國泰銀行,新光,中國信託,台新金4家銀行,2023年度的平均差,每日差,相關係數

[roberthsu2003]

[FriendlyLu]

import yfinance as yf
import pandas as pd

#國泰金(2882.TW),新光金(2888.TW),中信金(2891.TW),台新金(2887.TW)
all_data = {ticker:yf.download(ticker) for ticker in ['2882.TW','2888.TW','2891.TW','2887.TW']}

all_data1 = {ticker:df['Adj Close'] for ticker,df in all_data.items()}
prices_df = pd.DataFrame(all_data1)

prices_df.columns = ['國泰金','新光金','中信金','台新金']
prices_df1 = prices_df.dropna()
prices_df1_2023 = prices_df1.loc['2023']

#平均差
prices_df1_2023_avg_diff = prices_df1_2023.pct_change()
prices_df1_2023_avg_diff.dropna()

#每日差
prices_df1_2023_day_diff = prices_df1_2023 - prices_df1_2023.shift(1)
prices_df1_2023_day_diff.dropna()

#相關係數
prices_df1_2023_avg_diff.corr()

[hellno3009]

import yfinance as yf
import pandas as pd
yf.download("AAPL")
all_data = {ticker:yf.download(ticker) for ticker in ['2882.TW','2888.TW','2891.TW','2887.TW']}
all_data1 = {ticker:df['Adj Close'] for ticker,df in all_data.items()}
prices_df = pd.DataFrame(all_data1)
prices_df.columns = ['國泰金','新光金','中信金','台新金']
prices_df1 = prices_df.dropna()
prices_df1_2023 = prices_df1.loc["2023"]

prices_df1_2023_avg = prices_df1_2023.pct_change()
display(prices_df1_2023_avg.dropna())

prices_df1_2023_day = prices_df1_2023 - prices_df1_2023.shift(1)
display(prices_df1_2023_day.dropna())

display(prices_df1_2023_avg.corr())

[yawenny]

import yfinance as yf
import pandas as pd
yf.download('AAPL')

all_data = {ticker:yf.download(ticker) for ticker in ['2882.TW','2888.TW','2891.TW','2887.TW']}
all_data

all_data1 = {ticker:df['Adj Close'] for ticker,df in all_data.items()}
prices_df = pd.DataFrame(all_data1)
prices_df

prices_df.columns = ['國泰金','新光金','中信金','台新金']
prices_df1 = prices_df.dropna()
prices_df1

prices_df1_2023 = prices_df1.loc['2023']

#平均差
prices_pce_change = prices_df1_2023.pct_change()
prices_pce_change_nonan =prices_pce_change.dropna()
display(prices_pce_change_nonan)

#每日差
prices_diff = prices_df1_2023-prices_df1_2023.shift(1)
prices_diff_nonan =prices_diff.dropna()
display(prices_diff_nonan)

#相關係數
prices_corr = prices_pce_change.corr()
display(prices_corr)

[WayneChou-bot]

import pandas_datareader.data as pdr
import yfinance as yf
import pandas as pd

all_data1 = {ticker:df['Adj Close'] for ticker,df in all_data.items()}
prices_df = pd.DataFrame(all_data1)
prices_df.columns = ['國泰銀行','新光金','中信金','台新金']
prices_df1 = prices_df.dropna()
prices_df1_2023 = prices_df1.loc['2023-01-01':'2023-12-31']
prices_pct_change = prices_df1_2023.pct_change()      # 平均差
print(prices_pct_change)
print(prices_df1_2023 - prices_df1_2023.shift(1))          # 每天的差值
print(prices_pct_change.corr())                                      # 相關係數

[Oscar-Lee-20211298]

import yfinance as yf
import pandas as pd

yf.download('AAPL')
all_data = {ticker:yf.download(ticker) for ticker in ['2882.TW','2888.TW','2891.TW','2887.TW']}

all_data1 = {ticker:df['Adj Close'] for ticker,df in all_data.items()}
prices_df = pd.DataFrame(all_data1)

prices_df.columns = ['國泰金','新光金','中信金','台新金']
prices_df1 = prices_df.dropna()
prices_df1_2023 = prices_df1.loc['2023']

prices_df1_2023_avg = prices_df1_2023.pct_change()
display(prices_df1_2023_avg.dropna())

prices_df1_2023_day = prices_df1_2023 - prices_df1_2023.shift(1)
display(prices_df1_2023_day.dropna())

display(prices_df1_2023_avg.corr())

[teddy-wz]

import yfinance as yf
import pandas as pd
yf.download('AAPL')

all_data={ticker:yf.download(ticker) for ticker in ['2882.TW','2888.TW','2891.TW','2887.TW']}
all_data1={ticker:df['Adj Close'] for ticker, df in all_data.items()}
prices_df=pd.DataFrame(all_data1)
prices_df.columns=['國泰金','新光金','中信金','台新金']
prices_df1=prices_df.dropna()

prices_df1_2023=prices_df1.loc["2023"]

prices_pce_change=prices_df1_2023.pct_change()
prices_pce_change_nonan=prices_pce_change.dropna()
display(prices_pce_change_nonan)

prices_diff=prices_df1_2023-prices_df1_2023.shift(1)
prices_diff_nonan=prices_diff.dropna()
display(prices_diff_nonan)

prices_corr=prices_pce_change.corr()
display(prices_corr)

[grace588]

mport pandas_datareader.data as pdr
import yfinance as yf
yf.download('AAPL')

#國泰金控（2882）、新光金控（2888）、中信金控（2891）、台新金控（2887）
yf.download('2330.TW')

all_data = {ticker:yf.download(ticker) for ticker in ['2882.TW','2888.TW','2891.TW','2887.TW']}
all_data1 = {ticker:df['Adj Close'] for ticker,df in all_data.items()}
prices_df = pd.DataFrame(all_data1)
prices_df.columns = ['國泰金控','新光金控','中信金控','台新金控']
prices_df1 = prices_df.dropna()
prices_df1_202305 = prices_df1.loc['2023']

#平均差
prices_pct_change = prices_df1_202305.pct_change()
prices_pct_change

#每日差
prices_diff = prices_df1_2023-prices_df1_2023.shift(1)

#相關係數
prices_pct_change.corr()
prices_pct_change.plot()

[Srdell7]

import yfinance as yf
import pandas as pd

all_data = {ticker:yf.download(ticker) for ticker in ['2882.TW','2888.TW','2891.TW','2887.TW']}

all_data = {ticker:df['Adj Close'] for ticker,df in all_data.items()}
prices_df = pd.DataFrame(all_data)
prices_df.columns = ['國泰金','新光金','中信金','台新金']
prices_df1 = prices_df.dropna()

prices_df1.loc['2023']

prices_df1_2023 = prices_df1.loc['2023']
prices_pct_change = prices_df1_2023.pct_change()#平均差
prices_df1_2023 - prices_df1_2023.shift(1)#每日差
prices_pct_change.corr()#相關係數

display(prices_pct_change)
display(prices_df1_2023 - prices_df1_2023.shift(1))
display(prices_pct_change.corr())