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Backtesting Brilliance: Navigating the Investment Labyrinth with Modern Portfolio Theory
Backtesting Brilliance: Navigating the Investment Labyrinth with Modern Portfolio Theory
Diving Deep into Investment Simulations Powered by Python.
Summary
Summary
Inspired by my father's request to look over his retirement funds, I developed a tool aimed at simplifying investment decisions while providing factual insights. My father's account, managed by an asset management firm, was incurring high fees and underperforming compared to basic stock market benchmarks. This observation, combined with my finance background, led me to build a tool that does a historical backtest of stock returns with an aim to better help families understand their investment opportunities.
Inspired by my father's request to look over his retirement funds, I developed a tool aimed at simplifying investment decisions while providing factual insights. My father's account, managed by an asset management firm, was incurring high fees and underperforming compared to basic stock market benchmarks. This observation, combined with my finance background, led me to build a tool that does a historical backtest of stock returns with an aim to better help families understand their investment opportunities.
The Framework: The Modern Portfolio Theory (MPT), by Nobel Prize Winner Harry Markowitz, led the way for simulating and selecting the optimal portfolio. The use of the Efficient Frontier helped to output the highest return given the user’s specified risk, as well as clearly show the portfolio’s performance to other benchmarks.
The Framework: The Modern Portfolio Theory (MPT), by Nobel Prize Winner Harry Markowitz, led the way for simulating and selecting the optimal portfolio. The use of the Efficient Frontier helped to output the highest return given the user’s specified risk, as well as clearly show the portfolio’s performance to other benchmarks.
The Tool's Design: Prioritizing ease-of-use, the tool requires only 4-6 user inputs (age, risk tolerance, duration, initial investment, recurring investment) and automates complex calculations.The Python-driven backend simulates tens of thousands of portfolios, assesses their standard deviation, and then leverages the Efficient Frontier to pinpoint optimal portfolios based on user preferences. The user-friendly interface, built using Streamlit, is divided into input and output sections, presenting data through various interactive charts and visuals.
The Tool's Design: Prioritizing ease-of-use, the tool requires only 4-6 user inputs (age, risk tolerance, duration, initial investment, recurring investment) and automates complex calculations.The Python-driven backend simulates tens of thousands of portfolios, assesses their standard deviation, and then leverages the Efficient Frontier to pinpoint optimal portfolios based on user preferences. The user-friendly interface, built using Streamlit, is divided into input and output sections, presenting data through various interactive charts and visuals.
Outputs: The tool offers a comprehensive view of the projected portfolio's performance against popular investment benchmarks. Visuals such as line charts depict potential growth, while pie charts break down portfolio allocation by asset class. To elevate the user experience, I integrated a feature to generate a downloadable PDF report, including a "Stock Shopping List" for actionable insights.
Outputs: The tool offers a comprehensive view of the projected portfolio's performance against popular investment benchmarks. Visuals such as line charts depict potential growth, while pie charts break down portfolio allocation by asset class. To elevate the user experience, I integrated a feature to generate a downloadable PDF report, including a "Stock Shopping List" for actionable insights.
Below is an embedded version of my Portfolio Backtest tool. For best performace open the tool in a new tab with the "Fullscreen" button in the bottom of the embed. Once you press "Calculate Results" it may take a minute to output your results.
Below is an embedded version of my Portfolio Backtest tool. For best performace open the tool in a new tab with the "Fullscreen" button in the bottom of the embed. Once you press "Calculate Results" it may take a minute to output your results.
The video walkthrough of this project can be found here: Youtube
The Full Project Analysis can be found here: Full Analysis
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