Tactical Asset Allocation and Automatic Optimization

Highest return optimization algorithm.py

import numpy as np
import pandas as pd
import yfinance as yf
from scipy.optimize import minimize

# Download the ETF data from Yahoo Finance
etf_data = {}
for etf in ['SPY', 'MDY', 'EFA', 'EEM', 'TLT']:
    etf_data[etf] = yf.Ticker(etf).history(period="10y")

# Calculate the returns for each ETF
etf_returns = {}
for etf, data in etf_data.items():
    etf_returns[etf] = data['Adj Close'].pct_change().dropna()

# Define the optimization function
def optimize_weights(weights, returns):
    # Calculate the portfolio return
    portfolio_return = np.sum(returns.mean() * weights) * 252
    # Calculate the portfolio volatility
    portfolio_volatility = np.sqrt(np.dot(weights.T, np.dot(returns.cov() * 252, weights)))
    # Calculate the Sharpe ratio
    sharpe_ratio = portfolio_return / portfolio_volatility
    # Return the negative Sharpe ratio to minimize
    return -sharpe_ratio

# Set the optimization constraints
constraints = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1},
               {'type': 'ineq', 'fun': lambda x: x})

# Set the initial weights
initial_weights = np.array([0.2, 0.2, 0.2, 0.2, 0.2])

# Run the optimization
results = minimize(optimize_weights, initial_weights, args=(etf_returns), method='SLSQP', bounds=bounds, constraints=constraints)

# Print the optimized weights
print(results.x)