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import yfinance as yf
import pandas as pd
import numpy as np
from scipy.stats import norm
import matplotlib.pyplot as plt
import seaborn as sns

cryptocurrency = ['BTC-USD', 'ETH-USD', 'USDT-USD', 'XRP-USD', 'LTC-USD', 'BCH-USD', 'ADA-USD', 'BNB-USD', 'LINK-USD']

data= yf.download(cryptocurrency, start="2018-01-01", end="2020-12-31")['Close']

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fig, axs = plt.subplots(5, 2, figsize=(15,15))

axs[0, 0].plot(data.index, data['BTC-USD'], 'tab:blue' )
axs[0, 0].set_title('BTC')
axs[0, 1].plot(data.index, data['ETH-USD'], 'tab:orange')
axs[0, 1].set_title('ETH')
axs[1, 0].plot(data.index, data['USDT-USD'], 'tab:green')
axs[1, 0].set_title('USDT')
axs[1, 1].plot(data.index, data['XRP-USD'], 'tab:red')
axs[1, 1].set_title('XRP')

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return_data = data.pct_change()
return_data.head()

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var_matrix = return_data.cov()
var_matrix

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port_return = []

# Initialize an empty list for storing the portfolio volatility
port_volatility = []

# Initialize an empty list for storing the portfolio weights
port_weights = []

num_assets = len(data.columns)
num_portfolio = 1000000

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rf = 0.02
min_vol_port = portfolios_V1.iloc[portfolios_V1['Volatility'].idxmin()]
optimal_risky_port = portfolios_V1.iloc[((portfolios_V1['Returns']-rf)/portfolios_V1['Volatility']).idxmax()]
max_ret_port = portfolios_V1.iloc[portfolios_V1['Returns'].idxmax()]
weights_min_vol = np.array(min_vol_port[2:])

#weights in a portfolio with max Sharpe Ratio
weights_opt_sr = np.array(optimal_risky_port[2:])

#weights in a portfolio with max Returns

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df_weights = pd.DataFrame(columns=portfolios_V1.columns)
df_weights = df_weights.append(min_vol_port.rename("Minimum Volatility").to_frame().T)
df_weights = df_weights.append(max_ret_port.rename("Maximum Returns").to_frame().T)
df_weights = df_weights.append(optimal_risky_port.rename("Maximum Sharpe Ratio").to_frame().T)

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array_returns = np.asarray(return_data.dropna())
array_cov = np.asarray(var_matrix)

mean_returns = np.mean(array_returns, axis = 0)

portfolios_V1_div = portfolios_V1.copy()

div_ratio = []

for i in range(portfolios_V1.shape[0]):

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df_weights

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df_weights_circle = df_weights.drop(['Returns', 'Volatility'], axis = 1)
df_weights_circle