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#Import necesary libraries
from datetime import date
import numpy as np
import pandas as pd 
import matplotlib.pyplot as plt 
import seaborn as sns
import yfinance as yf


%matplotlib inline

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def boxplot_group_day(returns_slice):
    Daily_Returns = returns_slice.groupby([returns_slice.index.isocalendar().week.rename('week'), 
                                 returns_slice.index.dayofweek.rename('day')]).mean()
    return Daily_Returns.boxplot(column='Returns', by='day')

boxplot_group_day(returns);

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returns_of_june = returns.loc[returns.index.month.isin([6])]
boxplot_group_day(returns_of_june);

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returns_of_december = returns.loc[returns.index.month.isin([12])]
boxplot_group_day(returns_of_december);

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md_returns = returns.squeeze().groupby([returns.index.month.rename('month'), 
                                 returns.index.dayofweek.rename('day')]).mean().unstack(0)

fig, ax = plt.subplots(figsize=(16,6))
sns.heatmap(md_returns, ax=ax, cmap="Spectral");

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Monthly_Returns = ratesD.groupby([ratesD.index.year.rename('year'), 
                                  ratesD.index.month.rename('month')]
                                ).median().to_frame("Returns")
Monthly_Returns.boxplot(by='month', figsize=(16, 8));

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Monthly_Returns = ratesD.groupby([ratesD.index.year.rename('year'), 
                                  ratesD.index.month.rename('month')]
                                ).median().to_frame("Returns")
Monthly_Returns.boxplot(by='month', figsize=(16, 8));

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import statsmodels.api as sm
decomposition = sm.tsa.seasonal_decompose(data['Adj Close'], model="additive", period = 253)

trend = decomposition.trend
seasonal = decomposition.seasonal
residual = decomposition.resid

fig, axs = plt.subplots(4, figsize=(14, 10), sharex=True)
data['Adj Close'].plot(title='Original', color="blue", ax=axs[0])
trend.plot(title='Trend', color="red", ax=axs[1])

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seasonal["2019"].plot(label='Seasonality', color="blue", figsize=(20,8));

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seasonal["2019-11":"2019-12"].plot(label='Seasonality', color="blue", figsize=(20,8));