Sector performance ranking.

Finnomena: Portfolio Specialist.

import pandas as pd
import pandas_datareader.data as web
import numpy as np
import matplotlib.pyplot as plt

import seaborn as sns
plt.style.use('fivethirtyeight')

%matplotlib inline

sector_name = {'xlb':'BasicMaterials', 'xle':'Energy', 'xlf':'Financial', 'xli':'Industrial', 'xlk':'Technology', 'xlp':'ConsumerStaples'
               , 'xlu':'Utilities', 'xlv':'HealthCare', 'xly':'ConsumerDiscretionary'}

Symbol = ['xlv','xly','xlk','xlp','xlu','xlf','xli','xle','xlb'] 


start = '2020/05/01'
end = '2020/12/15'

Price = web.get_data_yahoo(Symbol,start=start,end=end)

Price = Price['Adj Close'].rename(sector_name, axis='columns')
daily_returns = Price.pct_change().dropna()

daily_returns  # เรียกดูตัวอย่างข้อมูล

Symbols	HealthCare	ConsumerDiscretionary	Technology	ConsumerStaples	Utilities	Financial	Industrial	Energy	BasicMaterials
Date
2020-05-01	-0.019052	-0.037393	-0.027459	-0.010985	-0.024101	-0.032032	-0.030213	-0.056579	-0.021010
2020-05-04	-0.000511	0.005435	0.013273	-0.000521	0.007516	-0.009066	-0.012687	0.034589	0.003544
2020-05-05	0.021681	0.003633	0.013766	0.000868	0.008170	-0.003202	0.003904	0.000809	0.002550
2020-05-06	-0.009910	-0.000177	0.007775	-0.010930	-0.034003	-0.021570	-0.013772	-0.026131	-0.017221
2020-05-07	-0.000101	0.015719	0.014995	-0.002807	0.004012	0.023452	0.011664	0.026003	0.020510
...	...	...	...	...	...	...	...	...	...
2020-12-09	-0.003461	-0.000953	-0.019163	-0.003986	-0.001758	-0.002433	0.002242	0.002235	0.000558
2020-12-10	-0.001692	-0.000891	0.001447	-0.004446	-0.005123	0.001742	-0.009507	0.030723	-0.006132
2020-12-11	-0.003924	-0.004776	-0.002087	0.002233	0.001609	-0.010087	0.002371	-0.011538	-0.005749
2020-12-14	-0.007522	0.001472	0.003620	-0.003862	-0.005141	-0.011595	-0.012955	-0.035019	-0.012694
2020-12-15	0.010826	0.013480	0.016673	0.002386	0.019703	0.017064	0.013924	0.019657	0.018143

159 rows × 9 columns

# calculate the correlation matrix
corr = daily_returns.corr()

# plot the heatmap
plt.figure(figsize=(15,8))
sns.heatmap(corr, annot=True, ).set_title("Sector Correlation")

Text(0.5, 1.0, 'Sector Correlation')

etf_return = Price.apply(lambda x: x / x[0])
etf_return.dropna().plot(figsize=(30,15))

<matplotlib.axes._subplots.AxesSubplot at 0x7fedfa6cdd10>

# calculate the correlation matrix
#ผลตอบแทนแต่ละ sector ถึงปัจจุบัน
return_rank = etf_return.iloc[-1].sort_values(ascending = False)-1
return_rank.plot.bar(figsize=(20,8)).set_title("Ranking Performance")

Text(0.5, 1.0, 'Ranking Performance')

Happy Coding!