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

'''
獲取國內(nèi)股票數(shù)據(jù)的方式是：“股票代碼”+“對應(yīng)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(shù)
函數(shù)功能：計算股票漲跌幅=（現(xiàn)在股價-買入價格）/買入價格
輸入?yún)?shù)：column是收盤價這一列的數(shù)據(jù)
返回數(shù)據(jù)：漲跌幅
'''
def change(column):
    # 買入價格
    buyPrice=column[0]
    # 現(xiàn)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(shù)據(jù)
    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(shù)據(jù)示例
'''
sxDf = data.DataReader('005930', 'naver', start='2021-01-01', end='2022-01-01')
sxDf.head()

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    object
 1   High    248 non-null    object
 2   Low     248 non-null    object
 3   Close   248 non-null    object
 4   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 2021-12-30Data columns (total 5 columns): #   Column  Non-Null Count  Dtype  ---  ------  --------------  -----   0   Open    248 non-null    float64 1   High    248 non-null    float64 2   Low     248 non-null    float64 3   Close   248 non-null    float64 4   Volume  248 non-null    int32  dtypes: float64(4), int32(1)memory usage: 10.7 KB<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    float64
 1   High    248 non-null    float64
 2   Low     248 non-null    float64
 3   Close   248 non-null    float64
 4   Volume  248 non-null    int32  
dtypes: float64(4), int32(1)
memory usage: 10.7 KB

# 讀取數(shù)據(jù)
AliDf=pd.read_excel(r'C:\Users\EDY\Desktop\吧哩吧啦\學(xué)習\Untitled Folder\阿里巴巴2017年股票數(shù)據(jù).xlsx',index_col='Date')
AliDf.tail()

# 查看基本信息及數(shù)據(jù)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    float64
 1   High       251 non-null    float64
 2   Low        251 non-null    float64
 3   Close      251 non-null    float64
 4   Adj Close  251 non-null    float64
 5   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

# 讀取數(shù)據(jù)
GoogleDf=pd.read_excel(r'C:\Users\EDY\Desktop\吧哩吧啦\學(xué)習\Untitled Folder\谷歌2017年股票數(shù)據(jù).xlsx',index_col='Date')
GoogleDf.tail()

# 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

# 讀取數(shù)據(jù)
AppleDf=pd.read_excel(r'C:\Users\EDY\Desktop\吧哩吧啦\學(xué)習\Untitled Folder\蘋果2017年股票數(shù)據(jù).xlsx',index_col='Date')
AppleDf.tail()

# 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

# 讀取數(shù)據(jù)
TencentDf=pd.read_excel(r'C:\Users\EDY\Desktop\吧哩吧啦\學(xué)習\Untitled Folder\騰訊2017年股票數(shù)據(jù).xlsx',index_col='Date')
TencentDf.tail()

# 讀取數(shù)據(jù)
TencentDf=pd.read_excel(r'C:\Users\EDY\Desktop\吧哩吧啦\學(xué)習\Untitled Folder\騰訊2017年股票數(shù)據(jù).xlsx',index_col='Date')
TencentDf.tail()

# 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

# 讀取數(shù)據(jù)
AmazonDf=pd.read_excel(r'C:\Users\EDY\Desktop\吧哩吧啦\學(xué)習\Untitled Folder\亞馬遜2017年股票數(shù)據(jù).xlsx',index_col='Date')
AmazonDf.tail()

# 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

# 讀取數(shù)據(jù)
FacebookDf=pd.read_excel(r'C:\Users\EDY\Desktop\吧哩吧啦\學(xué)習\Untitled Folder\Facebook2017年股票數(shù)據(jù).xlsx',index_col='Date')
FacebookDf.tail()

# 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

import matplotlib.pyplot as plt

# 查看成交量與股價之間的關(guān)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(guān)系')
plt.show()

<Figure size 720x360 with 0 Axes>

# 查看各個參數(shù)之間的相關(guān)性，與股價與成交量之間呈中度相關(guān)
AliDf.corr()

AliDf['Close'].mean()

141.79179260159364

'''數(shù)據(jù)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(chuàng)建畫板
fig=plt.figure(figsize=(10,5))
# 繪圖
CloseMeanSer.plot(kind='bar')
# 設(shè)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(shè)置y周標簽刻度
plt.yticks(np.arange(0,1100,100))
# 顯示y軸網(wǎng)格
plt.grid(True,axis='y')
# 顯示圖像
plt.show()

'''數(shù)據(jù)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(chuàng)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(shè)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(shè)置y周標簽刻度
# plt.yticks(np.arange(0,1300,100))
# 顯示y軸網(wǎng)格
plt.grid(True,axis='y')
# 子圖3
ax3=plt.subplot(224)
CloseCollectDf[['Alibaba','Apple','Tencent','Facebook']].plot(ax=ax3,kind='box')
# 設(shè)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(shè)置y周標簽刻度
# plt.yticks(np.arange(0,1300,100))
# 顯示y軸網(wǎng)格
plt.grid(True,axis='y')
plt.subplot
# 顯示圖像
plt.show()

# 創(chuàng)建畫板并設(shè)置大小,constrained_layout=True設(shè)置自動調(diào)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(shè)置xy軸標簽
plt.xlabel('時間')
plt.ylabel('股價')
# 設(shè)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(shè)置x,y軸間隔,設(shè)置旋轉(zhuǎn)角度，以免重疊
plt.xticks(AliDf.index[::10],rotation=45)
plt.yticks(np.arange(0, 1300, step=100))
# 顯示網(wǎng)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(shè)置xy軸標簽
plt.xlabel('時間')
plt.ylabel('累計增長率')
# 設(shè)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(shè)置x,y軸間隔,設(shè)置旋轉(zhuǎn)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(wǎng)格
plt.grid(True)
# 調(diào)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()