पायथन में सामान्य वित्तीय विश्लेषण (भाग 1)

import pandas as pd
import yfinance as yf
import numpy as np
import matplotlib.pyplot as plt

sber = yf.download('SBER.ME','2016-01-01')

लाभप्रदता

#   `
daily_close = sber[['Adj Close']]

#  
daily_pct_change = daily_close.pct_change()

#  NA   0
daily_pct_change.fillna(0, inplace=True)

print(daily_pct_change.head())

#   
daily_log_returns = np.log(daily_close.pct_change()+1)

print(daily_log_returns.head())

#   `sber`      
monthly = sber.resample('BM').apply(lambda x: x[-1])

#  
print(monthly.pct_change().tail())

#  `sber`        
quarter = sber.resample("4M").mean()

#  
print(quarter.pct_change().tail())

#  
daily_pct_change = daily_close / daily_close.shift(1) - 1

print(daily_pct_change.head())

#  `daily_pct_c`
daily_pct_change.hist(bins=50)

plt.show()

#  
print(daily_pct_change.describe())

संचयी वापसी

#   
cum_daily_return = (1 + daily_pct_change).cumprod()

print(cum_daily_return.tail())

#    
cum_daily_return.plot(figsize=(12,8))

plt.show()

#   
cum_monthly_return = cum_daily_return.resample("M").mean()

print(cum_monthly_return.tail())

ticker = ['AFLT.ME','DSKY.ME','IRAO.ME','PIKK.ME', 'PLZL.ME','SBER.ME','ENRU.ME']

stock = yf.download(ticker,'2018-01-01')

#    `daily_close_px`
daily_pct_change = stock['Adj Close'].pct_change()

# 
daily_pct_change.hist(bins=50, sharex=True, figsize=(20,8))

plt.show()

from pandas.plotting import scatter_matrix

#   `daily_pct_change`  
scatter_matrix(daily_pct_change, diagonal='kde', alpha=0.1,figsize=(20,20))

plt.show()