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color_pie =  ['Grey', 'Purple', 'Blue', 'Green', 'Orange', 'Red', 'Yellow', 'magenta', 'cyan']
color_pie_dict = dict(zip(df_weights_circle.columns, color_pie))

fig, axes = plt.subplots(2, 2, figsize=(15, 10))

for i, (idx, row) in enumerate(df_weights_circle.iterrows()):
  ax = axes[i//2, i%2]
  row = row[row.gt(row.sum() * .01)]
  ax.pie(row, labels=row.index, colors=[color_pie_dict.get(i) for i in row.index], startangle=30)
  ax.set_title(idx)

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data_2021= yf.download(cryptocurrency, start="2020-12-31", end="2021-12-31")['Adj Close']
[*********************100%***********************]  9 of 9 completed

data_2021.head()

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return_data_2021 = data_2021.pct_change()
cum_daily_return = (1 + return_data_2021).cumprod()
var_matrix_2021 = return_data_2021.cov()
var_matrix_2021.head()

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individual_rets_2021 = data_2021.resample('Y').last().pct_change().mean()
result_2021 = pd.DataFrame()
for i in range(df_weights_circle.shape[0]):
  weight = np.array(df_weights_circle.iloc[i])
  ret = (np.dot(weight, individual_rets_2021))
  vol = np.sqrt(np.dot(weight.T,np.dot(var_matrix*252,weight))) 
  result_2021 = result_2021.append(pd.Series([ret, vol]).rename(df_weights_circle.iloc[[i]].index[0]).to_frame().T)
result_2021.columns = ['Returns', 'Volatility']
result_2021

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fig, axs = plt.subplots(figsize=(15,10))

(cum_daily_return.multiply(weights_min_vol, axis=1).sum(axis=1) - 1).plot(label = 'Minimum volatility')
(cum_daily_return.multiply(weights_opt_sr, axis=1).sum(axis=1) - 1).plot(label = 'Maximum Sharp')
(cum_daily_return.multiply(weights_div, axis=1).sum(axis=1) - 1).plot(label = 'Maximum Diversification')
(cum_daily_return.multiply(weights_max_ret, axis=1).sum(axis=1) - 1).plot(label = 'Maximum Returns')

plt.title('2021 year')

plt.ylim(0)

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import pandas as pd
import numpy as np
import seaborn as sns

from scipy.stats import mannwhitneyu
from scipy.stats import ttest_ind
from scipy.stats import norm
from scipy.stats import shapiro
from scipy.stats import levene
from scipy.stats import normaltest

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def get_bootstrap(
    data_column_1, # numeric values of the first sample
    data_column_2, # numeric values of the second sample
    boot_it = 10000, # number of bootstrap subsamples
    statistic = np.mean, # statistics of interest to us
    bootstrap_conf_level = 0.95 # significance level
):
    boot_len = max([len(data_column_1), len(data_column_2)])
    boot_data = []
    for i in range(boot_it): # extracting subsamples

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game = pd.read_csv('NameOfGames.csv', skiprows=1)
game.head()

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game.info()

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# Rewarded Videos Ads Watched
rvaw = game[['A', 'B']][:642]

# Interstitial Ads Watched
iaw = game[['A.1', 'B.1']][:839]
iaw.columns = ['A', 'B']

# User Progress Level
upl = game[['A.2', 'B.2']][:2115]
upl.columns = ['A', 'B']