Monte Carlo Simulation for Portfolio Project

Introduction

This project is designed to help credit union members determine the expected range of returns for their portfolios over a given time horizon, using Monte Carlo simulation.

Data

The project uses historical stock prices for two assets - AGG (Bonds) and SPY (Stocks). The data for these assets was collected for the past 10 years.

Methodology

A Monte Carlo simulation was used to model the cumulative returns of the portfolio over a given time horizon. The simulation was run for both 10 and 30 years, using 10 samples each time. Note: ⭐️Only running this 10x to show cell is performing correctly, feel free to change the num_simulation⭐️

Results

The simulation results were used to calculate the lower and upper 95% confidence intervals for the cumulative returns of the portfolio over the 10 and 30-year horizons.

Conclusion

The results of the Monte Carlo simulation provide a range of expected returns for the credit union members' portfolios over the 10 and 30-year horizons. This information can be useful in helping the members make informed decisions about their investments. However, it is important to keep in mind that this is only a model and actual results may vary.

Libraries and Dependencies

from pathlib import Path

import os

import requests

import json

import pandas as pd

from dotenv import load_dotenv

import alpaca_trade_api as tradeapi

from MCForecastTools import MCSimulation

%matplotlib inline

Contributors

Demi Gao

Julio Rodriguez

Sources

Bootcamp Spot

Google

AskBCS Learning Assistant