本文主要是介绍同花顺Supermind量化交易 技术面分析专题（一）--相似k线在预测股价中的运用，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
如何应用“相似K线”来预测未来股价走势？:
我们已经清楚了“相似K线”，接下来让我们尝试将其用于预测未来股价。
第一步：选择浦发银行股票，锁定60个交易日的K线图，2018-10-08至2018-12-28。如下图：
第二步：以上证50指数成份股为样本池，对历史4年的所有股票的K线图进行相似度计算，其中K线图长度为60个交易日，时间周期为日级，计算间隔为20个交易日。最终找出2850张K线图。
第三步：找出历史中相似度最高的K线图，并观察其后续股价走势情况。我们找到了中国铁建，其在2015-03-31至2015-06-26的K线图与浦发银行在2018-10-08至2018-12-28的K线图相似度高达0.8685。
我们观察下图，发现中国铁建出现该段K线后，其后续走势出现反转上涨
第四步：根据中国铁建的前车之鉴，我们大胆判断出浦发银行在出现该走势后，也会反转上涨，我们画出K线图，进行验证。观察下图，可以发现，浦发银行也出现了反转上涨。
第五步：以上四步，即为“相似K线”预测股价的整个流程，我们将其应用于当下行情，选取浦发银行最近60个交易日的K线图
并在上证50指数成份股的历史行情中，找出与其相似度最高的K线图，股票保利地产在2014-01-03至2014-04-04发生与其相似度高达0.8785 观察历史发生该段K线图后的后续走势，我们发生股价出现下跌，根据“历史会重演”的技术理念，判断未来浦发银行的走势大概率出现下跌。
在“相似K线”的独特视角下，A股市场的价格走势呈现怎样的态势？:
第一步：我们获取上证指数的近60个交易日的K线走势图，如下：
第二步：我们计算上证指数的K线图与A股所有个股的近期K线图的相似度。
第三步：数据分析，并作出初步结论：
1.A股市场中，接近75%的股票与大盘（上证指数）走势较为相近，与大盘走势无关或者相反的个股股票数量较少占比不到25%
2.近期与大盘走势最为接近的是兴蓉环境，相似度高达0.94
3.近期与大盘走势最为相反的是泰永长征，相似度高达-0.731，其可能与次新炒作相关，脱离大盘走势。
用相似K线进行股价预测，波段操作
浦发银行(600000.SH) 2018-10-08至2018-12-28
In [6]:
stock = ['600000.SH']
trade = list(closedf.index.strftime('%Y%m%d'))
num = trade.index('20181228')
close = closedf[stock].iloc[num-59:num+1]
opens = opendf[stock].iloc[num-59:num+1]
high = highdf[stock].iloc[num-59:num+1]
low = lowdf[stock].iloc[num-59:num+1]
lowOut[6]:
| 600000.SH | |
|---|---|
| 2018-10-08 | 10.15 |
| 2018-10-09 | 10.07 |
| 2018-10-10 | 10.13 |
| 2018-10-11 | 9.71 |
| 2018-10-12 | 9.83 |
| 2018-10-15 | 9.97 |
| 2018-10-16 | 9.97 |
| 2018-10-17 | 10.14 |
| 2018-10-18 | 10.16 |
| 2018-10-19 | 10.07 |
| 2018-10-22 | 10.32 |
| 2018-10-23 | 10.45 |
| 2018-10-24 | 10.52 |
| 2018-10-25 | 10.54 |
| 2018-10-26 | 10.74 |
| 2018-10-29 | 10.71 |
| 2018-10-30 | 10.89 |
| 2018-10-31 | 10.87 |
| 2018-11-01 | 10.86 |
| 2018-11-02 | 10.94 |
| 2018-11-05 | 11.08 |
| 2018-11-06 | 11.00 |
| 2018-11-07 | 10.87 |
| 2018-11-08 | 10.90 |
| 2018-11-09 | 10.70 |
| 2018-11-12 | 10.75 |
| 2018-11-13 | 10.68 |
| 2018-11-14 | 10.68 |
| 2018-11-15 | 10.65 |
| 2018-11-16 | 10.71 |
| 2018-11-19 | 10.77 |
| 2018-11-20 | 10.65 |
| 2018-11-21 | 10.61 |
| 2018-11-22 | 10.57 |
| 2018-11-23 | 10.50 |
| 2018-11-26 | 10.43 |
| 2018-11-27 | 10.45 |
| 2018-11-28 | 10.50 |
| 2018-11-29 | 10.54 |
| 2018-11-30 | 10.62 |
| 2018-12-03 | 10.84 |
| 2018-12-04 | 10.97 |
| 2018-12-05 | 10.96 |
| 2018-12-06 | 10.82 |
| 2018-12-07 | 10.88 |
| 2018-12-10 | 10.77 |
| 2018-12-11 | 10.69 |
| 2018-12-12 | 10.70 |
| 2018-12-13 | 10.73 |
| 2018-12-14 | 10.59 |
| 2018-12-17 | 10.62 |
| 2018-12-18 | 10.54 |
| 2018-12-19 | 10.54 |
| 2018-12-20 | 10.11 |
| 2018-12-21 | 10.00 |
| 2018-12-24 | 9.86 |
| 2018-12-25 | 9.60 |
| 2018-12-26 | 9.64 |
| 2018-12-27 | 9.66 |
| 2018-12-28 | 9.71 |
In [7]:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.finance import candlestick2_ohlc
import datetime
for s in stock:open1=list(opens[s])high1=list(high[s])low1=list(low[s])close1=list(close[s])#画图fig,ax = plt.subplots(figsize = (10,6.18),facecolor='white')fig.subplots_adjust() ticks = ax.set_xticks([0,60])labels = ax.set_xticklabels([list(opens.index.strftime('%Y%m%d'))[0],list(opens.index.strftime('%Y%m%d'))[-1]], fontsize=10) plt.yticks() plt.title("{} K线走势图".format(s),fontsize = 15) plt.ylabel("股价",fontsize = 15) candlestick2_ohlc(ax,open1,high1,low1,close1,width=0.6,colorup='red',colordown='green')
寻找出上证50指数成份股内，与该段行情最接近的行情
In [8]:
dt = pd.DataFrame(columns=['stock','startdate','enddate','T'])
stock = '600000.SH'
trade = list(closedf.index.strftime('%Y%m%d'))
num = trade.index('20181228')
close = list(closedf[stock].iloc[num-59:num+1])
opens = list(opendf[stock].iloc[num-59:num+1])
high = list(highdf[stock].iloc[num-59:num+1])
low = list(lowdf[stock].iloc[num-59:num+1])stocklist = list(closedf.columns)
y=0
import datetime
for d in list(range(60,num-20,20)):print(d,num)close2 = closedf.iloc[d-59:d+1]opens2 = opendf.iloc[d-59:d+1]high2 = highdf.iloc[d-59:d+1]low2 = lowdf.iloc[d-59:d+1]for s in stocklist:corropen = round(np.corrcoef(opens,opens2[s])[0][1],3)corrhigh = round(np.corrcoef(high,high2[s])[0][1],3)corrlow = round(np.corrcoef(low,low2[s])[0][1],3)corrclose = round(np.corrcoef(close,close2[s])[0][1],3)#综合值T = (corropen+corrhigh+corrlow+corrclose)/4startdate = trade[d-59]enddate = trade[d+1]dt.loc[y] = [s,startdate,enddate,T]y+=1
dt = dt.fillna(0)
dt = dt.sort_values(by='T',ascending=False)
dt60 1219 80 1219 100 1219 120 1219 140 1219 160 1219 180 1219 200 1219 220 1219 240 1219 260 1219 280 1219 300 1219 320 1219 340 1219 360 1219 380 1219 400 1219 420 1219 440 1219 460 1219 480 1219 500 1219 520 1219 540 1219 560 1219 580 1219 600 1219 620 1219 640 1219 660 1219 680 1219 700 1219 720 1219 740 1219 760 1219 780 1219 800 1219 820 1219 840 1219 860 1219 880 1219 900 1219 920 1219 940 1219 960 1219 980 1219 1000 1219 1020 1219 1040 1219 1060 1219 1080 1219 1100 1219 1120 1219 1140 1219 1160 1219 1180 1219
Out[8]:
| stock | startdate | enddate | T | |
|---|---|---|---|---|
| 776 | 601186.SH | 20150331 | 20150626 | 0.86850 |
| 790 | 601766.SH | 20150331 | 20150626 | 0.82450 |
| 1520 | 600887.SH | 20160621 | 20160913 | 0.81300 |
| 2200 | 600000.SH | 20170811 | 20171110 | 0.79325 |
| 2246 | 601988.SH | 20170811 | 20171110 | 0.78500 |
| 1504 | 600029.SH | 20160621 | 20160913 | 0.78300 |
| 1145 | 601939.SH | 20151027 | 20160120 | 0.77400 |
| 1683 | 601360.SH | 20160913 | 20161215 | 0.76775 |
| 2668 | 600690.SH | 20180511 | 20180806 | 0.76775 |
| 784 | 601390.SH | 20150331 | 20150626 | 0.76100 |
| 2239 | 601688.SH | 20170811 | 20171110 | 0.74875 |
| 1105 | 600030.SH | 20151027 | 20160120 | 0.74600 |
| 2670 | 600887.SH | 20180511 | 20180806 | 0.74400 |
| 2616 | 600585.SH | 20180411 | 20180709 | 0.74325 |
| 1346 | 601988.SH | 20160224 | 20160520 | 0.74275 |
| 1557 | 600048.SH | 20160719 | 20161020 | 0.73550 |
| 1883 | 601360.SH | 20170113 | 20170418 | 0.73075 |
| 2227 | 601211.SH | 20170811 | 20171110 | 0.72825 |
| 2622 | 601088.SH | 20180411 | 20180709 | 0.72750 |
| 2636 | 601601.SH | 20180411 | 20180709 | 0.72750 |
| 751 | 600016.SH | 20150331 | 20150626 | 0.72675 |
| 1142 | 601818.SH | 20151027 | 20160120 | 0.72600 |
| 1342 | 601818.SH | 20160224 | 20160520 | 0.72150 |
| 1146 | 601988.SH | 20151027 | 20160120 | 0.71950 |
| 1338 | 601668.SH | 20160224 | 20160520 | 0.71900 |
| 2054 | 600029.SH | 20170517 | 20170811 | 0.71750 |
| 1117 | 600606.SH | 20151027 | 20160120 | 0.71700 |
| 1331 | 601328.SH | 20160224 | 20160520 | 0.71600 |
| 2337 | 601628.SH | 20171013 | 20180108 | 0.70975 |
| 788 | 601668.SH | 20150331 | 20150626 | 0.70750 |
| ... | ... | ... | ... | ... |
| 1632 | 601336.SH | 20160816 | 20161117 | -0.74300 |
| 1799 | 603993.SH | 20161117 | 20170217 | -0.74375 |
| 1437 | 601628.SH | 20160421 | 20160719 | -0.74750 |
| 1401 | 600016.SH | 20160421 | 20160719 | -0.74775 |
| 192 | 601818.SH | 20140404 | 20140703 | -0.75200 |
| 626 | 601186.SH | 20141226 | 20150331 | -0.75275 |
| 1443 | 601857.SH | 20160421 | 20160719 | -0.75475 |
| 1427 | 601211.SH | 20160421 | 20160719 | -0.75875 |
| 2329 | 601288.SH | 20171013 | 20180108 | -0.75925 |
| 634 | 601390.SH | 20141226 | 20150331 | -0.76600 |
| 638 | 601668.SH | 20141226 | 20150331 | -0.76800 |
| 1937 | 601628.SH | 20170217 | 20170517 | -0.76800 |
| 1201 | 600016.SH | 20151222 | 20160323 | -0.76850 |
| 1438 | 601668.SH | 20160421 | 20160719 | -0.76925 |
| 1932 | 601336.SH | 20170217 | 20170517 | -0.77175 |
| 605 | 600030.SH | 20141226 | 20150331 | -0.77400 |
| 639 | 601688.SH | 20141226 | 20150331 | -0.78350 |
| 152 | 600019.SH | 20140404 | 20140703 | -0.78500 |
| 215 | 600547.SH | 20140507 | 20140731 | -0.78625 |
| 239 | 601688.SH | 20140507 | 20140731 | -0.78850 |
| 2159 | 600104.SH | 20170714 | 20171013 | -0.79475 |
| 1403 | 600028.SH | 20160421 | 20160719 | -0.79875 |
| 602 | 600019.SH | 20141226 | 20150331 | -0.80200 |
| 1416 | 600585.SH | 20160421 | 20160719 | -0.80500 |
| 220 | 600887.SH | 20140507 | 20140731 | -0.80775 |
| 620 | 600887.SH | 20141226 | 20150331 | -0.80825 |
| 2730 | 601318.SH | 20180608 | 20180903 | -0.81375 |
| 2568 | 600690.SH | 20180312 | 20180608 | -0.81875 |
| 1422 | 601088.SH | 20160421 | 20160719 | -0.82450 |
| 1408 | 600050.SH | 20160421 | 20160719 | -0.83000 |
2850 rows × 4 columns
In [9]:
Tdt = dt.iloc[0]
stock = Tdt.stock
startdate = Tdt.startdate
enddate = Tdt.enddate import numpy as np
import matplotlib.pyplot as plt
from matplotlib.finance import candlestick2_ohlc
import datetime
s = stocktrade = list(closedf.index.strftime('%Y%m%d'))
num = trade.index(enddate)
close1 = closedf[s].iloc[num-59:num+1]
open1 = opendf[s].iloc[num-59:num+1]
high1 = highdf[s].iloc[num-59:num+1]
low1 = lowdf[s].iloc[num-59:num+1]#画图
fig,ax = plt.subplots(figsize = (10,6.18),facecolor='white')
fig.subplots_adjust() ticks = ax.set_xticks([0,60])
labels = ax.set_xticklabels([startdate,enddate], fontsize=10) plt.yticks()
plt.title("{} K线走势图".format(s),fontsize = 15)
plt.ylabel("股价",fontsize = 15)
candlestick2_ohlc(ax,open1,high1,low1,close1,width=0.6,colorup='red',colordown='green')Out[9]:
(<matplotlib.collections.LineCollection at 0x7f8b256197b8>,<matplotlib.collections.PolyCollection at 0x7f8b255f82e8>)
6.分析分析相似行情是否具备延续性
In [10]:
Tdt = dt.iloc[0]
Tdt.TOut[10]:
stock 601186.SH startdate 20150331 enddate 20150626 T 0.8685 Name: 776, dtype: object
相似度最高
In [11]:
stock = Tdt.stock
startdate =Tdt.startdate
enddate = Tdt.enddateimport numpy as np
import matplotlib.pyplot as plt
from matplotlib.finance import candlestick2_ohlc
import datetime
s = stocktrade = list(closedf.index.strftime('%Y%m%d'))
num = trade.index(enddate)
close1 = closedf[s].iloc[num-59:num+21]
open1 = opendf[s].iloc[num-59:num+21]
high1 = highdf[s].iloc[num-59:num+21]
low1 = lowdf[s].iloc[num-59:num+21]#画图
fig,ax = plt.subplots(figsize = (10,6.18),facecolor='white')
fig.subplots_adjust() #支撑线
plt.plot([60,60],[np.min(low1),np.max(high1)],'gold',linewidth=3)ticks = ax.set_xticks([0,60])
labels = ax.set_xticklabels([startdate,enddate], fontsize=10) plt.yticks()
plt.title("{} K线走势图".format(s),fontsize = 15)
plt.ylabel("股价",fontsize = 15)
candlestick2_ohlc(ax,open1,high1,low1,close1,width=0.6,colorup='red',colordown='green')Out[11]:
(<matplotlib.collections.LineCollection at 0x7f8b25036a58>,<matplotlib.collections.PolyCollection at 0x7f8b250364a8>)
In [12]:
stock = '600000.SH'
startdate = '20181008'
enddate = '20181228'import numpy as np
import matplotlib.pyplot as plt
from matplotlib.finance import candlestick2_ohlc
import datetime
s = stocktrade = list(closedf.index.strftime('%Y%m%d'))
num = trade.index(enddate)
close1 = closedf[s].iloc[num-59:num+21]
open1 = opendf[s].iloc[num-59:num+21]
high1 = highdf[s].iloc[num-59:num+21]
low1 = lowdf[s].iloc[num-59:num+21]#画图
fig,ax = plt.subplots(figsize = (10,6.18),facecolor='white')
fig.subplots_adjust() #支撑线
plt.plot([60,60],[np.min(low1),np.max(high1)],'gold',linewidth=3)ticks = ax.set_xticks([0,60])
labels = ax.set_xticklabels([startdate,enddate], fontsize=10) plt.yticks()
plt.title("{} K线走势图".format(s),fontsize = 15)
plt.ylabel("股价",fontsize = 15)
candlestick2_ohlc(ax,open1,high1,low1,close1,width=0.6,colorup='red',colordown='green')Out[12]:
(<matplotlib.collections.LineCollection at 0x7f8b275eb048>,<matplotlib.collections.PolyCollection at 0x7f8b275eb588>)
相似K线预测应用
In [13]:
data = get_price(stocklist,'20140101','20190211','1d',['open','high','low','close'],is_panel =1)
#收盘价
closedf = data['close'].fillna(0)
opendf = data['open'].fillna(0)
highdf = data['high'].fillna(0)
lowdf = data['low'].fillna(0)stock = ['600000.SH']
trade = list(closedf.index.strftime('%Y%m%d'))
num = trade.index('20190211')
close = closedf[stock].iloc[num-59:num+1]
opens = opendf[stock].iloc[num-59:num+1]
high = highdf[stock].iloc[num-59:num+1]
low = lowdf[stock].iloc[num-59:num+1]
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.finance import candlestick2_ohlc
import datetime
for s in stock:open1=list(opens[s])high1=list(high[s])low1=list(low[s])close1=list(close[s])#画图fig,ax = plt.subplots(figsize = (10,6.18),facecolor='white')fig.subplots_adjust() ticks = ax.set_xticks([0,60])labels = ax.set_xticklabels([list(opens.index.strftime('%Y%m%d'))[0],list(opens.index.strftime('%Y%m%d'))[-1]], fontsize=10) plt.yticks() plt.title("{} K线走势图".format(s),fontsize = 15) plt.ylabel("股价",fontsize = 15) candlestick2_ohlc(ax,open1,high1,low1,close1,width=0.6,colorup='red',colordown='green')
In [14]:
dt = pd.DataFrame(columns=['stock','startdate','enddate','T'])
stock = '600000.SH'
trade = list(closedf.index.strftime('%Y%m%d'))
num = trade.index('20190211')
close = list(closedf[stock].iloc[num-59:num+1])
opens = list(opendf[stock].iloc[num-59:num+1])
high = list(highdf[stock].iloc[num-59:num+1])
low = list(lowdf[stock].iloc[num-59:num+1])stocklist = list(closedf.columns)
y=0
import datetime
for d in list(range(60,num-20,20)):print(d,num)close2 = closedf.iloc[d-59:d+1]opens2 = opendf.iloc[d-59:d+1]high2 = highdf.iloc[d-59:d+1]low2 = lowdf.iloc[d-59:d+1]for s in stocklist:corropen = round(np.corrcoef(opens,opens2[s])[0][1],3)corrhigh = round(np.corrcoef(high,high2[s])[0][1],3)corrlow = round(np.corrcoef(low,low2[s])[0][1],3)corrclose = round(np.corrcoef(close,close2[s])[0][1],3)#综合值T = (corropen+corrhigh+corrlow+corrclose)/4startdate = trade[d-59]enddate = trade[d+1]dt.loc[y] = [s,startdate,enddate,T]y+=1
dt = dt.fillna(0)
dt = dt.sort_values(by='T',ascending=False)
dt60 1243 80 1243 100 1243 120 1243 140 1243 160 1243 180 1243 200 1243 220 1243 240 1243 260 1243 280 1243 300 1243 320 1243 340 1243 360 1243 380 1243 400 1243 420 1243 440 1243 460 1243 480 1243 500 1243 520 1243 540 1243 560 1243 580 1243 600 1243 620 1243 640 1243 660 1243 680 1243 700 1243 720 1243 740 1243 760 1243 780 1243 800 1243 820 1243 840 1243 860 1243 880 1243 900 1243 920 1243 940 1243 960 1243 980 1243 1000 1243 1020 1243 1040 1243 1060 1243 1080 1243 1100 1243 1120 1243 1140 1243 1160 1243 1180 1243 1200 1243 1220 1243
Out[14]:
| stock | startdate | enddate | T | |
|---|---|---|---|---|
| 7 | 600048.SH | 20140103 | 20140404 | 0.87700 |
| 2674 | 601166.SH | 20180511 | 20180806 | 0.85375 |
| 2650 | 600000.SH | 20180511 | 20180806 | 0.85150 |
| 2656 | 600036.SH | 20180511 | 20180806 | 0.84825 |
| 2687 | 601628.SH | 20180511 | 20180806 | 0.84050 |
| 2680 | 601318.SH | 20180511 | 20180806 | 0.83975 |
| 2692 | 601818.SH | 20180511 | 20180806 | 0.83725 |
| 2679 | 601288.SH | 20180511 | 20180806 | 0.83650 |
| 1218 | 600690.SH | 20151222 | 20160323 | 0.82425 |
| 2651 | 600016.SH | 20180511 | 20180806 | 0.81625 |
| 1966 | 600585.SH | 20170317 | 20170616 | 0.81250 |
| 1396 | 601988.SH | 20160323 | 20160621 | 0.79950 |
| 2909 | 600104.SH | 20181009 | 20190103 | 0.79650 |
| 1377 | 601211.SH | 20160323 | 20160621 | 0.79200 |
| 1975 | 601169.SH | 20170317 | 20170616 | 0.79100 |
| 1381 | 601328.SH | 20160323 | 20160621 | 0.79075 |
| 2695 | 601939.SH | 20180511 | 20180806 | 0.79025 |
| 269 | 600703.SH | 20140605 | 20140828 | 0.79000 |
| 2690 | 601766.SH | 20180511 | 20180806 | 0.78700 |
| 1853 | 600028.SH | 20170113 | 20170418 | 0.78625 |
| 2868 | 600690.SH | 20180903 | 20181204 | 0.78550 |
| 1394 | 601888.SH | 20160323 | 20160621 | 0.78475 |
| 38 | 601668.SH | 20140103 | 20140404 | 0.78050 |
| 2521 | 601006.SH | 20180205 | 20180511 | 0.77425 |
| 1392 | 601818.SH | 20160323 | 20160621 | 0.77375 |
| 2296 | 601988.SH | 20170908 | 20171208 | 0.77000 |
| 2281 | 601328.SH | 20170908 | 20171208 | 0.76975 |
| 1993 | 601857.SH | 20170317 | 20170616 | 0.76900 |
| 2518 | 600690.SH | 20180205 | 20180511 | 0.76850 |
| 2655 | 600030.SH | 20180511 | 20180806 | 0.76800 |
| ... | ... | ... | ... | ... |
| 721 | 601006.SH | 20150303 | 20150528 | -0.78000 |
| 2648 | 603259.SH | 20180411 | 20180709 | -0.78025 |
| 264 | 600519.SH | 20140605 | 20140828 | -0.78100 |
| 590 | 601766.SH | 20141128 | 20150303 | -0.78550 |
| 2428 | 601229.SH | 20171208 | 20180312 | -0.79150 |
| 1055 | 600030.SH | 20150922 | 20151222 | -0.79475 |
| 63 | 600340.SH | 20140207 | 20140507 | -0.79525 |
| 3 | 600028.SH | 20140103 | 20140404 | -0.79925 |
| 1689 | 601688.SH | 20160913 | 20161215 | -0.79925 |
| 740 | 601766.SH | 20150303 | 20150528 | -0.80600 |
| 1489 | 601688.SH | 20160520 | 20160816 | -0.81050 |
| 2189 | 601688.SH | 20170714 | 20171013 | -0.81650 |
| 2741 | 601800.SH | 20180608 | 20180903 | -0.81750 |
| 367 | 600606.SH | 20140731 | 20141031 | -0.81800 |
| 257 | 600048.SH | 20140605 | 20140828 | -0.81800 |
| 263 | 600340.SH | 20140605 | 20140828 | -0.82200 |
| 2004 | 600029.SH | 20170418 | 20170714 | -0.82300 |
| 2429 | 601288.SH | 20171208 | 20180312 | -0.82525 |
| 1913 | 600340.SH | 20170217 | 20170517 | -0.83075 |
| 1938 | 601668.SH | 20170217 | 20170517 | -0.83975 |
| 10 | 600196.SH | 20140103 | 20140404 | -0.84200 |
| 2186 | 601601.SH | 20170714 | 20171013 | -0.84600 |
| 2431 | 601328.SH | 20171208 | 20180312 | -0.84950 |
| 2445 | 601939.SH | 20171208 | 20180312 | -0.84975 |
| 2435 | 601398.SH | 20171208 | 20180312 | -0.85700 |
| 2843 | 601857.SH | 20180806 | 20181106 | -0.86225 |
| 2446 | 601988.SH | 20171208 | 20180312 | -0.86375 |
| 67 | 600606.SH | 20140207 | 20140507 | -0.86875 |
| 2442 | 601818.SH | 20171208 | 20180312 | -0.87700 |
| 1497 | 601989.SH | 20160520 | 20160816 | -0.87900 |
2950 rows × 4 columns
In [15]:
Tdt = dt.iloc[0]stock = Tdt.stock
startdate = Tdt.startdate
enddate = Tdt.enddate import numpy as np
import matplotlib.pyplot as plt
from matplotlib.finance import candlestick2_ohlc
import datetime
s = stocktrade = list(closedf.index.strftime('%Y%m%d'))
num = trade.index(enddate)
close1 = closedf[s].iloc[num-59:num+21]
open1 = opendf[s].iloc[num-59:num+21]
high1 = highdf[s].iloc[num-59:num+21]
low1 = lowdf[s].iloc[num-59:num+21]#画图
fig,ax = plt.subplots(figsize = (10,6.18),facecolor='white')
fig.subplots_adjust() #支撑线
plt.plot([60,60],[np.min(low1),np.max(high1)],'gold',linewidth=3)ticks = ax.set_xticks([0,60])
labels = ax.set_xticklabels([startdate,enddate], fontsize=10) plt.yticks()
plt.title("{} K线走势图".format(s),fontsize = 15)
plt.ylabel("股价",fontsize = 15)
candlestick2_ohlc(ax,open1,high1,low1,close1,width=0.6,colorup='red',colordown='green')Out[15]:
(<matplotlib.collections.LineCollection at 0x7f8b26a53b00>,<matplotlib.collections.PolyCollection at 0x7f8b26a50ef0>)
快速挑选出与大盘走势同步、相反的个股
In [16]:
indexcode = '000001.SH'
date = '20190211'
stocklist = list(get_all_securities('stock',date).index)+[indexcode]
stocklistOut[16]:
['300056.SZ','300674.SZ','300108.SZ','300357.SZ','600841.SH','600495.SH','300249.SZ','002852.SZ','000058.SZ','002568.SZ','300199.SZ','300490.SZ','000893.SZ','603859.SH','600565.SH','603063.SH','600117.SH','300036.SZ','300746.SZ','002906.SZ','600052.SH','002187.SZ','300613.SZ','000596.SZ','002351.SZ','000736.SZ','000810.SZ','002767.SZ','601318.SH','300183.SZ','002755.SZ','002502.SZ','603722.SH','002298.SZ','300711.SZ','300678.SZ','600362.SH','002176.SZ','600887.SH','300430.SZ','300712.SZ','300291.SZ','300486.SZ','600876.SH','601988.SH','601100.SH','603688.SH','600078.SH','603958.SH','000811.SZ','000537.SZ','300260.SZ','002434.SZ','000541.SZ','002673.SZ','600804.SH','603268.SH','300567.SZ','600038.SH','300382.SZ','600370.SH','000150.SZ','600158.SH','000888.SZ','603458.SH','300723.SZ','300670.SZ','600100.SH','600678.SH','603566.SH','600237.SH','002166.SZ','002073.SZ','300071.SZ','601666.SH','600422.SH','002642.SZ','000027.SZ','002537.SZ','000631.SZ','002457.SZ','603359.SH','600189.SH','600592.SH','600290.SH','603988.SH','002857.SZ','300105.SZ','603321.SH','601398.SH','600755.SH','002480.SZ','600919.SH','300690.SZ','603133.SH','300328.SZ','300759.SZ','603979.SH','002055.SZ','002741.SZ','300391.SZ','600211.SH','002278.SZ','000639.SZ','300273.SZ','300543.SZ','000823.SZ','300521.SZ','000762.SZ','002493.SZ','000597.SZ','600765.SH','000826.SZ','300680.SZ','300341.SZ','002628.SZ','603611.SH','000573.SZ','000766.SZ','001979.SZ','600620.SH','300575.SZ','000726.SZ','002920.SZ','002081.SZ','002797.SZ','002383.SZ','300471.SZ','600011.SH','600151.SH','300628.SZ','600007.SH','300488.SZ','300539.SZ','000831.SZ','000539.SZ','300538.SZ','600814.SH','000889.SZ','002839.SZ','600926.SH','600805.SH','002155.SZ','002319.SZ','600562.SH','000652.SZ','000603.SZ','600313.SH','002131.SZ','000812.SZ','300332.SZ','600775.SH','603886.SH','000963.SZ','300535.SZ','000656.SZ','002261.SZ','603029.SH','300369.SZ','600326.SH','600811.SH','300451.SZ','600488.SH','600392.SH','603966.SH','600597.SH','002651.SZ','002733.SZ','600509.SH','600477.SH','300485.SZ','600978.SH','603712.SH','601179.SH','600055.SH','002903.SZ','000968.SZ','002526.SZ','000088.SZ','603260.SH','000690.SZ','600185.SH','002392.SZ','600398.SH','300444.SZ','002865.SZ','002897.SZ','002057.SZ','603678.SH','002510.SZ','600409.SH','002484.SZ','300065.SZ','002943.SZ','002276.SZ','000510.SZ','300431.SZ','300097.SZ','600022.SH','600171.SH','000014.SZ','300124.SZ','300651.SZ','600983.SH','002915.SZ','600010.SH','000683.SZ','600621.SH','002476.SZ','300368.SZ','300525.SZ','300394.SZ','300453.SZ','000955.SZ','300003.SZ','600777.SH','002144.SZ','002584.SZ','002592.SZ','600220.SH','600221.SH','603716.SH','300658.SZ','600622.SH','002832.SZ','603603.SH','002381.SZ','300195.SZ','000806.SZ','002451.SZ','300224.SZ','600088.SH','002418.SZ','300347.SZ','603733.SH','600153.SH','603679.SH','601800.SH','601108.SH','002743.SZ','600982.SH','300320.SZ','000922.SZ','603648.SH','300045.SZ','600187.SH','600594.SH','300634.SZ','600673.SH','000096.SZ','600306.SH','600651.SH','002577.SZ','601010.SH','600103.SH','002204.SZ','603311.SH','600846.SH','000760.SZ','002033.SZ','603585.SH','600396.SH','002438.SZ','002564.SZ','000543.SZ','601311.SH','603689.SH','000782.SZ','300725.SZ','002727.SZ','603685.SH','600094.SH','600609.SH','300365.SZ','002691.SZ','600785.SH','002037.SZ','601966.SH','300591.SZ','000902.SZ','600888.SH','603036.SH','603916.SH','002668.SZ','600405.SH','603839.SH','300167.SZ','002294.SZ','600209.SH','000589.SZ','603986.SH','000925.SZ','002390.SZ','600826.SH','000068.SZ','000680.SZ','300139.SZ','603383.SH','603887.SH','000973.SZ','002137.SZ','300505.SZ','002788.SZ','300026.SZ','000055.SZ','002787.SZ','000004.SZ','600360.SH','600480.SH','000558.SZ','300545.SZ','300305.SZ','600059.SH','603520.SH','002397.SZ','300307.SZ','300620.SZ','002314.SZ','000403.SZ','600086.SH','000070.SZ','002009.SZ','002302.SZ','300705.SZ','600583.SH','300572.SZ','000868.SZ','603701.SH','002032.SZ','603056.SH','600499.SH','600936.SH','603612.SH','000605.SZ','300103.SZ','300663.SZ','000807.SZ','300581.SZ','300450.SZ','603059.SH','600764.SH','600395.SH','601952.SH','600272.SH','002761.SZ','000969.SZ','002201.SZ','300091.SZ','002689.SZ','300079.SZ','300161.SZ','600280.SH','300321.SZ','600579.SH','300423.SZ','603917.SH','000533.SZ','300151.SZ','002747.SZ','603037.SH','002181.SZ','002461.SZ','002305.SZ','002112.SZ','600338.SH','600839.SH','300514.SZ','600248.SH','002309.SZ','603897.SH','300568.SZ','603829.SH','601633.SH','600714.SH','300311.SZ','600724.SH','300532.SZ','600284.SH','601838.SH','000521.SZ','300114.SZ','603589.SH','002329.SZ','002024.SZ','600779.SH','603718.SH','300573.SZ','002663.SZ','000069.SZ','002817.SZ','300698.SZ','603668.SH','000937.SZ','603908.SH','002156.SZ','000531.SZ','601668.SH','600292.SH','002725.SZ','603500.SH','000528.SZ','002026.SZ','002648.SZ','601038.SH','000629.SZ','000777.SZ','000692.SZ','000566.SZ','603111.SH','002653.SZ','000548.SZ','300659.SZ','600063.SH','600066.SH','300482.SZ','002866.SZ','300176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获取数据
In [17]:
data = get_price(stocklist,None,date,'1d',['open','high','low','close'],bar_count = 60,is_panel =1)
dataOut[17]:
<class 'pandas.core.panel.Panel'> Dimensions: 4 (items) x 60 (major_axis) x 3586 (minor_axis) Items axis: close to open Major_axis axis: 2018-11-09 00:00:00 to 2019-02-11 00:00:00 Minor_axis axis: 000001.SH to 603999.SH
In [18]:
closedf = data['close'].fillna(0)
opendf = data['open'].fillna(0)
highdf = data['high'].fillna(0)
lowdf = data['low'].fillna(0)
lowdfOut[18]:
| 000001.SH | 000001.SZ | 000002.SZ | 000004.SZ | 000005.SZ | 000006.SZ | 000007.SZ | 000008.SZ | 000009.SZ | 000010.SZ | ... | 603987.SH | 603988.SH | 603989.SH | 603990.SH | 603991.SH | 603993.SH | 603996.SH | 603997.SH | 603998.SH | 603999.SH | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2018-11-09 | 2598.1610 | 10.40 | 23.53 | 16.00 | 2.86 | 5.03 | 6.71 | 4.43 | 4.27 | 4.26 | ... | 6.04 | 9.92 | 22.10 | 30.01 | 17.70 | 4.10 | 8.40 | 7.95 | 5.18 | 4.75 |
| 2018-11-12 | 2590.2110 | 10.38 | 23.40 | 16.15 | 2.79 | 4.97 | 7.06 | 3.99 | 4.26 | 4.20 | ... | 6.01 | 10.99 | 22.30 | 30.15 | 17.90 | 4.04 | 8.44 | 7.91 | 5.20 | 4.74 |
| 2018-11-13 | 2597.3480 | 10.38 | 23.44 | 16.50 | 2.93 | 5.05 | 8.03 | 4.12 | 4.37 | 4.23 | ... | 6.09 | 10.86 | 22.33 | 30.50 | 18.18 | 4.06 | 8.40 | 7.93 | 5.33 | 4.82 |
| 2018-11-14 | 2627.9565 | 10.42 | 23.96 | 17.00 | 3.04 | 5.24 | 7.92 | 4.19 | 4.50 | 4.33 | ... | 6.25 | 10.64 | 22.51 | 32.03 | 19.14 | 4.13 | 8.72 | 8.16 | 5.58 | 5.08 |
| 2018-11-15 | 2631.8875 | 10.42 | 23.96 | 16.80 | 3.01 | 5.24 | 7.70 | 4.15 | 4.59 | 4.34 | ... | 6.28 | 10.60 | 22.51 | 31.99 | 19.51 | 4.10 | 8.87 | 8.18 | 5.53 | 5.11 |
| 2018-11-16 | 2657.0341 | 10.49 | 24.02 | 16.93 | 3.04 | 5.45 | 7.75 | 4.15 | 4.87 | 4.40 | ... | 6.43 | 10.62 | 22.80 | 32.70 | 20.30 | 4.15 | 8.96 | 8.26 | 5.64 | 5.21 |
| 2018-11-19 | 2674.1781 | 10.57 | 24.68 | 17.30 | 3.11 | 5.51 | 8.15 | 4.13 | 5.33 | 4.42 | ... | 6.45 | 10.82 | 22.55 | 32.26 | 20.07 | 4.11 | 8.85 | 8.26 | 5.68 | 5.25 |
| 2018-11-20 | 2643.3588 | 10.52 | 25.23 | 16.80 | 3.03 | 5.47 | 7.88 | 4.00 | 5.02 | 4.30 | ... | 6.27 | 10.70 | 22.08 | 31.85 | 19.34 | 4.10 | 8.74 | 8.00 | 5.52 | 5.11 |
| 2018-11-21 | 2617.7759 | 10.48 | 25.35 | 16.65 | 2.97 | 5.39 | 8.23 | 3.96 | 5.04 | 4.25 | ... | 6.19 | 10.60 | 21.00 | 31.50 | 19.35 | 3.97 | 8.53 | 7.88 | 5.42 | 4.98 |
| 2018-11-22 | 2634.4827 | 10.48 | 25.28 | 17.04 | 3.03 | 5.45 | 9.05 | 4.12 | 5.00 | 4.32 | ... | 6.43 | 10.75 | 21.18 | 31.66 | 19.45 | 4.05 | 8.72 | 8.19 | 5.57 | 5.11 |
| 2018-11-23 | 2577.3511 | 10.31 | 24.68 | 16.49 | 2.88 | 5.25 | 9.37 | 4.01 | 4.74 | 4.31 | ... | 6.06 | 10.35 | 21.01 | 31.21 | 18.51 | 3.85 | 7.96 | 8.36 | 5.21 | 5.04 |
| 2018-11-26 | 2568.0352 | 10.30 | 24.74 | 16.35 | 2.87 | 5.25 | 8.64 | 3.95 | 4.75 | 4.34 | ... | 6.07 | 10.11 | 20.68 | 31.50 | 18.35 | 3.79 | 7.95 | 8.20 | 5.15 | 5.12 |
| 2018-11-27 | 2566.1663 | 10.16 | 24.78 | 16.46 | 2.89 | 5.26 | 8.14 | 3.95 | 4.84 | 4.47 | ... | 6.14 | 10.01 | 20.70 | 31.76 | 18.53 | 3.80 | 8.02 | 8.21 | 5.13 | 5.17 |
| 2018-11-28 | 2561.5618 | 10.16 | 24.99 | 16.67 | 2.89 | 5.17 | 7.93 | 3.89 | 4.74 | 4.42 | ... | 6.03 | 10.12 | 20.50 | 31.88 | 18.13 | 3.77 | 7.88 | 8.25 | 5.08 | 5.02 |
| 2018-11-29 | 2567.4434 | 10.19 | 25.00 | 16.32 | 2.99 | 5.32 | 7.29 | 3.75 | 4.69 | 4.45 | ... | 6.40 | 9.80 | 19.70 | 31.70 | 17.73 | 3.86 | 7.96 | 8.00 | 5.11 | 5.10 |
| 2018-11-30 | 2555.3223 | 10.17 | 25.03 | 15.96 | 2.90 | 5.22 | 6.87 | 3.66 | 4.58 | 4.02 | ... | 6.60 | 8.87 | 19.80 | 31.70 | 17.80 | 3.82 | 7.86 | 7.96 | 5.06 | 4.80 |
| 2018-12-03 | 2629.1843 | 10.47 | 24.81 | 16.39 | 3.03 | 5.44 | 7.26 | 3.85 | 4.85 | 4.06 | ... | 6.70 | 10.80 | 20.72 | 32.66 | 18.68 | 4.05 | 8.25 | 8.16 | 5.25 | 5.05 |
| 2018-12-04 | 2644.8964 | 10.53 | 24.86 | 16.52 | 3.03 | 5.49 | 7.39 | 3.96 | 4.89 | 4.06 | ... | 6.77 | 10.71 | 21.05 | 32.76 | 18.91 | 4.03 | 8.34 | 8.11 | 5.27 | 5.05 |
| 2018-12-05 | 2625.9132 | 10.40 | 24.76 | 16.55 | 2.98 | 5.47 | 7.84 | 3.91 | 4.80 | 4.01 | ... | 6.61 | 10.70 | 21.38 | 31.35 | 18.80 | 3.97 | 8.19 | 8.15 | 5.47 | 5.35 |
| 2018-12-06 | 2603.1323 | 10.23 | 24.90 | 16.64 | 2.95 | 5.55 | 7.76 | 3.91 | 4.75 | 4.02 | ... | 6.66 | 10.38 | 21.46 | 32.42 | 18.50 | 4.05 | 8.18 | 8.10 | 5.30 | 5.80 |
| 2018-12-07 | 2599.2775 | 10.27 | 25.12 | 16.30 | 3.00 | 5.62 | 7.91 | 3.90 | 4.76 | 3.86 | ... | 6.55 | 10.20 | 21.25 | 32.60 | 18.63 | 4.03 | 8.21 | 8.00 | 5.20 | 5.46 |
| 2018-12-10 | 2576.2424 | 10.13 | 24.90 | 15.80 | 2.91 | 5.53 | 7.20 | 3.80 | 4.65 | 3.82 | ... | 6.35 | 10.19 | 21.10 | 32.08 | 18.03 | 3.97 | 8.16 | 7.66 | 5.05 | 5.12 |
| 2018-12-11 | 2583.1959 | 10.17 | 24.91 | 16.07 | 2.93 | 5.62 | 7.38 | 3.83 | 4.66 | 3.70 | ... | 6.43 | 10.23 | 21.20 | 32.12 | 18.20 | 3.97 | 8.22 | 7.65 | 5.07 | 5.19 |
| 2018-12-12 | 2595.0750 | 10.20 | 26.14 | 16.52 | 2.99 | 5.76 | 8.25 | 3.85 | 4.71 | 3.61 | ... | 6.40 | 10.28 | 21.26 | 31.74 | 18.31 | 4.03 | 8.24 | 7.79 | 5.10 | 5.25 |
| 2018-12-13 | 2599.4586 | 10.20 | 26.40 | 16.41 | 2.99 | 5.72 | 8.34 | 3.86 | 4.73 | 3.52 | ... | 6.71 | 10.42 | 21.28 | 31.53 | 18.35 | 4.07 | 8.16 | 8.15 | 5.09 | 5.21 |
| 2018-12-14 | 2593.7407 | 10.16 | 26.31 | 16.46 | 2.92 | 5.66 | 8.10 | 3.96 | 4.58 | 3.38 | ... | 6.46 | 9.96 | 20.88 | 31.20 | 18.03 | 4.07 | 8.01 | 8.10 | 5.03 | 5.10 |
| 2018-12-17 | 2573.8674 | 10.10 | 25.94 | 17.25 | 2.91 | 5.72 | 8.05 | 3.90 | 4.52 | 3.38 | ... | 6.31 | 9.90 | 20.30 | 31.51 | 17.75 | 4.04 | 7.94 | 7.92 | 4.88 | 5.03 |
| 2018-12-18 | 2563.5731 | 10.10 | 25.34 | 16.83 | 2.87 | 5.38 | 8.22 | 3.82 | 4.56 | 3.45 | ... | 6.33 | 9.83 | 20.21 | 32.19 | 17.66 | 4.09 | 7.68 | 7.82 | 4.89 | 5.08 |
| 2018-12-19 | 2547.0783 | 9.90 | 25.15 | 16.32 | 2.86 | 5.42 | 8.79 | 3.81 | 4.55 | 3.47 | ... | 6.33 | 9.98 | 20.21 | 32.10 | 17.61 | 4.08 | 7.71 | 7.80 | 4.88 | 5.08 |
| 2018-12-20 | 2516.0193 | 9.63 | 24.91 | 16.30 | 2.84 | 5.40 | 8.61 | 3.79 | 4.53 | 3.45 | ... | 6.36 | 9.95 | 20.19 | 31.90 | 17.51 | 4.01 | 7.75 | 7.71 | 4.84 | 5.06 |
| 2018-12-21 | 2498.6937 | 9.33 | 23.70 | 16.17 | 2.83 | 5.33 | 8.23 | 3.80 | 4.48 | 3.46 | ... | 6.54 | 9.89 | 20.32 | 31.28 | 17.55 | 3.91 | 7.62 | 7.70 | 4.86 | 5.11 |
| 2018-12-24 | 2500.4430 | 9.31 | 23.33 | 16.08 | 2.82 | 5.27 | 8.18 | 3.80 | 4.50 | 3.45 | ... | 6.61 | 9.80 | 20.20 | 31.80 | 17.61 | 3.92 | 7.65 | 7.72 | 4.91 | 5.09 |
| 2018-12-25 | 2462.8448 | 9.21 | 23.38 | 15.92 | 2.72 | 5.03 | 8.01 | 3.85 | 4.23 | 3.32 | ... | 6.35 | 9.80 | 20.07 | 31.60 | 17.45 | 3.76 | 7.33 | 7.38 | 4.79 | 4.80 |
| 2018-12-26 | 2492.0758 | 9.27 | 23.58 | 16.16 | 2.73 | 5.15 | 8.46 | 3.96 | 4.37 | 3.40 | ... | 6.43 | 10.06 | 20.45 | 32.26 | 18.36 | 3.84 | 7.56 | 7.46 | 4.85 | 4.90 |
| 2018-12-27 | 2483.0864 | 9.28 | 23.44 | 16.03 | 2.66 | 5.16 | 8.10 | 3.84 | 4.30 | 3.26 | ... | 6.42 | 9.95 | 20.10 | 32.79 | 18.37 | 3.75 | 7.43 | 7.50 | 4.65 | 4.81 |
| 2018-12-28 | 2478.3250 | 9.31 | 23.67 | 15.73 | 2.65 | 5.15 | 7.90 | 3.85 | 4.30 | 3.20 | ... | 6.34 | 9.98 | 20.00 | 32.60 | 18.18 | 3.74 | 7.36 | 7.40 | 4.67 | 4.81 |
| 2019-01-02 | 2456.4233 | 9.16 | 23.67 | 16.01 | 2.66 | 5.10 | 7.99 | 3.81 | 4.26 | 3.25 | ... | 6.42 | 9.82 | 19.25 | 32.15 | 18.06 | 3.65 | 7.48 | 7.65 | 4.52 | 4.75 |
| 2019-01-03 | 2455.9256 | 9.15 | 23.71 | 16.00 | 2.65 | 5.11 | 7.58 | 3.83 | 4.23 | 3.26 | ... | 6.40 | 9.66 | 18.75 | 31.80 | 17.82 | 3.67 | 7.47 | 7.70 | 4.53 | 4.72 |
| 2019-01-04 | 2440.9066 | 9.22 | 23.85 | 16.01 | 2.66 | 5.06 | 7.50 | 3.80 | 4.18 | 3.22 | ... | 6.36 | 9.62 | 18.59 | 31.79 | 17.42 | 3.67 | 7.36 | 7.72 | 4.50 | 4.66 |
| 2019-01-07 | 2515.5083 | 9.63 | 24.99 | 16.46 | 2.76 | 5.28 | 7.93 | 3.92 | 4.41 | 3.32 | ... | 6.58 | 9.95 | 19.39 | 31.80 | 18.36 | 3.83 | 7.73 | 7.75 | 4.74 | 4.92 |
| 2019-01-08 | 2520.1648 | 9.62 | 24.63 | 16.80 | 2.82 | 5.31 | 7.82 | 4.03 | 4.44 | 3.35 | ... | 6.55 | 10.03 | 19.20 | 31.81 | 18.56 | 3.85 | 7.74 | 7.78 | 4.86 | 5.01 |
| 2019-01-09 | 2536.1570 | 9.70 | 25.11 | 16.72 | 2.84 | 5.34 | 7.80 | 4.06 | 4.46 | 3.36 | ... | 6.80 | 10.18 | 19.53 | 31.87 | 18.85 | 3.83 | 8.01 | 7.85 | 4.92 | 5.00 |
| 2019-01-10 | 2531.6638 | 9.86 | 25.03 | 16.66 | 2.84 | 5.29 | 7.77 | 4.00 | 4.41 | 3.36 | ... | 6.72 | 10.22 | 19.16 | 31.81 | 18.85 | 3.80 | 7.89 | 7.90 | 4.98 | 4.96 |
| 2019-01-11 | 2533.3586 | 10.05 | 25.01 | 16.49 | 2.91 | 5.30 | 7.63 | 3.98 | 4.40 | 3.34 | ... | 6.68 | 10.19 | 19.23 | 31.55 | 18.79 | 3.80 | 7.91 | 7.70 | 5.00 | 4.95 |
| 2019-01-14 | 2533.0101 | 10.07 | 24.92 | 16.49 | 2.97 | 5.28 | 6.97 | 4.00 | 4.44 | 3.52 | ... | 6.35 | 9.94 | 18.76 | 31.50 | 19.06 | 3.80 | 7.87 | 7.64 | 5.00 | 4.93 |
| 2019-01-15 | 2532.4333 | 10.09 | 24.32 | 16.40 | 3.04 | 5.23 | 7.05 | 3.98 | 4.44 | 3.47 | ... | 6.35 | 10.01 | 18.90 | 31.52 | 18.88 | 3.80 | 7.91 | 7.77 | 5.00 | 4.94 |
| 2019-01-16 | 2563.0061 | 10.23 | 24.83 | 16.55 | 2.96 | 5.31 | 7.07 | 4.00 | 4.43 | 3.51 | ... | 6.40 | 10.24 | 19.38 | 31.56 | 19.14 | 3.81 | 8.08 | 7.77 | 5.03 | 4.97 |
| 2019-01-17 | 2557.7114 | 10.17 | 24.94 | 16.40 | 2.95 | 5.25 | 6.77 | 3.95 | 4.37 | 3.42 | ... | 6.28 | 9.88 | 18.80 | 31.50 | 18.69 | 3.78 | 7.99 | 7.62 | 4.96 | 4.95 |
| 2019-01-18 | 2565.9043 | 10.28 | 24.99 | 16.44 | 2.95 | 5.24 | 6.56 | 3.80 | 4.33 | 3.27 | ... | 6.34 | 9.75 | 18.86 | 31.50 | 18.58 | 3.81 | 7.95 | 7.61 | 4.98 | 4.86 |
| 2019-01-21 | 2599.0575 | 10.32 | 25.40 | 16.42 | 2.96 | 5.33 | 6.72 | 3.89 | 4.40 | 3.31 | ... | 6.42 | 9.81 | 18.91 | 31.05 | 18.28 | 3.91 | 7.99 | 7.66 | 5.02 | 5.07 |
| 2019-01-22 | 2573.0617 | 10.26 | 25.23 | 16.28 | 2.92 | 5.25 | 6.70 | 3.89 | 4.25 | 3.34 | ... | 6.42 | 9.68 | 18.82 | 31.36 | 18.89 | 3.85 | 7.87 | 7.65 | 4.97 | 5.03 |
| 2019-01-23 | 2572.4054 | 10.29 | 25.20 | 16.20 | 2.97 | 5.23 | 6.62 | 3.86 | 4.26 | 3.31 | ... | 6.37 | 9.66 | 18.71 | 31.53 | 18.75 | 3.83 | 7.86 | 7.61 | 4.93 | 4.97 |
| 2019-01-24 | 2569.7004 | 10.37 | 24.93 | 16.22 | 2.90 | 5.20 | 6.45 | 3.84 | 4.28 | 3.28 | ... | 6.39 | 9.68 | 17.95 | 31.85 | 18.59 | 3.83 | 8.07 | 7.62 | 4.94 | 4.97 |
| 2019-01-25 | 2595.6294 | 10.55 | 25.49 | 16.27 | 2.91 | 5.25 | 6.20 | 3.85 | 4.28 | 3.25 | ... | 6.36 | 9.59 | 17.73 | 32.03 | 18.43 | 3.83 | 8.02 | 7.63 | 4.93 | 4.93 |
| 2019-01-28 | 2591.1000 | 10.88 | 25.86 | 16.01 | 2.90 | 5.20 | 5.61 | 3.85 | 4.22 | 3.24 | ... | 6.12 | 9.32 | 17.75 | 32.22 | 18.23 | 3.83 | 7.93 | 7.54 | 4.86 | 4.93 |
| 2019-01-29 | 2559.9820 | 10.77 | 25.87 | 15.00 | 2.78 | 5.10 | 5.05 | 3.80 | 4.08 | 3.02 | ... | 5.92 | 9.07 | 17.63 | 32.27 | 17.53 | 3.62 | 7.23 | 7.41 | 4.71 | 5.10 |
| 2019-01-30 | 2575.4091 | 10.86 | 26.63 | 15.03 | 2.80 | 5.12 | 4.61 | 3.82 | 4.12 | 2.82 | ... | 6.05 | 9.22 | 17.51 | 32.39 | 17.27 | 3.73 | 7.28 | 7.33 | 4.65 | 4.92 |
| 2019-01-31 | 2571.5789 | 10.94 | 27.00 | 14.05 | 2.82 | 5.11 | 4.75 | 3.50 | 4.02 | 2.85 | ... | 5.85 | 8.41 | 17.50 | 32.60 | 16.57 | 3.78 | 7.25 | 7.01 | 4.61 | 4.72 |
| 2019-02-01 | 2590.5543 | 10.96 | 26.98 | 14.70 | 2.85 | 5.17 | 4.88 | 3.65 | 4.08 | 2.91 | ... | 5.96 | 8.75 | 17.56 | 33.13 | 16.80 | 3.83 | 7.46 | 7.12 | 4.66 | 4.73 |
| 2019-02-11 | 2613.1742 | 10.97 | 26.89 | 14.85 | 2.97 | 5.25 | 5.12 | 3.61 | 4.26 | 3.04 | ... | 6.19 | 9.04 | 18.12 | 34.91 | 17.45 | 3.89 | 7.92 | 7.45 | 4.77 | 4.86 |
60 rows × 3586 columns
绘制大盘走势图
In [19]:
stock = indexcode
startdate = '20181109'
enddate = dateimport numpy as np
import matplotlib.pyplot as plt
from matplotlib.finance import candlestick2_ohlc
import datetime
s = stocktrade = list(closedf.index.strftime('%Y%m%d'))
num = trade.index(enddate)
close1 = closedf[s].iloc[num-59:num+21]
open1 = opendf[s].iloc[num-59:num+21]
high1 = highdf[s].iloc[num-59:num+21]
low1 = lowdf[s].iloc[num-59:num+21]#画图
fig,ax = plt.subplots(figsize = (10,6.18),facecolor='white')
fig.subplots_adjust() ticks = ax.set_xticks([0,60])
labels = ax.set_xticklabels([startdate,enddate], fontsize=10) plt.yticks()
plt.title("{} K线走势图".format(s),fontsize = 15)
plt.ylabel("股价",fontsize = 15)
candlestick2_ohlc(ax,open1,high1,low1,close1,width=0.6,colorup='red',colordown='green')Out[19]:
(<matplotlib.collections.LineCollection at 0x7f8b25f69588>,<matplotlib.collections.PolyCollection at 0x7f8b25f695f8>)
In [20]:
dt = pd.DataFrame(columns=['stock','startdate','enddate','T'])
stock = '000001.SH'
trade = list(closedf.index.strftime('%Y%m%d'))
close = list(closedf[stock])
opens = list(opendf[stock])
high = list(highdf[stock])
low = list(lowdf[stock])stocklist = list(closedf.columns)
y=0
for s in stocklist:corropen = round(np.corrcoef(opens,opendf[s])[0][1],3)corrhigh = round(np.corrcoef(high,highdf[s])[0][1],3)corrlow = round(np.corrcoef(low,lowdf[s])[0][1],3)corrclose = round(np.corrcoef(close,closedf[s])[0][1],3)#综合值T = (corropen+corrhigh+corrlow+corrclose)/4startdate = '20181109'enddate = '20190211'dt.loc[y] = [s,startdate,enddate,T]y+=1
dt = dt.fillna(0)
dt = dt.sort_values(by='T',ascending=False)
dtOut[20]:
| stock | startdate | enddate | T | |
|---|---|---|---|---|
| 0 | 000001.SH | 20181109 | 20190211 | 1.00000 |
| 177 | 000598.SZ | 20181109 | 20190211 | 0.94000 |
| 3382 | 603588.SH | 20181109 | 20190211 | 0.93050 |
| 51 | 000066.SZ | 20181109 | 20190211 | 0.92375 |
| 144 | 000553.SZ | 20181109 | 20190211 | 0.92275 |
| 2499 | 600449.SH | 20181109 | 20190211 | 0.91825 |
| 1285 | 002833.SZ | 20181109 | 20190211 | 0.91250 |
| 2806 | 600801.SH | 20181109 | 20190211 | 0.91150 |
| 2993 | 601169.SH | 20181109 | 20190211 | 0.90825 |
| 2441 | 600367.SH | 20181109 | 20190211 | 0.90800 |
| 704 | 002240.SZ | 20181109 | 20190211 | 0.90800 |
| 1213 | 002756.SZ | 20181109 | 20190211 | 0.90750 |
| 592 | 002128.SZ | 20181109 | 20190211 | 0.90675 |
| 2903 | 600926.SH | 20181109 | 20190211 | 0.90550 |
| 700 | 002236.SZ | 20181109 | 20190211 | 0.90250 |
| 882 | 002419.SZ | 20181109 | 20190211 | 0.90175 |
| 192 | 000616.SZ | 20181109 | 20190211 | 0.90175 |
| 2641 | 600618.SH | 20181109 | 20190211 | 0.89975 |
| 3126 | 601996.SH | 20181109 | 20190211 | 0.89875 |
| 99 | 000488.SZ | 20181109 | 20190211 | 0.89700 |
| 542 | 002078.SZ | 20181109 | 20190211 | 0.89700 |
| 3018 | 601318.SH | 20181109 | 20190211 | 0.89650 |
| 1762 | 300376.SZ | 20181109 | 20190211 | 0.89625 |
| 2857 | 600859.SH | 20181109 | 20190211 | 0.89625 |
| 2951 | 601009.SH | 20181109 | 20190211 | 0.89550 |
| 211 | 000639.SZ | 20181109 | 20190211 | 0.89550 |
| 869 | 002406.SZ | 20181109 | 20190211 | 0.89450 |
| 2234 | 600123.SH | 20181109 | 20190211 | 0.89300 |
| 738 | 002275.SZ | 20181109 | 20190211 | 0.89275 |
| 838 | 002375.SZ | 20181109 | 20190211 | 0.89200 |
| ... | ... | ... | ... | ... |
| 2811 | 600807.SH | 20181109 | 20190211 | -0.56725 |
| 707 | 002243.SZ | 20181109 | 20190211 | -0.56775 |
| 1333 | 002885.SZ | 20181109 | 20190211 | -0.56900 |
| 3293 | 603322.SH | 20181109 | 20190211 | -0.58125 |
| 1223 | 002767.SZ | 20181109 | 20190211 | -0.58325 |
| 181 | 000603.SZ | 20181109 | 20190211 | -0.58875 |
| 2576 | 600547.SH | 20181109 | 20190211 | -0.59000 |
| 3194 | 603098.SH | 20181109 | 20190211 | -0.59300 |
| 1944 | 300560.SZ | 20181109 | 20190211 | -0.59350 |
| 1883 | 300497.SZ | 20181109 | 20190211 | -0.59400 |
| 29 | 000035.SZ | 20181109 | 20190211 | -0.60075 |
| 2127 | 300753.SZ | 20181109 | 20190211 | -0.60125 |
| 132 | 000539.SZ | 20181109 | 20190211 | -0.60200 |
| 909 | 002446.SZ | 20181109 | 20190211 | -0.60250 |
| 2577 | 600548.SH | 20181109 | 20190211 | -0.60575 |
| 2338 | 600242.SH | 20181109 | 20190211 | -0.60775 |
| 899 | 002436.SZ | 20181109 | 20190211 | -0.61050 |
| 1529 | 300140.SZ | 20181109 | 20190211 | -0.61400 |
| 1264 | 002811.SZ | 20181109 | 20190211 | -0.61625 |
| 2015 | 300635.SZ | 20181109 | 20190211 | -0.62075 |
| 2635 | 600612.SH | 20181109 | 20190211 | -0.62150 |
| 516 | 002052.SZ | 20181109 | 20190211 | -0.62925 |
| 1948 | 300565.SZ | 20181109 | 20190211 | -0.63600 |
| 3368 | 603558.SH | 20181109 | 20190211 | -0.64475 |
| 1815 | 300429.SZ | 20181109 | 20190211 | -0.66475 |
| 2527 | 600489.SH | 20181109 | 20190211 | -0.68400 |
| 2783 | 600775.SH | 20181109 | 20190211 | -0.70025 |
| 1054 | 002592.SZ | 20181109 | 20190211 | -0.70950 |
| 1693 | 300305.SZ | 20181109 | 20190211 | -0.71700 |
| 1371 | 002927.SZ | 20181109 | 20190211 | -0.73100 |
3586 rows × 4 columns
In [21]:
dt =dt.iloc[1:]
dt.describe()Out[21]:
| T | |
|---|---|
| count | 3585.000000 |
| mean | 0.421866 |
| std | 0.349293 |
| min | -0.731000 |
| 25% | 0.240000 |
| 50% | 0.524750 |
| 75% | 0.682250 |
| max | 0.940000 |
In [22]:
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
fig = plt.figure()
axes = fig.add_axes([0.1, 0.1, 1, 0.618])
x=range(0,len(list(dt.index)))
y=list(dt['T'])
axes.scatter(x,y,color='tomato')
axes.set_title('相似度分布',fontsize=15)#设置Y轴
axes.set_yticks([-1,0,1])
axes.set_yticklabels(['-1','0','1'], fontsize=18)Out[22]:
[<matplotlib.text.Text at 0x7f8b26360470>,<matplotlib.text.Text at 0x7f8b2750f630>,<matplotlib.text.Text at 0x7f8b2695a0b8>]
In [23]:
s = 1
d = 0.2
sdf = pd.DataFrame(columns=['min','max','数量'])
for t in range(0,10):s = round(s-d,2)mn=smx=s+dnum = len(dt[(dt['T']>mn)&(dt['T']<mx)])sdf.loc[s]=[mn,mx,num]
sdfOut[23]:
| min | max | 数量 | |
|---|---|---|---|
| 0.8 | 0.8 | 1.0 | 282.0 |
| 0.6 | 0.6 | 0.8 | 1089.0 |
| 0.4 | 0.4 | 0.6 | 920.0 |
| 0.2 | 0.2 | 0.4 | 454.0 |
| 0.0 | 0.0 | 0.2 | 311.0 |
| -0.2 | -0.2 | 0.0 | 232.0 |
| -0.4 | -0.4 | -0.2 | 169.0 |
| -0.6 | -0.6 | -0.4 | 96.0 |
| -0.8 | -0.8 | -0.6 | 20.0 |
| -1.0 | -1.0 | -0.8 | 0.0 |
In [24]:
fig = plt.figure()
axes = fig.add_axes([0.1, 0.1, 1, 0.618])
x=range(0,10)
y=list(sdf['数量'])
axes.bar(x,y,color ='tomato')
axes.set_title('相似度数量分布',fontsize = 20)#设置Y轴
axes.set_xticks(x)
axes.set_xticklabels(['1.0~0.8','0.8~0.6','0.6~0.4','0.4~0.2','0.2~0.0','0.0~-0.2','-0.2~-0.4','-0.4~-0.6','-0.6~-0.8','-0.8~-1.0'], fontsize=10)Out[24]:
[<matplotlib.text.Text at 0x7f8b261da780>,<matplotlib.text.Text at 0x7f8b26acefd0>,<matplotlib.text.Text at 0x7f8b25e29710>,<matplotlib.text.Text at 0x7f8b264e6a58>,<matplotlib.text.Text at 0x7f8b2745fc18>,<matplotlib.text.Text at 0x7f8b274f3198>,<matplotlib.text.Text at 0x7f8b273dc048>,<matplotlib.text.Text at 0x7f8b266bb400>,<matplotlib.text.Text at 0x7f8b2607d240>,<matplotlib.text.Text at 0x7f8b26445ef0>]
近期与大盘走势最相似的个股
In [25]:
stock = dt.iloc[0].stock
startdate = dt.iloc[0].startdate
enddate = dt.iloc[0].enddateimport numpy as np
import matplotlib.pyplot as plt
from matplotlib.finance import candlestick2_ohlc
import datetime
s = stocktrade = list(closedf.index.strftime('%Y%m%d'))
num = trade.index(enddate)
close1 = closedf[s].iloc[num-59:num+21]
open1 = opendf[s].iloc[num-59:num+21]
high1 = highdf[s].iloc[num-59:num+21]
low1 = lowdf[s].iloc[num-59:num+21]#画图
fig,ax = plt.subplots(figsize = (10,6.18),facecolor='white')
fig.subplots_adjust() ticks = ax.set_xticks([0,60])
labels = ax.set_xticklabels([startdate,enddate], fontsize=10) plt.yticks()
plt.title("{} K线走势图".format(s),fontsize = 15)
plt.ylabel("股价",fontsize = 15)
candlestick2_ohlc(ax,open1,high1,low1,close1,width=0.6,colorup='red',colordown='green')Out[25]:
(<matplotlib.collections.LineCollection at 0x7f8b263082e8>,<matplotlib.collections.PolyCollection at 0x7f8b273ce518>)
近期与大盘走势最不相似的个股
In [26]:
stock = dt.iloc[-1].stock
startdate = dt.iloc[-1].startdate
enddate = dt.iloc[-1].enddateimport numpy as np
import matplotlib.pyplot as plt
from matplotlib.finance import candlestick2_ohlc
import datetime
s = stocktrade = list(closedf.index.strftime('%Y%m%d'))
num = trade.index(enddate)
close1 = closedf[s].iloc[num-59:num+21]
open1 = opendf[s].iloc[num-59:num+21]
high1 = highdf[s].iloc[num-59:num+21]
low1 = lowdf[s].iloc[num-59:num+21]#画图
fig,ax = plt.subplots(figsize = (10,6.18),facecolor='white')
fig.subplots_adjust() ticks = ax.set_xticks([0,60])
labels = ax.set_xticklabels([startdate,enddate], fontsize=10) plt.yticks()
plt.title("{} K线走势图".format(s),fontsize = 15)
plt.ylabel("股价",fontsize = 15)
candlestick2_ohlc(ax,open1,high1,low1,close1,width=0.6,colorup='red',colordown='green')Out[26]:
(<matplotlib.collections.LineCollection at 0x7f8b275c8da0>,<matplotlib.collections.PolyCollection at 0x7f8b257a6ba8>)
In [ ]:
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