import numpy as np import pandas as pd from pandas_datareader import data import datetime as dt

數據準備

''' 獲取國內股票數據的方式是：“股票代碼”+“對應股市”（港股為.hk，A股為.ss） 例如騰訊是港股是：0700.hk ''' #字典：6家公司的股票 # gafataDict={'谷歌':'GOOG','亞馬遜':'AMZN','Facebook':'FB', '蘋果':'AAPL','阿里巴巴':'BABA','騰訊':'0700.hk'} ''' 定義函數 函數功能：計算股票漲跌幅=（現在股價-買入價格）/買入價格 輸入參數：column是收盤價這一列的數據 返回數據：漲跌幅 ''' def change(column):# 買入價格buyPrice=column[0]# 現在股價curPrice=column[column.size-1]priceChange=(curPrice-buyPrice)/buyPrice# 判斷股票是上漲還是下跌if priceChange>0:print('股票累計上漲=',round(priceChange*100,2),'%')elif priceChange==0:print('股票無變化=',round(priceChange*100,2)*100,'%')else:print('股票累計下跌=',round(priceChange*100,2)*100,'%')# 返回數據return priceChange ''' 三星電子每日股票價位信息 Open:開盤價 High:最高加 Low：最低價 Close：收盤價 Volume：成交量因雅虎連接不到，僅以三星作為獲取數據示例 ''' sxDf = data.DataReader('005930', 'naver', start='2021-01-01', end='2022-01-01') sxDf.head() OpenHighLowCloseVolumeDate2021-01-042021-01-052021-01-062021-01-072021-01-08

81000	84400	80200	83000	38655276
81600	83900	81600	83900	35335669
83300	84500	82100	82200	42089013
82800	84200	82700	82900	32644642
83300	90000	83000	88800	59013307

sxDf.info() <class 'pandas.core.frame.DataFrame'> DatetimeIndex: 248 entries, 2021-01-04 to 2021-12-30 Data columns (total 5 columns):# Column Non-Null Count Dtype --- ------ -------------- ----- 0 Open 248 non-null object1 High 248 non-null object2 Low 248 non-null object3 Close 248 non-null object4 Volume 248 non-null object dtypes: object(5) memory usage: 11.6+ KB sxDf.iloc[:,0:4]=sxDf.iloc[:,0:4].astype('float') sxDf.iloc[:,-1]=sxDf.iloc[:,-1].astype('int') sxDf.info() <class 'pandas.core.frame.DataFrame'> DatetimeIndex: 248 entries, 2021-01-04 to 2021-12-30 Data columns (total 5 columns):# Column Non-Null Count Dtype --- ------ -------------- ----- 0 Open 248 non-null float641 High 248 non-null float642 Low 248 non-null float643 Close 248 non-null float644 Volume 248 non-null int32 dtypes: float64(4), int32(1) memory usage: 10.7 KB

阿里巴巴

# 讀取數據 AliDf=pd.read_excel(r'C:\Users\EDY\Desktop\吧哩吧啦\學習\Untitled Folder\阿里巴巴2017年股票數據.xlsx',index_col='Date') AliDf.tail() OpenHighLowCloseAdj CloseVolumeDate2017-12-222017-12-262017-12-272017-12-282017-12-29

175.839996	176.660004	175.039993	176.289993	176.289993	12524700
174.550003	175.149994	171.729996	172.330002	172.330002	12913800
172.289993	173.869995	171.729996	172.970001	172.970001	10152300
173.039993	173.529999	171.669998	172.300003	172.300003	9508100
172.279999	173.669998	171.199997	172.429993	172.429993	9704600

# 查看基本信息及數據類型 AliDf.info() <class 'pandas.core.frame.DataFrame'> DatetimeIndex: 251 entries, 2017-01-03 to 2017-12-29 Data columns (total 6 columns):# Column Non-Null Count Dtype --- ------ -------------- ----- 0 Open 251 non-null float641 High 251 non-null float642 Low 251 non-null float643 Close 251 non-null float644 Adj Close 251 non-null float645 Volume 251 non-null int64 dtypes: float64(5), int64(1) memory usage: 13.7 KB # 計算漲跌幅 AliChange=change(AliDf['Close']) 股票累計上漲= 94.62 % '''增加一列累計增長百分比''' #一開始的股價 Close1=AliDf['Close'][0] # # .apply(lambda x: format(x, '.2%')) AliDf['sum_pct_change']=AliDf['Close'].apply(lambda x: (x-Close1)/Close1) AliDf['sum_pct_change'].tail() Date 2017-12-22 0.989729 2017-12-26 0.945034 2017-12-27 0.952257 2017-12-28 0.944695 2017-12-29 0.946162 Name: sum_pct_change, dtype: float64

谷歌

# 讀取數據 GoogleDf=pd.read_excel(r'C:\Users\EDY\Desktop\吧哩吧啦\學習\Untitled Folder\谷歌2017年股票數據.xlsx',index_col='Date') GoogleDf.tail() OpenHighLowCloseAdj CloseVolumeDate2017-12-222017-12-262017-12-272017-12-282017-12-29

1061.109985	1064.199951	1059.439941	1060.119995	1060.119995	755100
1058.069946	1060.119995	1050.199951	1056.739990	1056.739990	760600
1057.390015	1058.369995	1048.050049	1049.369995	1049.369995	1271900
1051.599976	1054.750000	1044.770020	1048.140015	1048.140015	837100
1046.719971	1049.699951	1044.900024	1046.400024	1046.400024	887500

# 計算漲跌幅 GoogleChange=change(GoogleDf['Close']) 股票累計上漲= 33.11 % '''增加一列累計增長百分比''' #一開始的股價 Close1=GoogleDf['Close'][0] # # .apply(lambda x: format(x, '.2%')) GoogleDf['sum_pct_change']=GoogleDf['Close'].apply(lambda x: (x-Close1)/Close1) GoogleDf['sum_pct_change'].tail() Date 2017-12-22 0.348513 2017-12-26 0.344213 2017-12-27 0.334839 2017-12-28 0.333274 2017-12-29 0.331061 Name: sum_pct_change, dtype: float64

蘋果

# 讀取數據 AppleDf=pd.read_excel(r'C:\Users\EDY\Desktop\吧哩吧啦\學習\Untitled Folder\蘋果2017年股票數據.xlsx',index_col='Date') AppleDf.tail() OpenHighLowCloseAdj CloseVolumeDate2017-12-222017-12-262017-12-272017-12-282017-12-29

174.679993	175.419998	174.500000	175.009995	174.299362	16349400
170.800003	171.470001	169.679993	170.570007	169.877396	33185500
170.100006	170.779999	169.710007	170.600006	169.907272	21498200
171.000000	171.850006	170.479996	171.080002	170.385315	16480200
170.520004	170.589996	169.220001	169.229996	168.542831	25999900

# 計算漲跌幅 AppleChange=change(AppleDf['Close']) 股票累計上漲= 45.7 % '''增加一列累計增長百分比''' #一開始的股價 Close1=AppleDf['Close'][0] # # .apply(lambda x: format(x, '.2%')) AppleDf['sum_pct_change']=AppleDf['Close'].apply(lambda x: (x-Close1)/Close1) AppleDf['sum_pct_change'].tail() Date 2017-12-22 0.506758 2017-12-26 0.468532 2017-12-27 0.468790 2017-12-28 0.472923 2017-12-29 0.456995 Name: sum_pct_change, dtype: float64

騰訊

# 讀取數據 TencentDf=pd.read_excel(r'C:\Users\EDY\Desktop\吧哩吧啦\學習\Untitled Folder\騰訊2017年股票數據.xlsx',index_col='Date') TencentDf.tail() OpenHighLowCloseAdj CloseVolumeDate2017-12-222017-12-272017-12-282017-12-292018-01-02

403.799988	405.799988	400.799988	405.799988	405.799988	16146080
405.799988	407.799988	401.000000	401.200012	401.200012	16680601
404.000000	408.200012	402.200012	408.200012	408.200012	11662053
408.000000	408.000000	403.399994	406.000000	406.000000	16601658
406.000000	406.000000	406.000000	406.000000	406.000000	0

# 騰訊是港股，轉換為美元,匯率：0.1277 TencentDf[['Open','High','Low','Close','Adj Close']]=TencentDf[['Open','High','Low','Close','Adj Close']]*0.1277 TencentDf.tail() OpenHighLowCloseAdj CloseVolumeDate2017-12-222017-12-272017-12-282017-12-292018-01-02

51.565258	51.820658	51.182158	51.820658	51.820658	16146080
51.820658	52.076058	51.207700	51.233242	51.233242	16680601
51.590800	52.127142	51.360942	52.127142	52.127142	11662053
52.101600	52.101600	51.514179	51.846200	51.846200	16601658
51.846200	51.846200	51.846200	51.846200	51.846200	0

# 計算漲跌幅 TencentChange=change(TencentDf['Close']) 股票累計上漲= 114.36 % '''增加一列累計增長百分比''' #一開始的股價 Close1=TencentDf['Close'][0] # # .apply(lambda x: format(x, '.2%')) TencentDf['sum_pct_change']=TencentDf['Close'].apply(lambda x: (x-Close1)/Close1) TencentDf['sum_pct_change'].tail() Date 2017-12-22 1.142555 2017-12-27 1.118268 2017-12-28 1.155227 2017-12-29 1.143611 2018-01-02 1.143611 Name: sum_pct_change, dtype: float64

亞馬遜

# 讀取數據 AmazonDf=pd.read_excel(r'C:\Users\EDY\Desktop\吧哩吧啦\學習\Untitled Folder\亞馬遜2017年股票數據.xlsx',index_col='Date') AmazonDf.tail() OpenHighLowCloseAdj CloseVolumeDate2017-12-222017-12-262017-12-272017-12-282017-12-29

1172.079956	1174.619995	1167.829956	1168.359985	1168.359985	1585100
1168.359985	1178.319946	1160.550049	1176.760010	1176.760010	2005200
1179.910034	1187.290039	1175.609985	1182.260010	1182.260010	1867200
1189.000000	1190.099976	1184.380005	1186.099976	1186.099976	1841700
1182.349976	1184.000000	1167.500000	1169.469971	1169.469971	2688400

# 計算漲跌幅 AmazonChange=change(AmazonDf['Close']) 股票累計上漲= 55.17 % '''增加一列累計增長百分比''' #一開始的股價 Close1=AmazonDf['Close'][0] # # .apply(lambda x: format(x, '.2%')) AmazonDf['sum_pct_change']=AmazonDf['Close'].apply(lambda x: (x-Close1)/Close1) AmazonDf['sum_pct_change'].tail() Date 2017-12-22 0.550228 2017-12-26 0.561373 2017-12-27 0.568671 2017-12-28 0.573766 2017-12-29 0.551700 Name: sum_pct_change, dtype: float64

Facebook

# 讀取數據 FacebookDf=pd.read_excel(r'C:\Users\EDY\Desktop\吧哩吧啦\學習\Untitled Folder\Facebook2017年股票數據.xlsx',index_col='Date') FacebookDf.tail() OpenHighLowCloseAdj CloseVolumeDate2017-12-222017-12-262017-12-272017-12-282017-12-29

177.139999	177.529999	176.229996	177.199997	177.199997	8509500
176.630005	177.000000	174.669998	175.990005	175.990005	8897300
176.550003	178.440002	176.259995	177.619995	177.619995	9496100
177.949997	178.940002	177.679993	177.919998	177.919998	12220800
178.000000	178.850006	176.460007	176.460007	176.460007	10261500

# 計算漲跌幅 FacebookChange=change(FacebookDf['Close']) 股票累計上漲= 51.0 % '''增加一列每日增長百分比''' # .pct_change()返回變化百分比，第一行因沒有可對比的，返回Nan，填充為0 FacebookDf['pct_change']=FacebookDf['Close'].pct_change(1).fillna(0) FacebookDf['pct_change'].head() Date 2017-01-03 0.000000 2017-01-04 0.015660 2017-01-05 0.016682 2017-01-06 0.022707 2017-01-09 0.012074 Name: pct_change, dtype: float64 '''增加一列累計增長百分比''' #一開始的股價 Close1=FacebookDf['Close'][0] # .apply(lambda x: format(x, '.2%')) FacebookDf['sum_pct_change']=FacebookDf['Close'].apply(lambda x: (x-Close1)/Close1) FacebookDf['sum_pct_change'].tail() Date 2017-12-22 0.516344 2017-12-26 0.505990 2017-12-27 0.519938 2017-12-28 0.522506 2017-12-29 0.510012 Name: sum_pct_change, dtype: float64

數據可視化

import matplotlib.pyplot as plt # 查看成交量與股價之間的關系 fig=plt.figure(figsize=(10,5)) AliDf.plot(x='Volume',y='Close',kind='scatter') plt.xlabel('成交量') plt.ylabel('股價') plt.title('成交量與股價之間的關系') plt.show() <Figure size 720x360 with 0 Axes>

# 查看各個參數之間的相關性，與股價與成交量之間呈中度相關 AliDf.corr() OpenHighLowCloseAdj CloseVolumesum_pct_changeOpenHighLowCloseAdj CloseVolumesum_pct_change

1.000000	0.999281	0.998798	0.998226	0.998226	0.424686	0.998226
0.999281	1.000000	0.998782	0.999077	0.999077	0.432467	0.999077
0.998798	0.998782	1.000000	0.999249	0.999249	0.401456	0.999249
0.998226	0.999077	0.999249	1.000000	1.000000	0.415801	1.000000
0.998226	0.999077	0.999249	1.000000	1.000000	0.415801	1.000000
0.424686	0.432467	0.401456	0.415801	0.415801	1.000000	0.415801
0.998226	0.999077	0.999249	1.000000	1.000000	0.415801	1.000000

查看各個公司的股價平均值

AliDf['Close'].mean() 141.79179260159364 '''數據準備''' # 計算每家公司的收盤價平均值 Close_mean={'Alibaba':AliDf['Close'].mean(),'Google':GoogleDf['Close'].mean(),'Apple':AppleDf['Close'].mean(),'Tencent':TencentDf['Close'].mean(),'Amazon':AmazonDf['Close'].mean(),'Facebook':FacebookDf['Close'].mean()} CloseMeanSer=pd.Series(Close_mean) CloseMeanSer.sort_values(ascending=False,inplace=True) '''繪制柱狀圖''' # 創建畫板 fig=plt.figure(figsize=(10,5)) # 繪圖 CloseMeanSer.plot(kind='bar') # 設置x、y軸標簽及標題 plt.xlabel('公司') plt.ylabel('股價平均值（美元）') plt.title('2017年各公司股價平均值') # 設置y周標簽刻度 plt.yticks(np.arange(0,1100,100)) # 顯示y軸網格 plt.grid(True,axis='y') # 顯示圖像 plt.show()

亞馬遜和谷歌的平均股價很高，遠遠超過其他4家，但是僅看平均值并不能代表什么，下面從分布和走勢方面查看

查看各公司股價分布情況

'''數據準備''' # 將6家公司的收盤價整合到一起 CloseCollectDf=pd.concat([AliDf['Close'],GoogleDf['Close'],AppleDf['Close'],TencentDf['Close'],AmazonDf['Close'],FacebookDf['Close']],axis=1) CloseCollectDf.columns=['Alibaba','Google','Apple','Tencent','Amazon','Facebook']'''繪制箱型圖''' # 創建畫板 fig=plt.figure(figsize=(20,10)) fig.suptitle('2017年各公司股價分布',fontsize=18) # 子圖1 ax1=plt.subplot(121) CloseCollectDf.plot(ax=ax1,kind='box') plt.xlabel('公司') plt.ylabel('股價（美元）') plt.title('2017年各公司股價分布') plt.grid(True,axis='y')# 因谷歌和亞馬遜和兩外四家的差別較大，分開查看， # 子圖2 ax2=plt.subplot(222) CloseCollectDf[['Google','Amazon']].plot(ax=ax2,kind='box') # 設置x、y軸標簽及標題 plt.ylabel('股價（美元）') plt.title('2017年谷歌和亞馬遜股價分布') # 設置y周標簽刻度 # plt.yticks(np.arange(0,1300,100)) # 顯示y軸網格 plt.grid(True,axis='y')# 子圖3 ax3=plt.subplot(224) CloseCollectDf[['Alibaba','Apple','Tencent','Facebook']].plot(ax=ax3,kind='box') # 設置x、y軸標簽及標題 plt.xlabel('公司') plt.ylabel('股價（美元）') plt.title('2017年阿里、蘋果、騰訊、Facebook股價分布') # 設置y周標簽刻度 # plt.yticks(np.arange(0,1300,100)) # 顯示y軸網格 plt.grid(True,axis='y')plt.subplot# 顯示圖像 plt.show()

從箱型圖看，谷歌和亞馬遜的股價分布較廣，且中位數偏上，騰訊股價最為集中，波動最小，相對穩定。

股價走勢對比

# 創建畫板并設置大小,constrained_layout=True設置自動調整子圖之間間距 fig=plt.figure(figsize=(15,10),constrained_layout=True) # ax=plt.subplots(2,1,sharex=True) fig.suptitle('股價走勢對比',fontsize=18)'''繪制圖像1 ''' ax1=plt.subplot(211) plt.plot(AliDf.index,AliDf['Close'],label='Alibaba') plt.plot(GoogleDf.index,GoogleDf['Close'],label='Google') plt.plot(AppleDf.index,AppleDf['Close'],label='Apple') plt.plot(TencentDf.index,TencentDf['Close'],label='Tencent') plt.plot(AmazonDf.index,AmazonDf['Close'],label='Amazon') plt.plot(FacebookDf.index,FacebookDf['Close'],label='Facebook') # # 設置xy軸標簽 plt.xlabel('時間') plt.ylabel('股價') # 設置標題 # plt.title('股價走勢對比') # 圖例顯示位置、大小 plt.legend(loc='upper left',fontsize=12) # 設置x,y軸間隔,設置旋轉角度，以免重疊 plt.xticks(AliDf.index[::10],rotation=45) plt.yticks(np.arange(0, 1300, step=100)) # 顯示網格 plt.grid(True)'''繪制圖像2''' ax2=plt.subplot(212) plt.plot(AliDf.index,AliDf['sum_pct_change'],label='Alibaba') plt.plot(GoogleDf.index,GoogleDf['sum_pct_change'],label='Google') plt.plot(AppleDf.index,AppleDf['sum_pct_change'],label='Apple') plt.plot(TencentDf.index,TencentDf['sum_pct_change'],label='Tencent') plt.plot(AmazonDf.index,AmazonDf['sum_pct_change'],label='Amazon') plt.plot(FacebookDf.index,FacebookDf['sum_pct_change'],label='Facebook')# 設置xy軸標簽 plt.xlabel('時間') plt.ylabel('累計增長率') # 設置標題 # plt.title('股價走勢對比') # 圖例顯示位置、大小 plt.legend(loc='upper left',fontsize=12) # 設置x,y軸間隔,設置旋轉角度，以免重疊 plt.xticks(AliDf.index[::10],rotation=45) plt.yticks(np.arange(0, 1.2, step=0.1)) # 顯示網格 plt.grid(True)# 調整子圖間距，subplots_adjust(left=None, bottom=None, right=None, top=None,wspace=None, hspace=None)# 顯示圖像 plt.show()

可以看出，在2017年間，亞馬遜和谷歌的股價雖然偏高，漲幅卻不如阿里巴巴和騰訊。

總結

觀察以上圖形，可以得出一下結果：
1、2017年谷歌和亞馬遜股價偏高，波動較大，但其漲幅并不高；
2、2017年阿里巴巴和騰訊的股價平均值相對較小，股價波動比較小，其漲幅卻很高，分別達到了94.62%和114.36%。

總結

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