AI Portfolio Architect - FAQs

What is Portfolio Architect?

The Portfolio Architect is an AI-driven system integrated with the Macroaxis Portfolio Optimization Engine. This advanced feature automates asset selection and portfolio construction, aligning them with your specific investment objectives. By leveraging machine learning, statistical analysis, and predictive modeling, the system optimizes for both risk-adjusted returns and diversification. This enables us to offer tailored investment strategies that adhere to the principles of modern portfolio theory, a framework for assembling a portfolio of assets such that the expected return is maximized for a given level of risk.

What are the main benefits of Macroaxis Portfolio Architect?

The Macroaxis Portfolio Architect provides multiple benefits to our users by leveraging cutting-edge machine learning algorithms, statistical analysis, and predictive modeling to automate the process of asset selection and portfolio construction, saving time and reducing human error for both individual and institutional investors. Other benefits include:

Risk-Adjusted Returns

The platform is designed to optimize for risk-adjusted returns, providing a more nuanced and effective investment strategy. Traditional investment platforms often focus solely on generating high returns. In contrast, Portfolio Architect aims for risk-adjusted returns, meaning it seeks to maximize gains while taking into consideration the level of risk involved. This provides a more balanced and sustainable approach to investing.


The system considers diversification as a key factor when constructing portfolios, thereby reducing potential risk. Diversification is a core tenet of modern portfolio theory, and this platform automates the process of diversifying your investments across different asset classes, sectors, or geographies. This reduces the portfolio's susceptibility to market volatility and specific risks associated with individual investments.

Alignment with Modern Portfolio Theory

The algorithms employed by the Portfolio Architect platform are grounded in the principles of modern portfolio theory (MPT). This theory is widely respected and used by financial professionals around the world, adding a layer of trust and credibility to the system's recommendations.

Financial Metrics

Beyond simply optimizing for returns, the platform also considers a variety of other important financial metrics like volatility, Sharpe ratio, and liquidity. This more holistic approach provides you with a comprehensive view of your portfolio's performance, strengths, and weaknesses.


The technology-driven nature of the platform makes it highly scalable, able to serve a wide range of investors from individuals to large institutions. Whether you're an individual investor looking to manage a small nest egg or an institutional investor with a multi-million dollar portfolio, the platform is designed to scale with your needs. Its technology-driven approach ensures that it can handle portfolios of any size with equal efficiency.

User Experience

Despite its sophisticated technology, the platform is designed to be intuitive and easy to use, making it accessible for investors of all levels. With the power of machine learning and predictive modeling, the platform generates investment strategies that are highly customized to an investor's specific financial objectives, risk tolerance, and investment horizon. This personalization increases the likelihood of meeting or exceeding your investment goals.

Do I need to acquire a separate license to use Portfolio Architect?

No, you do not need to acquire a separate license to use Portfolio Architect during its beta period. However, please note that our pricing structure will eventually change to better scale to investors' individual needs. We recommend subscribing to our Gold package to ensure that you receive all updates and new features as they become available. However, if you're working with a limited budget, you can still access Portfolio Architect with our Silver package during the beta period.

What Are The Key Steps In The Portfolio Optimization Process Where Input Is Specified Through Readable Prompts?

Portfolio optimization is a multi-objective problem rooted in the concept of Mean-Variance Optimization, a theory first introduced by Harry Markowitz in the early 1950s. Published in the Journal of Finance in 1952, Markowitz's groundbreaking work emphasized the importance of asset diversification. When assets in a portfolio move in unison and possess similar risk profiles, the portfolio's volatility equates to the weighted average of the individual asset volatilities. As such, the primary objective of portfolio optimization is to select assets that complement each other in terms of their volatility and market behavior.

With the advancement of machine learning and AI technologies, portfolio optimization has evolved significantly. We can now automate the process of stock screening and asset selection to construct portfolios tailored to specific investment goals and risk tolerances. This is particularly beneficial for novice investors who may not have the experience or knowledge to make these complex decisions manually.

When input is specified through readable prompts, the portfolio optimization process typically involves the following key steps:

1. Initial Asset Screening

This step automates the initial narrowing down of potential investments buy understanding the investor's specified criteria that stocks must meet to be considered for the portfolio. This can include factors like P/E ratios, sector, market cap, dividend yields, etc.

2. Potential Position Selection

After the screening process, the system employs machine learning algorithms to further refine the list of assets that align with your specific investment objectives and risk profile. It is also done by interpreting the provide prompt to locate investor's specified optimization preferences, such as budget constraints, number of assets, or risk level.

3. Running the Optimization Algorithms

Executing the optimization algorithms to identify the optimal weightings for each asset in the portfolio. By utilizeing Mean-Variance Optimization against the selected assets, the system constructs a portfolio designed to maximize returns for a given level of risk. The portfolio will reflect the input provided through readable prompts, ensuring it aligns closely with investor's financial goals and risk tolerance.

What Types of Prompts Should Investors Use for Optimal Results?

The clarity and specificity of your input prompts play a critical role in the effectiveness of the portfolio optimization process. When you provide vague or generic criteria, the algorithm may produce a less focused, suboptimal portfolio that does not truly align with your investment objectives or risk tolerance. However, by offering clear, detailed prompts that specify factors such as desired returns, risk level, sector focus, or even ESG considerations, you enable the AI system to hone in on assets that best meet your goals. Precise screening criteria, such as P/E ratios, dividend yields, or market capitalizations, can further refine the asset selection process. A specific prompt not only enhances the algorithm's ability to construct a tailored portfolio but also minimizes the potential for misunderstanding or errors, thereby increasing the likelihood of meeting or exceeding your investment goals.

We are still in the process of determining best practices and building a catalog of sample prompts that can help our users define their investment objectives in one or two simple paragraphs. But at this point, when using Portfolio Architect, it's crucial to leverage its machine learning capabilities to your advantage by being explicit and precise in your input criteria. Below are some of the possible inputs users can provide:

Generic Prompt

Generic user prompt, such as a simple list of company names or stock tickers, will be interpreted as direct suggestions for possible positions. Default assumptions will be applied to determine the optimal portfolio. This typically implies an average attitude towards risk, a desired portfolio with up to 20 positions, and a budget of $10,000. The portfolio will be optimized to minimize risk for maximum return, based on a default risk aversion setting.

I am interested in Tesla, IBM, GM, Apple, Google, CRM, and Microsoft.
Healthcare stocks please
I want to focus on renewable energy and oil space.

Abstract Prompt

Abstract user prompts are usually brief and might describe a specific sector or industry. In this case, machine learning algorithms will determine the user's intent and either proceed with creating a portfolio based on "Generic Input" rules or provide additional questions to clarify the user's goals.

I want to invest in tech stocks.
Artificial Intelligence
I'm interested in driverless cars.

Specific Intent Prompt

A specific intent prompt is a detailed user input that clearly specifies both the filtering criteria for stock selection and portfolio optimization preferences, such as budget and risk level.

I want a technology-focused portfolio with stocks having a P/E ratio less than 30. My budget is $20,000, and I'm looking for moderate risk.
Create a portfolio focused on technology stocks that pay dividends. My budget is $10,000 and I love taking risk.
I want to invest in companies from the healthcare sector with a market cap over $10 billion. I have a low-risk tolerance.

Please note, it is important to utilize standard financial terminology to define your risk tolerance, budget, and types of equities you're interested in. This not only ensures that the AI algorithms accurately interpret your investment objectives but also refines the asset selection and portfolio construction process. Being clear about filtering criteria such as market sectors, desired returns, or the exclusion of certain assets allows the system to optimize for risk-adjusted returns and diversification more effectively. In doing so, you'll receive a tailored investment strategy that aligns closely with your specific financial goals and risk profile.