Portfolio Allocatgion 1_N Factor Investing

1_n factor strategy.py

import yfinance as yf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Define a list of stock tickers for the portfolio
tickers = ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'TSLA']

# Set the date range for historical data
start_date = '2010-01-01'
end_date = '2020-12-31'

# Download historical stock price data from Yahoo Finance
data = yf.download(tickers, start=start_date, end=end_date)['Adj Close']

# Calculate daily returns
returns = data.pct_change().dropna()

# Define the number of assets in the portfolio
num_assets = len(tickers)

# Define initial portfolio weights (e.g., equal weights)
initial_weights = np.array([1.0 / num_assets] * num_assets)

# Define initial investment amount
initial_investment = 1000000  # $1,000,000

# Create an empty DataFrame to store portfolio values
portfolio_values = pd.DataFrame(index=returns.index, columns=['Portfolio Value'])

# Initialize portfolio value with the initial investment
portfolio_values.iloc[0] = initial_investment

# Simulate portfolio construction and rebalancing
for i in range(1, len(returns)):
    # Calculate portfolio values at each time step
    portfolio_values.iloc[i] = portfolio_values.iloc[i - 1] * (1 + returns.iloc[i])

# Calculate portfolio returns
portfolio_returns = (portfolio_values['Portfolio Value'] / initial_investment) - 1

# Calculate portfolio volatility (standard deviation of returns)
portfolio_volatility = portfolio_returns.std()

# Calculate the Sharpe ratio (assuming a risk-free rate of 0%)
risk_free_rate = 0.0
sharpe_ratio = (portfolio_returns.mean() - risk_free_rate) / portfolio_volatility

# Print portfolio performance metrics
print("Portfolio Performance Metrics:")
print("Cumulative Returns:", portfolio_returns[-1])
print("Annualized Portfolio Return:", portfolio_returns.mean() * 252)  # Assuming 252 trading days per year
print("Annualized Portfolio Volatility (Risk):", portfolio_volatility * np.sqrt(252))
print("Sharpe Ratio:", sharpe_ratio)

# Plot portfolio value over time
plt.figure(figsize=(12, 6))
plt.plot(portfolio_values.index, portfolio_values['Portfolio Value'], label='Portfolio Value')
plt.xlabel('Date')
plt.ylabel('Portfolio Value')
plt.title('Portfolio Value Over Time')
plt.legend()
plt.grid(True)
plt.show()