Stock Portfolio and S&P 500 Performance Simulation

This Python program utilizes Monte Carlo simulation with geometric Brownian motion to forecast the future performance of a user-defined stock portfolio and the S&P 500 index. The simulation calculates the daily close price percentage for each stock in the portfolio and the S&P 500, and then performs a Monte Carlo simulation to generate multiple possible future scenarios.

Prerequisites

Before running the program, make sure to have the required Python libraries installed. You can install them using the following:

pip install yfinance numpy matplotlib pandas

How to Use It

1. Import the necessary libraries:

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

2. Define the functions for fetching stock data, performing Monte Carlo simulation, and plotting the results.

# Function to get stock data and calculate daily close price percentage
def get_stock_data(ticker, start_date, end_date):
    #Implementation...

#Function for Monte Carlo simulation with geometric Brownian motion
def monte_carlo_simulation_gbm(data, num_simulations, forecast_period, days=252):
    #Implementation...

#Function to plot the average of Monte Carlo simulations
def plot_monte_carlo_average(data, simulations, label):
    #Implementation...

3. Define your stock portfolio and set the start and end dates for the simulation.

#Define portfolio as a dictionary with tickers and corresponding percentages
portfolio = {"AAPL": 0.4, "TSLA": 0.4, "NVDA": 0.2}

#Loop through each stock in the portfolio
daily_percentage_dict_portfolio = None
start_date_portfolio = "2022-01-01"
end_date_portfolio = "2023-10-01"

4. Fetch and process stock data for each stock in the portfolio.

for stock_ticker, weight in portfolio.items():
    #Implementation...

#Calculate the weighted average close prices across all stocks in the portfolio
daily_percentage_dict_portfolio['Average'] = daily_percentage_dict_portfolio.sum(axis=1)

5. Perform Monte Carlo simulation for the portfolio.

#Monte Carlo simulation with geometric Brownian motion for the portfolio
num_simulations_portfolio = 1000
forecast_period_portfolio = 66  #Approximately 3 months (assuming 22 business days per month)
portfolio_simulations = monte_carlo_simulation_gbm(daily_percentage_dict_portfolio['Average'], num_simulations_portfolio, forecast_period_portfolio)

6. Plot the results for the portfolio.

#Plot the average of Monte Carlo simulations for the portfolio
plot_monte_carlo_average(daily_percentage_dict_portfolio['Average'], portfolio_simulations, label="Portfolio")

7. Perform Monte Carlo simulation for the S&P 500.

#Get data for ^GSPC
sp500_data = yf.download('^GSPC', start=start_date_portfolio, end=end_date_portfolio)
sp500_close_prices_percentage = (sp500_data['Close'] / sp500_data['Open'].iloc[0]) * 100

#Monte Carlo simulation with geometric Brownian motion for ^GSPC
num_simulations_sp500 = 1000
forecast_period_sp500 = 66  #Approximately 3 months (assuming 22 business days per month)
sp500_simulations = monte_carlo_simulation_gbm(sp500_close_prices_percentage, num_simulations_sp500, forecast_period_sp500)

8. Plot the results for the S&P 500.

#Plot the average of Monte Carlo simulations for ^GSPC
plot_monte_carlo_average(sp500_close_prices_percentage, sp500_simulations, label="S&P 500")

9. Add labels and legend, then display the plot.

#Add labels and legend
plt.xlabel("Date")
plt.ylabel("Close Price Percentage")
plt.title("Stock Portfolio and S&P 500 Performance with Monte Carlo Simulation")
plt.legend()
plt.show()

Feel free to customize the stock portfolio, simulation parameters, and plotting options according to your needs.