | 2014-07-04 | -1 | -1 | -1 | 19 | 7.00 | -8.27 | 0.60 |
| 2014-07-07 | 600568.SH | 2014-07-07 | -1 | 1 | 1 | 11 | 3.71 | -5.23 | 0.00 |
| 2014-07-11 | 002629.SZ | 2014-07-11 | 1 | -1 | -1 | 60 | 14.15 | -5.76 | 3.86 |
| 2014-07-16 | 000868.SZ | 2014-07-16 | -1 | -1 | -1 | 1 | 0.69 | -2.19 | 5.31 |
| 2014-07-25 | 600319.SH | 2014-07-25 | -1 | 1 | 1 | 1 | 1.57 | -1.83 | 3.54 |
| 2014-07-28 | 600157.SH | 2014-07-28 | 1 | 1 | 1 | 1 | 1.48 | -2.11 | 4.34 |
| 2014-08-07 | 600556.SH | 2014-08-07 | -1 | -1 | -1 | 6 | 4.43 | -5.58 | 0.00 |
| 2014-08-11 | 600222.SH | 2014-08-11 | -1 | -1 | -1 | 95 | 4.91 | -5.48 | 0.00 |
| 2014-08-14 | 300208.SZ | 2014-08-14 | -1 | 1 | 1 | 3 | 2.98 | -2.43 | 3.87 |
| 2014-08-18 | 002125.SZ | 2014-08-18 | 1 | -1 | 1 | 1 | 0.73 | -1.55 | 5.67 |
| 2014-08-21 | 000922.SZ | 2014-08-21 | 1 | 1 | 1 | 211 | 20.19 | -12.07 | 0.00 |
| 2014-08-22 | 300356.SZ | 2014-08-22 | 1 | 1 | 1 | 1 | 1.64 | -2.07 | 12.18 |
| 2014-09-02 | 600691.SH | 2014-09-02 | 1 | 1 | 1 | 6 | 1.92 | -5.09 | 0.00 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2018-12-19 | 002845.SZ | 2018-12-19 | 1 | -1 | 1 | 2 | 1.35 | -0.82 | 5.63 |
| 2018-12-25 | 000531.SZ | 2018-12-25 | -1 | -1 | -1 | 1 | 0.26 | -1.28 | 0.00 |
| 2018-12-26 | 002927.SZ | 2018-12-26 | -1 | -1 | -1 | 2 | 5.88 | -2.72 | 11.84 |
| 2018-12-27 | 300265.SZ | 2018-12-27 | -1 | -1 | 1 | 3 | 1.06 | -4.61 | 1.83 |
| 2018-12-28 | 601619.SH | 2018-12-28 | -1 | -1 | -1 | 11 | 1.55 | -2.54 | 3.17 |
| 2019-01-04 | 300125.SZ | 2019-01-04 | 1 | 1 | 1 | 6 | 1.15 | -2.67 | 0.20 |
| 2019-01-07 | 600571.SH | 2019-01-07 | -1 | -1 | -1 | 1 | 1.40 | -1.16 | 0.50 |
| 2019-01-08 | 002130.SZ | 2019-01-08 | -1 | -1 | -1 | 1 | 1.08 | -1.43 | 2.39 |
| 2019-01-09 | 600452.SH | 2019-01-09 | 1 | 1 | 1 | 27 | 0.57 | -3.31 | 0.00 |
| 2019-01-10 | 300328.SZ | 2019-01-10 | -1 | -1 | -1 | 9 | 2.36 | -4.15 | 2.79 |
| 2019-01-11 | 300096.SZ | 2019-01-11 | -1 | -1 | -1 | 7 | 0.71 | -1.74 | 1.60 |
| 2019-01-14 | 000018.SZ | 2019-01-14 | -1 | -1 | -1 | 6 | 3.54 | -6.14 | 0.64 |
| 2019-01-16 | 002063.SZ | 2019-01-16 | 1 | 1 | 1 | 2 | 1.33 | -3.46 | 0.57 |
| 2019-01-18 | 600589.SH | 2019-01-18 | 1 | -1 | -1 | 1 | 0.94 | -0.21 | 0.56 |
| 2019-01-21 | 300693.SZ | 2019-01-21 | -1 | 1 | 1 | 1 | 3.01 | -1.63 | 3.94 |
| 2019-01-22 | 300503.SZ | 2019-01-22 | -1 | -1 | -1 | 1 | 1.47 | -1.62 | 1.12 |
| 2019-01-24 | 601811.SH | 2019-01-24 | 1 | -1 | -1 | 1 | 1.78 | -2.43 | 7.48 |
| 2019-01-25 | 600721.SH | 2019-01-25 | 1 | -1 | -1 | 175 | 3.81 | -4.69 | 0.48 |
| 2019-01-28 | 300096.SZ | 2019-01-28 | -1 | -1 | -1 | 1 | 2.47 | -4.14 | 0.78 |
| 2019-01-31 | 300250.SZ | 2019-01-31 | 1 | 1 | 1 | 15 | 1.20 | -1.48 | 4.80 |
| 2019-02-01 | 002011.SZ | 2019-02-01 | -1 | -1 | -1 | 1 | 1.27 | -3.88 | 1.30 |
| 2019-02-11 | 000802.SZ | 2019-02-11 | -1 | -1 | -1 | 2 | 3.23 | -2.62 | 2.11 |
| 2019-02-12 | 600318.SH | 2019-02-12 | 1 | -1 | -1 | 1 | 0.47 | -0.70 | 0.47 |
| 2019-02-13 | 300566.SZ | 2019-02-13 | 1 | 1 | 1 | 2 | 2.42 | -2.32 | 0.00 |
| 2019-02-14 | 300263.SZ | 2019-02-14 | -1 | 1 | 1 | 91 | 10.91 | -9.18 | 0.70 |
| 2019-02-18 | 601208.SH | 2019-02-18 | -1 | -1 | -1 | 1 | 0.91 | -0.81 | 9.70 |
| 2019-02-19 | 300466.SZ | 2019-02-19 | -1 | -1 | -1 | 1 | 0.81 | -0.30 | 2.03 |
| 2019-02-20 | 600095.SH | 2019-02-20 | -1 | -1 | -1 | 1 | 1.69 | -0.43 | 0.91 |
| 2019-02-21 | 002435.SZ | 2019-02-21 | -1 | 1 | -1 | 2 | 2.42 | -4.24 | 1.20 |
| 2019-02-22 | 600572.SH | 2019-02-22 | 1 | 1 | 1 | 5 | 2.10 | -3.77 | 0.00 |
508 rows × 9 columns
In [41]:
updt = labeldt[labeldt['1日收盘价']==1]
downdt = labeldt[labeldt['1日收盘价']==-1]
label = ['time','Change','drop range','limit Change']
from mpl_toolkits.mplot3d import Axes3D
xsup1 = updt[label[0]]
xsup2 = updt[label[1]]
xsup3 = updt[label[2]]
xsdown1 = downdt[label[0]]
xsdown2 = downdt[label[1]]
xsdown3 = downdt[label[2]]
fig = plt.figure()
ax = Axes3D(fig)
ax.scatter(xsup1,xsup2,xsup3,c='tomato')
ax.scatter(xsdown1,xsdown2,xsdown3,c='g')
ax.set_xlabel(label[0],fontsize=12)
ax.set_ylabel(label[1],fontsize=12)
ax.set_zlabel(label[2],fontsize=12)
ax.set_title('one day Data space 3D ',fontsize=20)
plt.show()
for l in label:
fig = plt.figure()
axes = fig.add_axes([0.1, 0.1, 1, 0.618])
x1_list=list(updt[l])
y=np.array(x1_list)
x=np.array(range(0,len(x1_list)))
axes.scatter(x,y,c='tomato')
x1_list=list(downdt[l])
y=np.array(x1_list)
x=np.array(range(0,len(x1_list)))
axes.scatter(x,y,c='g')
axes.set_ylabel('value',fontsize=15)
axes.set_title(l,fontsize=20)
In [42]:
labeldt
label = ['time','Change','drop range','limit Change']
train = labeldt[:300]
test = labeldt[-200:]
X=train[label]
Y=train['1日收盘价']
X_test=test[label]
Y_test=test['1日收盘价']
from sklearn import svm
model = svm.SVC(C=1, kernel='rbf', gamma=0.5, decision_function_shape='ovo')
model.fit(X, Y)
print('训练时,预测成功率 {}'.format(round(np.mean(model.predict(X)==Y),2)))
print('测试时,预测成功率 {}'.format(round(np.mean(model.predict(X_test)==Y_test),2)))
训练时,预测成功率 0.88 测试时,预测成功率 0.51
In [43]:
dataclosedf = datadf#[['stock','date','1日收盘价','2日收盘价','3日收盘价']]
dataclosedf = dataclosedf.sort_values(by='1日收盘价',ascending=False)
tradeday = list(get_trade_days(startdate, '20200202', count=None).strftime('%Y-%m-%d'))
timelist = []
trlist= []
lplist =[]
q_trlist = []
for d in tradeday:
time = 0
tr = 0
lp = 0
q_tr = 0
if d in list(labeldt['date']):
stock = labeldt['stock'][d]
day = tradeday[tradeday.index(d)+1]
stockdata = get_price(stock,None,day,'1m',['close','high','low','open','turnover_rate'],True,'pre',bar_count=241,is_panel=0)
pc = stockdata.iloc[0].close
highlimit = round(pc*1.1,2)
stockdata = stockdata.iloc[-240:]
for m in list(range(0,240)):
rc = stockdata.iloc[m].close
h = stockdata.iloc[(m+1)].high
if rc == highlimit:
q_tr +=stockdata.iloc[m].turnover_rate
if rc<highlimit:
time += 1
tr +=stockdata.iloc[m].turnover_rate
lp = min(stockdata.iloc[m].low/highlimit-1,lp)
if rc<highlimit and h == highlimit:
break
if time>0:
timelist.append(time)
trlist.append(round(tr,2))
lplist.append(round(lp*100,2))
q_trlist.append(round(q_tr,2))
dataclosedf['time'] = timelist
dataclosedf['Change'] = trlist
dataclosedf['drop range'] = lplist
dataclosedf['limit Change'] = q_trlist
dataclosedf
Out[43]:
| stock | date | buyprice | 当日收盘价 | 1日收盘价 | 1日开盘价 | 1日最高价 | 1日最低价 | 2日收盘价 | 2日开盘价 | 2日最高价 | 2日最低价 | 3日收盘价 | 3日开盘价 | 3日最高价 | 3日最低价 | time | Change | drop range | limit Change | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2014-12-24 | 002239.SZ | 2014-12-24 | 2.34 | 2.34 | 0.106838 | 0.008547 | 0.106838 | 0.000000 | 0.059829 | 0.158120 | 0.179487 | 0.055556 | -0.008547 | 0.034188 | 0.034188 | -0.025641 | 2 | 0.23 | -2.27 | 0.00 |
| 2015-01-12 | 002738.SZ | 2015-01-12 | 13.85 | 13.85 | 0.102527 | 0.010108 | 0.102527 | -0.031047 | 0.215162 | 0.156679 | 0.215162 | 0.133574 | 0.338628 | 0.257762 | 0.338628 | 0.246209 | 209 | 10.74 | -7.14 | 0.00 |
| 2015-07-13 | 002685.SZ | 2015-07-13 | 10.18 | 10.18 | 0.101179 | 0.044204 | 0.101179 | 0.015717 | -0.009823 | 0.093320 | 0.098232 | -0.009823 | -0.008841 | -0.009823 | 0.040275 | -0.110020 | 1 | 3.99 | -1.19 | 9.58 |
| 2018-11-28 | 002288.SZ | 2018-11-28 | 4.26 | 4.26 | 0.100939 | -0.056338 | 0.100939 | -0.056338 | 0.211268 | 0.131455 | 0.211268 | 0.068075 | 0.333333 | 0.291080 | 0.333333 | 0.272300 | 1 | 0.79 | -0.60 | 1.95 |
| 2015-07-30 | 300368.SZ | 2015-07-30 | 9.81 | 9.81 | 0.100917 | -0.002039 | 0.100917 | -0.040775 | 0.211009 | 0.113150 | 0.211009 | 0.076453 | 0.332314 | 0.332314 | 0.332314 | 0.298675 | 1 | 1.16 | -1.14 | 15.01 |
| 2018-08-08 | 600470.SH | 2018-08-08 | 4.66 | 4.66 | 0.100858 | -0.034335 | 0.100858 | -0.036481 | -0.006438 | 0.072961 | 0.094421 | -0.008584 | -0.062232 | -0.051502 | -0.040773 | -0.081545 | 1 | 1.42 | -1.99 | 2.48 |
| 2015-07-10 | 002446.SZ | 2015-07-10 | 15.27 | 15.27 | 0.100851 | 0.100851 | 0.100851 | 0.100851 | 0.211526 | 0.211526 | 0.211526 | 0.138179 | 0.089064 | 0.151277 | 0.174198 | 0.089064 | 25 | 8.88 | -7.78 | 1.11 |
| 2014-07-28 | 600157.SH | 2014-07-28 | 2.48 | 2.48 | 0.100806 | -0.004032 | 0.100806 | -0.012097 | 0.213710 | 0.153226 | 0.213710 | 0.137097 | 0.266129 | 0.262097 | 0.338710 | 0.241935 | 1 | 0.68 | -4.82 | 0.43 |
| 2018-11-01 | 000633.SZ | 2018-11-01 | 5.06 | 5.06 | 0.100791 | 0.100791 | 0.100791 | 0.081028 | 0.128458 | 0.106719 | 0.183794 | 0.096838 | 0.015810 | 0.051383 | 0.065217 | 0.015810 | 2 | 4.56 | -2.59 | 3.06 |
| 2015-04-02 | 002006.SZ | 2015-04-02 | 11.61 | 11.61 | 0.100775 | 0.088717 | 0.100775 | 0.063738 | 0.136090 | 0.193798 | 0.193798 | 0.074074 | 0.090439 | 0.114556 | 0.114556 | 0.055986 | 1 | 0.17 | -0.81 | 0.64 |
| 2014-08-22 | 300356.SZ | 2014-08-22 | 9.03 | 9.03 | 0.100775 | 0.014396 | 0.100775 | 0.014396 | 0.075305 | 0.071982 | 0.087486 | 0.047619 | 0.160576 | 0.107420 | 0.182724 | 0.091916 | 2 | 2.72 | -1.87 | 0.88 |
| 2018-08-07 | 300392.SZ | 2018-08-07 | 6.75 | 6.75 | 0.100741 | 0.044444 | 0.100741 | 0.038519 | 0.210370 | 0.210370 | 0.210370 | 0.133333 | 0.331852 | 0.331852 | 0.331852 | 0.331852 | 1 | 1.00 | -2.28 | 0.00 |
| 2017-03-15 | 002850.SZ | 2017-03-15 | 84.81 | 84.81 | 0.100696 | 0.047046 | 0.100696 | 0.031364 | 0.054946 | 0.101167 | 0.141729 | 0.047046 | 0.039382 | 0.031246 | 0.053060 | 0.000000 | 1 | 1.00 | -1.17 | 2.63 |
| 2015-06-01 | 300149.SZ | 2015-06-01 | 22.55 | 22.55 | 0.100665 | 0.100665 | 0.100665 | 0.053659 | 0.211530 | 0.211086 | 0.211530 | 0.189357 | 0.101109 | 0.191131 | 0.191131 | 0.089579 | 3 | 2.15 | -2.86 | 1.05 |
| 2015-10-09 | 002684.SZ | 2015-10-09 | 14.31 | 14.31 | 0.100629 | 0.022362 | 0.100629 | 0.022362 | 0.139064 | 0.116003 | 0.184486 | 0.079665 | 0.150943 | 0.127184 | 0.206848 | 0.097834 | 1 | 1.01 | -3.84 | 1.28 |
| 2015-03-16 | 600207.SH | 2015-03-16 | 7.16 | 7.16 | 0.100559 | 0.005587 | 0.100559 | -0.005587 | 0.150838 | 0.166201 | 0.210894 | 0.117318 | 0.113128 | 0.157821 | 0.157821 | 0.079609 | 5 | 3.30 | -3.93 | 0.00 |
| 2018-11-15 | 002708.SZ | 2018-11-15 | 8.95 | 8.95 | 0.100559 | 0.032402 | 0.100559 | 0.011173 | 0.211173 | 0.211173 | 0.211173 | 0.211173 | 0.331844 | 0.229050 | 0.331844 | 0.186592 | 228 | 20.97 | -4.61 | 4.82 |
| 2018-03-16 | 002264.SZ | 2018-03-16 | 8.95 | 8.95 | 0.100559 | 0.049162 | 0.100559 | 0.039106 | 0.128492 | 0.098324 | 0.173184 | 0.061453 | 0.241341 | 0.092737 | 0.241341 | 0.092737 | 19 | 7.00 | -8.27 | 0.60 |
| 2015-04-15 | 002044.SZ | 2015-04-15 | 12.73 | 12.73 | 0.100550 | 0.100550 | 0.100550 | 0.076198 | 0.044776 | 0.211312 | 0.211312 | 0.044776 | 0.147683 | 0.080911 | 0.149254 | 0.062058 | 11 | 3.71 | -5.23 | 0.00 |
| 2014-09-12 | 300161.SZ | 2014-09-12 | 14.82 | 14.82 | 0.100540 | 0.008772 | 0.100540 | 0.000000 | 0.089744 | 0.102564 | 0.176113 | 0.089069 | 0.057355 | 0.075574 | 0.075574 | 0.033738 | 60 | 14.15 | -5.76 | 3.86 |
| 2016-11-01 | 300044.SZ | 2016-11-01 | 9.55 | 9.55 | 0.100524 | 0.021990 | 0.100524 | 0.008377 | 0.067016 | 0.158115 | 0.211518 | 0.056545 | 0.086911 | 0.071204 | 0.106806 | 0.053403 | 1 | 0.69 | -2.19 | 5.31 |
| 2018-05-21 | 002828.SZ | 2018-05-21 | 17.92 | 17.92 | 0.100446 | 0.046875 | 0.100446 | 0.019531 | 0.210937 | 0.210937 | 0.210937 | 0.155692 | NaN | NaN | NaN | NaN | 1 | 1.57 | -1.83 | 3.54 |
| 2015-08-26 | 000628.SZ | 2015-08-26 | 11.45 | 11.45 | 0.100437 | 0.082969 | 0.100437 | 0.061135 | 0.210480 | 0.210480 | 0.210480 | 0.210480 | 0.089083 | 0.222707 | 0.222707 | 0.089083 | 1 | 1.48 | -2.11 | 4.34 |
| 2014-09-19 | 000852.SZ | 2014-09-19 | 16.13 | 16.13 | 0.100434 | 0.003100 | 0.100434 | 0.000620 | 0.115933 | 0.075635 | 0.141971 | 0.058896 | 0.115313 | 0.112213 | 0.135152 | 0.088655 | 6 | 4.43 | -5.58 | 0.00 |
| 2015-10-27 | 600207.SH | 2015-10-27 | 9.56 | 9.56 | 0.100418 | 0.047071 | 0.100418 | 0.035565 | 0.210251 | 0.210251 | 0.210251 | 0.155858 | 0.331590 | 0.315900 | 0.331590 | 0.284519 | 95 | 4.91 | -5.48 | 0.00 |
| 2017-08-29 | 603042.SH | 2017-08-29 | 33.56 | 33.56 | 0.100417 | -0.021454 | 0.100417 | -0.027414 | 0.076281 | 0.031585 | 0.103397 | 0.008641 | 0.030691 | 0.056317 | 0.093564 | 0.027712 | 3 | 2.98 | -2.43 | 3.87 |
| 2015-02-04 | 000875.SZ | 2015-02-04 | 5.28 | 5.28 | 0.100379 | 0.041667 | 0.100379 | 0.009470 | 0.090909 | 0.115530 | 0.181818 | 0.062500 | 0.009470 | 0.045455 | 0.077652 | -0.017045 | 1 | 0.73 | -1.55 | 5.67 |
| 2017-03-13 | 601212.SH | 2017-03-13 | 10.66 | 10.66 | 0.100375 | -0.016886 | 0.100375 | -0.031895 | 0.211069 | 0.158537 | 0.211069 | 0.137899 | 0.332083 | 0.280488 | 0.332083 | 0.267355 | 211 | 20.19 | -12.07 | 0.00 |
| 2015-12-02 | 000668.SZ | 2015-12-02 | 26.61 | 26.61 | 0.100338 | 0.050733 | 0.100338 | 0.033070 | 0.066892 | 0.104848 | 0.162345 | 0.062383 | 0.071778 | 0.069523 | 0.086809 | 0.044344 | 1 | 1.64 | -2.07 | 12.18 |
| 2014-02-28 | 600680.SH | 2014-02-28 | 13.26 | 13.26 | 0.100302 | 0.045249 | 0.100302 | 0.045249 | 0.210407 | 0.118401 | 0.210407 | 0.105581 | 0.331825 | 0.272247 | 0.331825 | 0.272247 | 6 | 1.92 | -5.09 | 0.00 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2018-11-27 | 000948.SZ | 2018-11-27 | 11.50 | 10.80 | -0.114783 | -0.052174 | -0.040870 | -0.133043 | -0.160870 | -0.119130 | -0.113913 | -0.161739 | -0.149565 | -0.160870 | -0.140870 | -0.177391 | 2 | 1.35 | -0.82 | 5.63 |
| 2016-03-08 | 600882.SH | 2016-03-08 | 9.94 | 9.57 | -0.115694 | -0.132797 | -0.099598 | -0.133803 | -0.170020 | -0.112676 | -0.105634 | -0.173038 | -0.195171 | -0.193159 | -0.175050 | -0.205231 | 1 | 0.26 | -1.28 | 0.00 |
| 2017-10-13 | 300062.SZ | 2017-10-13 | 11.30 | 10.72 | -0.116814 | -0.070796 | -0.070796 | -0.132743 | -0.122124 | -0.123894 | -0.112389 | -0.127434 | -0.132743 | -0.123894 | -0.107080 | -0.136283 | 2 | 5.88 | -2.72 | 11.84 |
| 2015-02-03 | 000586.SZ | 2015-02-03 | 12.36 | 11.65 | -0.117314 | -0.134304 | -0.073625 | -0.140777 | -0.149676 | -0.114078 | -0.107605 | -0.149676 | -0.064725 | -0.150485 | -0.064725 | -0.156958 | 3 | 1.06 | -4.61 | 1.83 |
| 2018-12-27 | 300265.SZ | 2018-12-27 | 9.12 | 8.87 | -0.118421 | -0.088816 | -0.029605 | -0.125000 | -0.072368 | -0.130482 | -0.033991 | -0.150219 | 0.020833 | -0.088816 | 0.020833 | -0.092105 | 11 | 1.55 | -2.54 | 3.17 |
| 2017-03-30 | 002040.SZ | 2017-03-30 | 27.13 | 26.46 | -0.122374 | -0.077774 | -0.066716 | -0.122374 | -0.159602 | -0.172503 | -0.140066 | -0.196093 | -0.136380 | -0.163656 | -0.092886 | -0.164394 | 6 | 1.15 | -2.67 | 0.20 |
| 2015-05-14 | 002625.SZ | 2015-05-14 | 56.28 | 51.36 | -0.123490 | -0.123490 | -0.060768 | -0.171109 | -0.044954 | -0.139481 | -0.038913 | -0.163113 | -0.101812 | -0.038913 | -0.038735 | -0.124378 | 1 | 1.40 | -1.16 | 0.50 |
| 2015-10-23 | 300081.SZ | 2015-10-23 | 16.35 | 15.89 | -0.125994 | -0.078287 | -0.056881 | -0.125994 | -0.099694 | -0.173089 | -0.083792 | -0.176758 | -0.185321 | -0.124771 | -0.116820 | -0.188991 | 1 | 1.08 | -1.43 | 2.39 |
| 2018-06-15 | 002423.SZ | 2018-06-15 | 14.62 | 14.08 | -0.133379 | -0.110807 | -0.110807 | -0.133379 | -0.112859 | -0.220246 | -0.056772 | -0.220246 | -0.178523 | -0.158687 | -0.083447 | -0.189466 | 27 | 0.57 | -3.31 | 0.00 |
| 2015-07-27 | 600728.SH | 2015-07-27 | 13.96 | 13.35 | -0.135387 | -0.131805 | -0.016476 | -0.139685 | -0.049427 | -0.105301 | -0.048711 | -0.174069 | -0.114613 | -0.065186 | -0.037966 | -0.123209 | 9 | 2.36 | -4.15 | 2.79 |
| 2018-07-27 | 600186.SH | 2018-07-27 | 2.49 | 2.34 | -0.136546 | -0.092369 | -0.048193 | -0.152610 | -0.148594 | -0.128514 | -0.116466 | -0.156627 | -0.136546 | -0.144578 | -0.116466 | -0.156627 | 7 | 0.71 | -1.74 | 1.60 |
| 2018-07-03 | 002211.SZ | 2018-07-03 | 7.40 | 7.02 | -0.141892 | -0.091892 | -0.064865 | -0.145946 | -0.201351 | -0.156757 | -0.133784 | -0.221622 | -0.229730 | -0.216216 | -0.204054 | -0.258108 | 6 | 3.54 | -6.14 | 0.64 |
| 2017-04-14 | 000616.SZ | 2017-04-14 | 6.12 | 5.78 | -0.145425 | -0.101307 | -0.084967 | -0.150327 | -0.145425 | -0.153595 | -0.111111 | -0.156863 | -0.156863 | -0.156863 | -0.140523 | -0.181373 | 2 | 1.33 | -3.46 | 0.57 |
| 2015-04-27 | 300188.SZ | 2015-04-27 | 17.96 | 16.97 | -0.150334 | -0.084076 | -0.055122 | -0.150334 | NaN | NaN | NaN | NaN | -0.149220 | -0.116927 | -0.097439 | -0.149777 | 1 | 0.94 | -0.21 | 0.56 |
| 2018-12-06 | 300006.SZ | 2018-12-06 | 4.92 | 4.42 | -0.154472 | -0.097561 | -0.085366 | -0.168699 | -0.203252 | -0.172764 | -0.166667 | -0.203252 | -0.199187 | -0.201220 | -0.191057 | -0.203252 | 1 | 3.01 | -1.63 | 3.94 |
| 2015-10-30 | 000019.SZ | 2015-10-30 | 9.76 | 9.16 | -0.154713 | -0.154713 | -0.081967 | -0.154713 | -0.211066 | -0.172131 | -0.143443 | -0.222336 | -0.163934 | -0.205943 | -0.148566 | -0.214139 | 1 | 1.47 | -1.62 | 1.12 |
| 2018-11-29 | 600936.SH | 2018-11-29 | 5.10 | 4.77 | -0.154902 | -0.109804 | -0.107843 | -0.158824 | -0.117647 | -0.158824 | -0.090196 | -0.162745 | -0.133333 | -0.133333 | -0.129412 | -0.150980 | 1 | 1.78 | -2.43 | 7.48 |
| 2018-04-11 | 300705.SZ | 2018-04-11 | 28.85 | 26.79 | -0.164298 | -0.071404 | -0.049567 | -0.164298 | -0.224263 | -0.198614 | -0.164298 | -0.235355 | -0.236742 | -0.250260 | -0.228076 | -0.253380 | 175 | 3.81 | -4.69 | 0.48 |
| 2018-10-26 | 603999.SH | 2018-10-26 | 5.75 | 5.25 | -0.177391 | -0.142609 | -0.142609 | -0.177391 | -0.160000 | -0.184348 | -0.137391 | -0.205217 | -0.161739 | -0.172174 | -0.149565 | -0.180870 | 1 | 2.47 | -4.14 | 0.78 |
| 2014-12-22 | 600169.SH | 2014-12-22 | 8.81 | 7.95 | -0.181612 | -0.113507 | -0.091941 | -0.187287 | -0.205448 | -0.205448 | -0.185017 | -0.225880 | -0.125993 | -0.187287 | -0.125993 | -0.197503 | 15 | 1.20 | -1.48 | 4.80 |
| 2018-10-25 | 002451.SZ | 2018-10-25 | 8.51 | 7.62 | -0.183314 | -0.177438 | -0.121034 | -0.193890 | -0.212691 | -0.195065 | -0.182139 | -0.229142 | -0.203290 | -0.222092 | -0.171563 | -0.236193 | 1 | 1.27 | -3.88 | 1.30 |
| 2019-01-28 | 300096.SZ | 2019-01-28 | 14.72 | 12.26 | -0.250679 | -0.171196 | -0.151495 | -0.250679 | -0.310462 | -0.275815 | -0.247962 | -0.323370 | -0.338995 | -0.307745 | -0.303668 | -0.357337 | 2 | 3.23 | -2.62 | 2.11 |
| 2016-02-17 | 600234.SH | 2016-02-17 | 22.85 | 22.22 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1 | 0.47 | -0.70 | 0.47 |
| 2017-04-11 | 002774.SZ | 2017-04-11 | 22.72 | 22.72 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | -0.100352 | -0.078785 | -0.034771 | -0.100352 | 2 | 2.42 | -2.32 | 0.00 |
| 2014-02-12 | 000971.SZ | 2014-02-12 | 3.64 | 3.64 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | -0.027473 | -0.098901 | -0.002747 | -0.098901 | 91 | 10.91 | -9.18 | 0.70 |
| 2015-03-24 | 300310.SZ | 2015-03-24 | 9.98 | 9.98 | NaN | NaN | NaN | NaN | -0.071142 | -0.046092 | -0.039078 | -0.100200 | -0.044088 | -0.090180 | -0.002004 | -0.097194 | 1 | 0.91 | -0.81 | 9.70 |
| 2018-01-10 | 600652.SH | 2018-01-10 | 8.94 | 8.94 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | -0.083893 | -0.051454 | -0.020134 | -0.090604 | 1 | 0.81 | -0.30 | 2.03 |
| 2016-03-03 | 000982.SZ | 2016-03-03 | 8.22 | 8.22 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1 | 1.69 | -0.43 | 0.91 |
| 2017-04-12 | 000605.SZ | 2017-04-12 | 25.75 | 25.75 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | -0.100194 | -0.058252 | -0.016699 | -0.100194 | 2 | 2.42 | -4.24 | 1.20 |
| 2018-05-14 | 002930.SZ | 2018-05-14 | 41.53 | 41.53 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.099928 | 0.059475 | 0.099928 | 0.037804 | 5 | 2.10 | -3.77 | 0.00 |
508 rows × 20 columns
In [44]:
xdf = dataclosedf.dropna(axis=0)
In [45]:
xdf = xdf.sort_values(by='date')
xlabel = ['time','Change','drop range','limit Change']
ylabel = '3日收盘价'
xdf[ylabel] =xdf[ylabel].apply(lambda x:1 if x>0.02 else -1)
train = xdf[:300]
test = xdf[-200:]
X=train[xlabel]
Y=train[ylabel]
X_test=test[xlabel]
Y_test=test[ylabel]
from sklearn import svm
model = svm.SVC(C=5, kernel='rbf', gamma=0.5)
model.fit(X, Y)
print('训练时,预测成功率 {}'.format(round(np.mean(model.predict(X)==Y),2)))
print('测试时,预测成功率 {}'.format(round(np.mean(model.predict(X_test)==Y_test),2)))
len(list(test[test[ylabel]==1].index)),len(list(test[test[ylabel]==-1].index))
训练时,预测成功率 0.91 测试时,预测成功率 0.66
Out[45]:
(52, 148)
In [ ]: