Sberbank के पसंदीदा और साधारण शेयरों के उदाहरण का उपयोग करके अजगर में दो शेयरों के उद्धरण की गतिशीलता की तुलना करें

import pandas as pd
import matplotlib.pyplot as plt

pd.set_option('display.max_columns', None)
pd.set_option('display.expand_frame_repr', False)
pd.set_option('max_colwidth', 80)
pd.set_option('max_rows', 6000)

स्टॉक डेटा पढ़ें

df = pd.read_csv("SBER_190101_200105.csv",sep=';', header=0, index_col='<DATE>', parse_dates=True)

df = df.sort_values(by='<DATE>')

print(df)

df['returns']=(df['<CLOSE>']/df['<CLOSE>'].shift(1))-1

df['returns_pers']=((df['<CLOSE>']/df['<CLOSE>'].shift(1))-1)*100

एक दूसरा हिस्सा जोड़ें

df2 = pd.read_csv("SBERP_190101_200105.csv",sep=';', header=0, index_col='<DATE>', parse_dates=True)

df = df.sort_values(by='<DATE>')

df2['returns_pers']=((df2['<CLOSE>']/df2['<CLOSE>'].shift(1))-1)*100
df2['returns']=(df2['<CLOSE>']/df2['<CLOSE>'].shift(1))-1
print(df2)

हम अपने स्टॉक कोट्स की कल्पना करते हैं

df['<CLOSE>'].plot(label='sber')
df2['<CLOSE>'].plot(label='sberp')
plt.legend()
plt.show()

df['<CLOSE>'].plot(label='sber')
df2['<CLOSE>'].plot(label='sberp')
df['ma50'] = df['<OPEN>'].rolling(50).mean().plot(label='ma50')
df2['ma50'] = df2['<OPEN>'].rolling(50).mean().plot(label='ma50')
plt.legend()
plt.show()

df['<CLOSE>'].plot(label='sber')
df2['<CLOSE>'].plot(label='sberp')
df['ma100'] = df['<OPEN>'].rolling(100).mean().plot(label='ma100')
df2['ma100'] = df2['<OPEN>'].rolling(100).mean().plot(label='ma100')
plt.legend()
plt.show()

df['total_trade'] = df['<OPEN>']*df['<VOL>']
df2['total_trade'] = df2['<OPEN>']*df2['<VOL>']
df['total_trade'].plot(label='sber',figsize=(16,8))
df2['total_trade'].plot(label='sberp',figsize=(16,8))
plt.legend()
plt.ylabel('Total Traded')
plt.show()

सहसंबंध विश्लेषण

all_sber = pd.concat([df['<OPEN>'],df2['<OPEN>']],axis=1)
all_sber.columns = ['sber_open','sberp_open']
print(all_sber)

from pandas.plotting import scatter_matrix

scatter_matrix(all_sber,figsize=(8,8),alpha=0.2,hist_kwds={'bins':100});
plt.show()

प्रतिभूति अस्थिरता मूल्यांकन

df['returns_pers'].plot(label='sber')
df2['returns_pers'].plot(label='sberp')
plt.legend()
plt.show()

df['returns_pers'].hist(bins=100,label='sber',alpha=0.5)
df2['returns_pers'].hist(bins=100,label='sberp',alpha=0.5)
plt.legend()
plt.show()

df['returns_pers'].hist(bins=10,label='sber',alpha=0.9)
df2['returns_pers'].hist(bins=10,label='sberp',alpha=0.9)
plt.legend()
plt.show()

संचित राजस्व विश्लेषण

df['Cumulative Return'] = (1+ df['returns']).cumprod()
df2['Cumulative Return'] = (1+ df2['returns']).cumprod()
print(df)
print(df2)

df['Cumulative Return'].plot(label='sber')
df2['Cumulative Return'].plot(label='sberp')
plt.legend()
plt.show()