3.4 熔斷機制 ? 股海拾貝之 [熔斷錯殺股] · Python 量化交易教程

# 3.4 熔斷機制 ? 股海拾貝之 [熔斷錯殺股] > 來源：https://uqer.io/community/share/568e4c78228e5b18e0ba2962 新年伊始，本是普天同慶之時，A股門前卻風聲鶴唳；熔斷機制推出4天，觸發-7%熔斷兩次；千古跌停中，有不少標的遭遇恐慌性拋售，有人稱之為熔斷機制的磁石效應！本文中，我們試圖以簡單的邏輯，來找到今天即7日有可能遭遇恐慌性拋售的熔斷錯殺股。 ```py import pandas as pd import numpy as np from matplotlib import pylab import matplotlib.pyplot as plt import seaborn ``` ## 1. 今日錯殺股 ```py from quartz.api import set_universe univ = set_universe('A') univ.remove('002778.XSHE') # 刪除2016-01-06上市新股 ``` 以最淺顯地理解，錯殺股票可能存在于以下情況中： + 今日開盤漲勢不錯，到首次熔斷時仍為紅盤，首次熔斷結束后殺跌； + 今日開盤處于跌勢，首次熔斷之前跌的少，而首次熔斷到二次熔斷之間殺跌； + 首次熔斷結束后開始交易，先殺跌，然后有明顯拉升跡象，使得二次熔斷時價格高于首次熔斷時價格三種情況下的走勢如下圖所示： ```py # 滬深300指數今日走勢 hs300PreClose = DataAPI.MktIdxdGet(tradeDate='20160106',ticker='399300').closeIndex.values[0] hs300 = DataAPI.MktBarRTIntraDayGet(securityID='399300.XSHE',startTime='09:30',endTime='10:30') hs300['hs300 index'] = hs300.closePrice/hs300PreClose - 1 hs300 = hs300[['barTime','hs300 index']] # 第一種情況，例如‘山東黃金’ case1PrePrice = DataAPI.MktEqudAdjGet(tradeDate='20160106',ticker='600547').closePrice.values[0] case1 = DataAPI.MktBarRTIntraDayGet(securityID='600547.XSHG',startTime='09:30',endTime='10:30') case1['600547.XSHG'] = case1.closePrice/case1PrePrice - 1 case1 = case1[['barTime','600547.XSHG']] # 第二種情況，例如‘紅豆股份’ case2PrePrice = DataAPI.MktEqudAdjGet(tradeDate='20160106',ticker='600400').closePrice.values[0] case2 = DataAPI.MktBarRTIntraDayGet(securityID='600400.XSHG',startTime='09:30',endTime='10:30') case2['600400.XSHG'] = case2.closePrice/case2PrePrice - 1 case2 = case2[['barTime','600400.XSHG']] # 第三種情況，例如‘天寶股份’ case3PrePrice = DataAPI.MktEqudAdjGet(tradeDate='20160106',ticker='002220').closePrice.values[0] case3 = DataAPI.MktBarRTIntraDayGet(securityID='002220.XSHE',startTime='09:30',endTime='10:30') case3['002220.XSHE'] = case3.closePrice/case3PrePrice - 1 case3 = case3[['barTime','002220.XSHE']] for case in (case1,case2,case3): hs300 = pd.merge(hs300,case) hs300 = hs300.set_index('barTime') hs300.plot(figsize=(10,6)) <matplotlib.axes.AxesSubplot at 0x6e01210> ``` ![](https://box.kancloud.cn/2016-07-30_579cbaffc94a8.png) 根據以上分析，我們拿取今日行情數據，對全A股中的股票做分析，得到表格如下，其中： + `preClose`：昨日收盤價 + `1stPrice`：首次熔斷時價格 + `2ndPrice`：二次熔斷時價格 + `1stCollapse`：首次熔斷時股價跌幅 + `2ndCollapse`：首次熔斷結束，之后股價繼續下跌的幅度，即首次熔斷后今日股價又下跌了這么多 + `collapseRatio`：首次熔斷之后的跌幅和首次熔斷之前的跌幅的比例 + `lowBetCollapse`：首次熔斷和二次熔斷之間的股價低點 + `closeToLow`：二次熔斷時的價格和lowBetCollapse的比例 ```py cols = ['preClose','1stPrice','2ndPrice','1stCollapse','2ndCollapse','collapseRatio','lowBetCollapse','closeToLow'] collapse0107 = pd.DataFrame(0.0,index=univ,columns=cols) # 股票前收盤價 collapse0107['preClose'] = DataAPI.MktEqudGet(tradeDate='20160106',secID=univ,field='secID,closePrice',pandas='1').set_index('secID') # 股票在-5%和-7%熔斷時候的價格 for stk in collapse0107.index: price = DataAPI.MktBarRTIntraDayGet(securityID=stk,startTime='09:56',endTime='10:03').closePrice.values collapse0107['1stPrice'][stk] = price[0] collapse0107['2ndPrice'][stk] = price[-1] collapse0107['lowBetCollapse'][stk] = np.min(price) # 兩次熔斷前的股票跌幅 collapse0107['1stCollapse'] = 1-collapse0107['1stPrice']/collapse0107['preClose'] collapse0107['2ndCollapse'] = 1-collapse0107['2ndPrice']/collapse0107['preClose'] - collapse0107['1stCollapse'] # 二次熔斷跌幅和一次熔斷跌幅比 collapse0107['collapseRatio'] = collapse0107['2ndCollapse']/collapse0107['1stCollapse'] # 第二次熔斷時價格與兩次熔斷之間的最低價的比值 collapse0107['closeToLow'] = collapse0107['2ndPrice']/collapse0107['lowBetCollapse'] collapse0107.sort(columns='collapseRatio',inplace=True) collapse0107 = collapse0107[~np.isnan(collapse0107.collapseRatio)] collapse0107.tail() ``` | | preClose | 1stPrice | 2ndPrice | 1stCollapse | 2ndCollapse | collapseRatio | lowBetCollapse | closeToLow | | --- | --- | | 603398.XSHG | 123.01 | 121.00 | 110.71 | 0.016340 | 0.083652 | 5.119403 | 110.71 | 1 | | 002750.XSHE | 40.59 | 40.05 | 37.00 | 0.013304 | 0.075142 | 5.648148 | 37.00 | 1 | | 600569.XSHG | 3.58 | 3.55 | 3.31 | 0.008380 | 0.067039 | 8.000000 | 3.31 | 1 | | 002768.XSHE | 71.17 | 71.00 | 66.00 | 0.002389 | 0.070254 | 29.411765 | 66.00 | 1 | | 600782.XSHG | 5.81 | 5.80 | 5.31 | 0.001721 | 0.084337 | 49.000000 | 5.31 | 1 | 按照以上數據，綜合前面的簡單邏輯，我們利用以下條件來選擇錯殺股： + `collapseRatio` > 1.5 + `collapseRatio` < -0.9 + `closeToLow` > 1.02 得到約40只股票如下 ```py good = collapse0107[(collapse0107.collapseRatio>1.5) | (collapse0107.collapseRatio<-0.9) | (collapse0107.closeToLow>1.02)].index good_stks = DataAPI.MktEqudGet(secID=good,tradeDate='20160107',field='secID,secShortName,tradeDate,preClosePrice,closePrice') good_stks ``` | | secID | secShortName | tradeDate | preClosePrice | closePrice | | --- | --- | | 0 | 000726.XSHE | 魯泰A | 2016-01-07 | 13.67 | 13.05 | | 1 | 000766.XSHE | 通化金馬 | 2016-01-07 | 14.04 | 12.68 | | 2 | 000838.XSHE | 財信發展 | 2016-01-07 | 53.34 | 49.92 | | 3 | 000856.XSHE | 冀東裝備 | 2016-01-07 | 12.91 | 12.00 | | 4 | 000937.XSHE | 冀中能源 | 2016-01-07 | 5.37 | 5.14 | | 5 | 002155.XSHE | 湖南黃金 | 2016-01-07 | 9.39 | 9.14 | | 6 | 002220.XSHE | 天寶股份 | 2016-01-07 | 16.46 | 15.15 | | 7 | 002251.XSHE | 步步高 | 2016-01-07 | 14.62 | 13.62 | | 8 | 002283.XSHE | 天潤曲軸 | 2016-01-07 | 19.47 | 18.35 | | 9 | 002291.XSHE | 星期六 | 2016-01-07 | 14.24 | 13.03 | | 10 | 002355.XSHE | 興民鋼圈 | 2016-01-07 | 18.83 | 16.95 | | 11 | 002444.XSHE | 巨星科技 | 2016-01-07 | 19.44 | 17.85 | | 12 | 002506.XSHE | 協鑫集成 | 2016-01-07 | 9.85 | 9.66 | | 13 | 002517.XSHE | 泰亞股份 | 2016-01-07 | 57.98 | 52.93 | | 14 | 002575.XSHE | 群興玩具 | 2016-01-07 | 17.67 | 16.87 | | 15 | 002588.XSHE | 史丹利 | 2016-01-07 | 31.15 | 28.13 | | 16 | 002615.XSHE | 哈爾斯 | 2016-01-07 | 29.16 | 26.64 | | 17 | 002617.XSHE | 露笑科技 | 2016-01-07 | 23.17 | 21.12 | | 18 | 002621.XSHE | 三壘股份 | 2016-01-07 | 20.91 | 20.00 | | 19 | 002640.XSHE | 跨境通 | 2016-01-07 | 31.68 | 31.42 | | 20 | 002702.XSHE | 海欣食品 | 2016-01-07 | 24.59 | 23.05 | | 21 | 002750.XSHE | 龍津藥業 | 2016-01-07 | 40.59 | 37.96 | | 22 | 002768.XSHE | 國恩股份 | 2016-01-07 | 71.17 | 67.03 | | 23 | 002779.XSHE | 中堅科技 | 2016-01-07 | 91.20 | 82.62 | | 24 | 300013.XSHE | 新寧物流 | 2016-01-07 | 17.50 | 16.38 | | 25 | 300148.XSHE | 天舟文化 | 2016-01-07 | 23.43 | 21.48 | | 26 | 300179.XSHE | 四方達 | 2016-01-07 | 9.79 | 9.18 | | 27 | 300320.XSHE | 海達股份 | 2016-01-07 | 12.88 | 11.95 | | 28 | 600005.XSHG | 武鋼股份 | 2016-01-07 | 3.66 | 3.30 | | 29 | 600057.XSHG | 象嶼股份 | 2016-01-07 | 12.01 | 11.25 | | 30 | 600262.XSHG | 北方股份 | 2016-01-07 | 35.69 | 33.50 | | 31 | 600265.XSHG | ST景谷 | 2016-01-07 | 29.81 | 28.36 | | 32 | 600291.XSHG | 西水股份 | 2016-01-07 | 26.11 | 24.16 | | 33 | 600298.XSHG | 安琪酵母 | 2016-01-07 | 30.30 | 29.47 | | 34 | 600328.XSHG | 蘭太實業 | 2016-01-07 | 13.63 | 12.54 | | 35 | 600395.XSHG | 盤江股份 | 2016-01-07 | 9.10 | 8.19 | | 36 | 600448.XSHG | 華紡股份 | 2016-01-07 | 9.01 | 8.60 | | 37 | 600547.XSHG | 山東黃金 | 2016-01-07 | 21.89 | 21.10 | | 38 | 600569.XSHG | 安陽鋼鐵 | 2016-01-07 | 3.58 | 3.38 | | 39 | 600671.XSHG | 天目藥業 | 2016-01-07 | 32.98 | 32.82 | | 40 | 600732.XSHG | *ST新梅 | 2016-01-07 | 8.51 | 8.46 | | 41 | 600782.XSHG | 新鋼股份 | 2016-01-07 | 5.81 | 5.54 | | 42 | 600874.XSHG | 創業環保 | 2016-01-07 | 9.63 | 9.67 | | 43 | 601001.XSHG | 大同煤業 | 2016-01-07 | 5.78 | 5.75 | | 44 | 603398.XSHG | 邦寶益智 | 2016-01-07 | 123.01 | 111.39 | 我們還想看一下上述股票在過去的05、06兩個交易日的表現： ```py fig = plt.figure(figsize=(10,8)) ax = fig.add_subplot(211) fullA = DataAPI.MktEqudAdjGet(secID=univ, beginDate='20160104', endDate='20160106', field='secID,tradeDate,closePrice', pandas='1') fullA = pd.DataFrame(fullA.groupby('secID').last().closePrice/fullA.groupby('secID').first().closePrice - 1) ax = pylab.hist(fullA.closePrice,bins=50,histtype='stepfilled',range=(-0.22,0.22)) pylab.xlabel("(01-05 to 01-06) 2 Days' Returns") pylab.ylabel('Number of stocks') ax = fig.add_subplot(212) good_stk_data = DataAPI.MktEqudAdjGet(secID=good, beginDate='20160104', endDate='20160106', field='secID,tradeDate,closePrice', pandas='1') good_stk_data = pd.DataFrame(good_stk_data.groupby('secID').last().closePrice/good_stk_data.groupby('secID').first().closePrice - 1) ax = pylab.hist(good_stk_data.closePrice,bins=50,histtype='stepfilled',range=(-0.22,0.22)) pylab.xlabel("(01-05 to 01-06) 2 Days' Returns") pylab.ylabel('Number of stocks') <matplotlib.text.Text at 0x7335050> ``` ![](https://box.kancloud.cn/2016-07-30_579cbaffe034a.png) 圖中，上圖為全A股在過去的5、6日兩個交易日收益表現分布；下圖為我們選出來的今日錯殺股在5、6兩個交易日的收益表現分布；明顯地，我們看出選出來的股票在過去兩天表現比較出色；當然，過去兩天的表現好不代表它們今天被錯殺 ## 2. 上次熔斷時的4日被錯殺股利用上節中的選股條件，我們選出來4日熔斷錯殺股，來驗證我們的邏輯 ```py cols = ['preClose','1stPrice','2ndPrice','1stCollapse','2ndCollapse','collapseRatio','lowBetCollapse','closeToLow'] collapse0104 = pd.DataFrame(0.0,index=univ,columns=cols) # 股票前收盤價 collapse0104['preClose'] = DataAPI.MktEqudGet(tradeDate='20151231',secID=univ,field='secID,closePrice',pandas='1').set_index('secID') # 股票在-5%和-7%熔斷時候的價格 for stk in collapse0104.index: price = DataAPI.MktBarHistOneDayGet(securityID=stk,date='20160104',startTime='13:25',endTime='13:40').closePrice.values collapse0104['1stPrice'][stk] = price[0] collapse0104['2ndPrice'][stk] = price[-1] collapse0104['lowBetCollapse'][stk] = np.min(price) # 兩次熔斷前的股票跌幅 collapse0104['1stCollapse'] = 1-collapse0104['1stPrice']/collapse0104['preClose'] collapse0104['2ndCollapse'] = 1-collapse0104['2ndPrice']/collapse0104['preClose'] - collapse0104['1stCollapse'] collapse0104['closeToLow'] = collapse0104['2ndPrice']/collapse0104['lowBetCollapse'] # 二次熔斷跌幅和一次熔斷跌幅比 collapse0104['collapseRatio'] = collapse0104['2ndCollapse']/collapse0104['1stCollapse'] collapse0104.sort(columns='collapseRatio',inplace=True) collapse0104 = collapse0104[~np.isnan(collapse0104.collapseRatio)] collapse0104.tail() ``` | | preClose | 1stPrice | 2ndPrice | 1stCollapse | 2ndCollapse | collapseRatio | lowBetCollapse | closeToLow | | --- | --- | | 600836.XSHG | 31.31 | 31.28 | 30.37 | 0.000958 | 0.029064 | 30.333333 | 29.47 | 1.030540 | | 600178.XSHG | 11.23 | 11.23 | 10.60 | 0.000000 | 0.056100 | inf | 10.48 | 1.011450 | | 600822.XSHG | 15.01 | 15.01 | 14.28 | 0.000000 | 0.048634 | inf | 14.11 | 1.012048 | | 002686.XSHE | 17.62 | 17.62 | 16.90 | 0.000000 | 0.040863 | inf | 16.18 | 1.044499 | | 002009.XSHE | 20.75 | 20.75 | 19.71 | 0.000000 | 0.050120 | inf | 19.20 | 1.026563 | 按照之前的選股條件，我們選出來了4日熔斷被錯殺的股票如下： ```py good = collapse0104[(collapse0104.collapseRatio>1.5) | (collapse0104.collapseRatio<-0.9) | (collapse0104.closeToLow>1.02)].index good_stks = DataAPI.MktEqudGet(secID=good,tradeDate='20160107',field='secID,secShortName,tradeDate,preClosePrice,closePrice') good_stks ``` | | secID | secShortName | tradeDate | preClosePrice | closePrice | | --- | --- | | 0 | 000040.XSHE | 寶安地產 | 2016-01-07 | 15.45 | 13.91 | | 1 | 000048.XSHE | 康達爾 | 2016-01-07 | 44.68 | 40.21 | | 2 | 000517.XSHE | 榮安地產 | 2016-01-07 | 6.20 | 5.58 | | 3 | 000519.XSHE | 江南紅箭 | 2016-01-07 | 19.20 | 17.28 | | 4 | 000520.XSHE | 長航鳳凰 | 2016-01-07 | 13.11 | 11.80 | | 5 | 000547.XSHE | 航天發展 | 2016-01-07 | 18.81 | 16.94 | | 6 | 000552.XSHE | 靖遠煤電 | 2016-01-07 | 9.85 | 8.87 | | 7 | 000597.XSHE | 東北制藥 | 2016-01-07 | 11.50 | 10.36 | | 8 | 000667.XSHE | 美好集團 | 2016-01-07 | 5.62 | 5.06 | | 9 | 000708.XSHE | 大冶特鋼 | 2016-01-07 | 13.20 | 11.88 | | 10 | 000709.XSHE | 河北鋼鐵 | 2016-01-07 | 3.67 | 3.30 | | 11 | 000723.XSHE | 美錦能源 | 2016-01-07 | 14.19 | 12.77 | | 12 | 000757.XSHE | 浩物股份 | 2016-01-07 | 10.80 | 9.72 | | 13 | 000767.XSHE | 漳澤電力 | 2016-01-07 | 6.29 | 5.66 | | 14 | 000795.XSHE | 太原剛玉 | 2016-01-07 | 17.12 | 15.41 | | 15 | 000801.XSHE | 四川九洲 | 2016-01-07 | 30.26 | 27.24 | | 16 | 000898.XSHE | 鞍鋼股份 | 2016-01-07 | 5.19 | 4.68 | | 17 | 000932.XSHE | 華菱鋼鐵 | 2016-01-07 | 3.85 | 4.14 | | 18 | 000952.XSHE | 廣濟藥業 | 2016-01-07 | 21.11 | 19.00 | | 19 | 000990.XSHE | 誠志股份 | 2016-01-07 | 24.59 | 22.18 | | 20 | 002009.XSHE | 天奇股份 | 2016-01-07 | 22.85 | 20.57 | | 21 | 002013.XSHE | 中航機電 | 2016-01-07 | 24.02 | 21.62 | | 22 | 002025.XSHE | 航天電器 | 2016-01-07 | 25.42 | 22.89 | | 23 | 002045.XSHE | 國光電器 | 2016-01-07 | 18.10 | 16.29 | | 24 | 002149.XSHE | 西部材料 | 2016-01-07 | 29.70 | 26.73 | | 25 | 002157.XSHE | 正邦科技 | 2016-01-07 | 21.13 | 19.30 | | 26 | 002179.XSHE | 中航光電 | 2016-01-07 | 37.56 | 34.11 | | 27 | 002191.XSHE | 勁嘉股份 | 2016-01-07 | 16.71 | 15.04 | | 28 | 002200.XSHE | 云投生態 | 2016-01-07 | 27.99 | 25.19 | | 29 | 002220.XSHE | 天寶股份 | 2016-01-07 | 16.46 | 15.15 | | ... | ... | ... | ... | ... | ... | | 104 | 600655.XSHG | 豫園商城 | 2016-01-07 | 15.44 | 14.20 | | 105 | 600662.XSHG | 強生控股 | 2016-01-07 | 17.13 | 15.42 | | 106 | 600663.XSHG | 陸家嘴 | 2016-01-07 | 50.94 | 45.93 | | 107 | 600685.XSHG | 中船防務 | 2016-01-07 | 41.41 | 37.27 | | 108 | 600734.XSHG | 實達集團 | 2016-01-07 | 24.90 | 22.41 | | 109 | 600755.XSHG | 廈門國貿 | 2016-01-07 | 8.24 | 7.43 | | 110 | 600760.XSHG | 中航黑豹 | 2016-01-07 | 14.60 | 13.17 | | 111 | 600774.XSHG | 漢商集團 | 2016-01-07 | 29.00 | 26.10 | | 112 | 600822.XSHG | 上海物貿 | 2016-01-07 | 15.97 | 14.37 | | 113 | 600826.XSHG | 蘭生股份 | 2016-01-07 | 36.16 | 32.54 | | 114 | 600833.XSHG | 第一醫藥 | 2016-01-07 | 18.55 | 16.71 | | 115 | 600834.XSHG | 申通地鐵 | 2016-01-07 | 20.30 | 18.27 | | 116 | 600836.XSHG | 界龍實業 | 2016-01-07 | 34.32 | 30.89 | | 117 | 600841.XSHG | 上柴股份 | 2016-01-07 | 18.28 | 16.45 | | 118 | 600855.XSHG | 航天長峰 | 2016-01-07 | 45.30 | 40.77 | | 119 | 600860.XSHG | 京城股份 | 2016-01-07 | 11.38 | 10.24 | | 120 | 600868.XSHG | 梅雁吉祥 | 2016-01-07 | 7.61 | 6.89 | | 121 | 600879.XSHG | 航天電子 | 2016-01-07 | 18.94 | 17.05 | | 122 | 600893.XSHG | 中航動力 | 2016-01-07 | 42.87 | 38.58 | | 123 | 600965.XSHG | 福成五豐 | 2016-01-07 | 14.85 | 13.66 | | 124 | 601069.XSHG | 西部黃金 | 2016-01-07 | 23.71 | 21.97 | | 125 | 601233.XSHG | 桐昆股份 | 2016-01-07 | 12.37 | 11.16 | | 126 | 601636.XSHG | 旗濱集團 | 2016-01-07 | 5.18 | 4.70 | | 127 | 601700.XSHG | 風范股份 | 2016-01-07 | 10.80 | 9.72 | | 128 | 601888.XSHG | 中國國旅 | 2016-01-07 | 56.49 | 51.97 | | 129 | 601890.XSHG | 亞星錨鏈 | 2016-01-07 | 13.43 | 12.09 | | 130 | 601989.XSHG | 中國重工 | 2016-01-07 | 9.40 | 8.48 | | 131 | 601998.XSHG | 中信銀行 | 2016-01-07 | 6.84 | 6.39 | | 132 | 603696.XSHG | 安記食品 | 2016-01-07 | 64.11 | 57.70 | | 133 | 603901.XSHG | 永創智能 | 2016-01-07 | 40.84 | 36.76 | ```py 134 rows × 5 columns ``` 我們選出來的4日熔斷被錯殺的股票，在后面的5、6兩日的表現究竟如何呢？請看下圖 ```py fig = plt.figure(figsize=(10,8)) ax = fig.add_subplot(211) fullA = DataAPI.MktEqudAdjGet(secID=univ, beginDate='20160104', endDate='20160106', field='secID,tradeDate,closePrice', pandas='1') fullA = pd.DataFrame(fullA.groupby('secID').last().closePrice/fullA.groupby('secID').first().closePrice - 1) ax = pylab.hist(fullA.closePrice,bins=50,histtype='stepfilled',range=(-0.22,0.22)) pylab.xlabel("(01-05 to 01-06) 2 Days' Returns") pylab.ylabel('Number of stocks') ax = fig.add_subplot(212) good_stk_data = DataAPI.MktEqudAdjGet(secID=good, beginDate='20160104', endDate='20160106', field='secID,tradeDate,closePrice', pandas='1') good_stk_data = pd.DataFrame(good_stk_data.groupby('secID').last().closePrice/good_stk_data.groupby('secID').first().closePrice - 1) ax = pylab.hist(good_stk_data.closePrice,bins=50,histtype='stepfilled',range=(-0.22,0.22)) pylab.xlabel("(01-05 to 01-06) 2 Days' Returns") pylab.ylabel('Number of stocks') <matplotlib.text.Text at 0x74df810> ``` ![](https://box.kancloud.cn/2016-07-30_579cbb00015c6.png) 圖中，上圖為全A股在過去的5、6日兩個交易日收益表現分布；下圖為我們選出來的4日熔斷錯殺股在5、6兩個交易日的收益表現分布，可以看出選出的錯殺股在過去兩天均有10%左右的漲幅；明顯地，我們看出選出來的4日熔斷錯殺股在后面的兩天表現出色 ## 3. 結論 2節中對于4日熔斷錯殺股在5、6兩個交易日的數據，似乎能夠支持我們在第1節中的錯殺股選股邏輯；對于1節中選出來的今天即7日錯殺股，搬個板凳看后面走勢究竟如何 PS：股市風險大，投資需謹慎；本文僅是研究之用，不構成任何薦股觀點