# Install required packages !pip install pandas_datareader !pip install yfinance !pip install numpy !pip install matplotlib !pip install PyPortfolioOpt # Import necessary libraries import pandas_datareader as pdr import pandas as pd import yfinance as yf import matplotlib.pyplot as plt import numpy as np import datetime from pypfopt.efficient_frontier import EfficientFrontier from pypfopt import risk_models from pypfopt import expected_returns from pypfopt import black_litterman, BlackLittermanModel, objective_functions

# Enable Yahoo Finance source yf.pdr_override() # Date range: past 5 years end_date = datetime.datetime.now() start_date = end_date - datetime.timedelta(days=5*365) # Selected stocks stocks = ['CELH', 'PWR', 'FRHC', 'RWWI', 'KLAC'] # Fetch data data = pdr.data.get_data_yahoo(stocks, start=start_date, end=end_date)['Adj Close'] # Calculate daily returns daily_returns = data.pct_change().dropna() # Statistical Exploration print("Mean Returns:") print(daily_returns.mean()) print("\nStandard Deviation:") print(daily_returns.std()) # Visualization for stock in stocks: plt.hist(daily_returns[stock], bins=50, alpha=0.5, label=f'{stock}') plt.xlabel('Daily Return') plt.ylabel('Frequency') plt.title('Distribution of Daily Returns in the Past 5 Years') plt.legend() plt.show()

# Enable Yahoo Finance source yf.pdr_override() # Date range: past 5 years end_date = datetime.datetime.now() start_date = end_date - datetime.timedelta(days=5*365) # Selected stocks stocks = ['CELH', 'PWR', 'FRHC', 'RWWI', 'KLAC'] # Calculate daily returns data = pdr.data.get_data_yahoo(stocks, start=start_date, end=end_date)['Adj Close'] daily_returns = data.pct_change().dropna() # Calculate expected returns and sample covariance mu = expected_returns.mean_historical_return(data) S = risk_models.sample_cov(data) # Optimize the portfolio for equal weighting ef = EfficientFrontier(mu, S) weights = ef.max_sharpe() # Maximize the Sharpe ratio ef.portfolio_performance(verbose=True) # Re-calculate the daily portfolio returns based on optimized weights optimized_weights = np.array(list(weights.values())) daily_portfolio_returns = daily_returns.dot(optimized_weights) # Basic Portfolio Statistics annualized_return = ((1 + daily_portfolio_returns.mean()) ** 252) - 1 annualized_volatility = daily_portfolio_returns.std() * np.sqrt(252) # Assuming a risk-free rate of 1% for the Sharpe ratio risk_free_rate = 0.01 sharpe_ratio = (annualized_return - risk_free_rate) / annualized_volatility print(f"\nAdditional Portfolio Statistics:") print(f"Annualized Portfolio Return: {annualized_return}") print(f"Annualized Portfolio Volatility: {annualized_volatility}") print(f"Portfolio Sharpe Ratio: {sharpe_ratio}") # Cumulative Returns Plot cumulative_returns = (1 + daily_portfolio_returns).cumprod() plt.plot(cumulative_returns.index, cumulative_returns) plt.xlabel('Date') plt.ylabel('Cumulative Returns') plt.title('5-Year Cumulative Returns of the Portfolio') plt.show()

# Enable Yahoo Finance source yf.pdr_override() # Date range: past 5 years end_date = datetime.datetime.now() start_date = end_date - datetime.timedelta(days=5*365) # Selected stocks stocks = ['CELH', 'PWR', 'FRHC', 'RWWI', 'KLAC'] # Calculate daily returns data = pdr.data.get_data_yahoo(stocks, start=start_date, end=end_date)['Adj Close'] daily_returns = data.pct_change().dropna() # Calculate expected returns and sample covariance mu = expected_returns.mean_historical_return(data) S = risk_models.sample_cov(data) # Portfolio with Maximum Sharpe Ratio ef = EfficientFrontier(mu, S) max_sharpe_weights = ef.max_sharpe() # Maximize the Sharpe ratio ef.portfolio_performance(verbose=True) daily_max_sharpe_returns = daily_returns.dot(np.array(list(max_sharpe_weights.values()))) # Portfolio with Minimum Volatility ef = EfficientFrontier(mu, S) min_volatility_weights = ef.min_volatility() ef.portfolio_performance(verbose=True) daily_min_volatility_returns = daily_returns.dot(np.array(list(min_volatility_weights.values()))) # Function to calculate statistics def calculate_statistics(daily_portfolio_returns): annualized_return = ((1 + daily_portfolio_returns.mean()) ** 252) - 1 annualized_volatility = daily_portfolio_returns.std() * np.sqrt(252) risk_free_rate = 0.01 sharpe_ratio = (annualized_return - risk_free_rate) / annualized_volatility print(f"Annualized Portfolio Return: {annualized_return}") print(f"Annualized Portfolio Volatility: {annualized_volatility}") print(f"Portfolio Sharpe Ratio: {sharpe_ratio}") print("\nMaximum Sharpe Portfolio Statistics:") calculate_statistics(daily_max_sharpe_returns) print("\nMinimum Volatility Portfolio Statistics:") calculate_statistics(daily_min_volatility_returns) # Cumulative Returns Plot cumulative_max_sharpe_returns = (1 + daily_max_sharpe_returns).cumprod() cumulative_min_volatility_returns = (1 + daily_min_volatility_returns).cumprod() plt.figure(figsize=(14, 7)) plt.plot(cumulative_max_sharpe_returns.index, cumulative_max_sharpe_returns, label="Max Sharpe Portfolio") plt.plot(cumulative_min_volatility_returns.index, cumulative_min_volatility_returns, label="Min Volatility Portfolio") plt.xlabel('Date') plt.ylabel('Cumulative Returns') plt.title('5-Year Cumulative Returns') plt.legend() plt.show() print(min_volatility_weights) print(max_sharpe_weights)

# Download stock data tickers = ['CELH', 'PWR', 'FRHC', 'RWWI', 'KLAC'] end_date = datetime.datetime.now() start_date = end_date - datetime.timedelta(days=5*365) prices = yf.download(tickers, start=start_date, end=end_date)['Adj Close'] # Calculate expected returns and sample covariance mu = expected_returns.mean_historical_return(prices) S = risk_models.sample_cov(prices) # Black-Litterman model market_caps = {"CELH": 15404388417.18, "PWR": 30359653682.75, "FRHC": 5684924310.39, "RWWI": 805874400, "KLAC": 70201656396.78} viewdict = {"CELH": 100, "KLAC": 0} market_prior = black_litterman.market_implied_prior_returns(market_caps, risk_aversion=1000.0, risk_free_rate=0.01, cov_matrix=S) bl = BlackLittermanModel(S, pi=market_prior, absolute_views=viewdict) ret_bl, S_bl = bl.bl_returns(), bl.bl_cov() # Efficient Frontier with L2 Regularization ef = EfficientFrontier(ret_bl, S_bl, weight_bounds=(0, 1)) ef.add_objective(objective_functions.L2_reg, gamma=0.1) # gamma is the tuning parameter for L2 regularization ef.max_sharpe() weights = ef.clean_weights() print(weights) # Calculate and show the portfolio performance ef.portfolio_performance(verbose=True) # Plotting the portfolio pd.Series(weights).plot.pie(autopct="%.2f%%") plt.show()