#Diable the warnings
import warnings
warnings.filterwarnings('ignore')



pip install yfinance

import numpy as np
import pandas as pd  
import pandas_datareader as pdr
import yfinance as yf

import scipy as sp
from scipy.stats import norm

from pylab import plt, mpl
import matplotlib.pyplot as plt
import seaborn as sns

from pandas.plotting import scatter_matrix
from statsmodels.graphics.tsaplots import plot_acf

#plt.style.use('seaborn')
plt.style.use('fivethirtyeight')
mpl.rcParams['font.family'] = 'DejaVu Sans'
%matplotlib inline

pd.set_option('precision', 3)
pd.set_option('display.max_colwidth', 100)

def get_prices(tickers, freq_p, st_day, end_day):
    mystock = pd.DataFrame()
    for t in tickers:
        mystock[t] = yf.download(t, start=st_day, end=end_day, interval=freq_p)['Adj Close']
    return mystock

tic=['SPY', 'IEF', 'TLT','TSLA', 'AAPL', 'NVDA', 'BAC']
prices= get_prices(tic, freq_p='1d', st_day="2011-01-01", end_day="2022-05-31")  # id , 1wk, 1mo
print(prices)

prices.info()
prices.tail(20)



prices.info()

prices.plot(figsize=(10, 18), subplots=True)


prices.pct_change().mean().plot(kind='bar', figsize=(10, 6));  

prices.pct_change().std().plot(kind='bar', figsize=(10, 6));  

rets = np.log(prices / prices.shift(1))  
rets.cumsum().apply(np.exp).plot(figsize=(12, 13))  

rets.plot.scatter("SPY", "TSLA", alpha=0.9);

rets.plot.scatter("SPY", "AAPL", alpha=0.6);

rets.plot.scatter("SPY", "IEF", alpha=0.8);

rets.hist(bins=50, sharex=False, sharey=False, xlabelsize=1, ylabelsize=1, figsize=(12,12))
plt.show()

rets.plot(kind='density', subplots=True, layout=(4,4), sharex=True, legend=True, fontsize=1, figsize=(15,15))
plt.show()

correlation = rets.corr()
plt.figure(figsize=(15,15))
plt.title('Correlation Matrix')
sns.heatmap(correlation, vmax=1, square=True,annot=True,cmap='cubehelix')

plt.figure(figsize=(15,15))
scatter_matrix(rets,figsize=(12,12))
plt.show()

!pip install mplcyberpunk

import mplcyberpunk
plt.style.use("cyberpunk")

rets.plot(kind='density', subplots=True, layout=(4,4), sharex=True, legend=True, fontsize=1, figsize=(15,15))
plt.show()
mplcyberpunk.add_glow_effects()

rets = np.log(prices / prices.shift(1))  
rets.cumsum().apply(np.exp).plot(figsize=(12, 13))