i want to make an application using the mindstudio ai platform. i want workflow suggestions and accuracy for this app: investment vision 

1. Data Acquisition (Prompt 1):

    The first prompt calculates the user's disposable income. It takes the user's monthly income and expenses as input
    The output is the disposable income, which is crucial for the subsequent prompts.

2. Portfolio Allocation (Prompt 2):

    This prompt suggests an investment allocation strategy.
    It receives the disposable income (output from Prompt 1) and the user's investment vision (e.g., "Crypto Bro") as input.
    The output is a recommended portfolio allocation, specifying percentages and dollar amounts for different investment categories.

3. Specific Investment Recommendations (Prompt 3):

    The final prompt provides specific investment vehicle recommendations.
    It uses the recommended portfolio allocation (output from Prompt 2) as input.
    The output is a list of specific investments (e.g., Bitcoin, Tesla stock), including brief explanations, suggested allocations, and potentially other relevant details.

Chaining and Implementation:

The application uses a chain of prompts. The output of one prompt serves as input for the next. This is implemented in Node.js using the Express framework. The developer uses the openai library to interact with 

OpenAI's API. The developer mentions using different GPT models (GPT-4, possibly GPT-3 variants) based on an "intelligence level" setting, suggesting future expansion of model selection.

Overall Functionality:

The app guides users through a process to:

    Input their financial information (income and expenses).
    Define their investment vision or risk tolerance.
    Receive a personalized investment plan, including:
        Disposable income calculation.
        Recommended portfolio allocation across investment categories.
        Specific investment recommendations with explanations and suggested allocations.

The investment vision application, as described, has a good foundation but could be significantly improved in several areas to enhance accuracy, user experience, and overall appeal. Here are some suggestions:

I. Enhancing Accuracy and Robustness:

    More Sophisticated Risk Assessment: The current risk assessment seems rudimentary ("Crypto Bro"). Implement a more robust system using questionnaires, possibly incorporating established risk tolerance 

models (e.g., questionnaire-based assessments, analyzing investment history if available). This would allow for more nuanced and accurate investment recommendations.

    Diversification Beyond Simple Categories: The investment categories (equities, funds, real estate, etc.) are broad. Refine these categories and allow for more granular diversification within each category (e.g., 

different types of equities, specific sectors, bond types).

    Incorporate Financial Data: Integrate real-time or near real-time market data to make recommendations more contextually relevant. The current system seems static; dynamic data would significantly improve the 

accuracy and timeliness of suggestions.

    Advanced Portfolio Optimization: Instead of simple percentage allocations, use portfolio optimization techniques (e.g., Modern Portfolio Theory, mean-variance optimization) to create portfolios that balance risk and 

return based on the user's risk profile and investment goals.

    Tax Implications: Consider incorporating tax implications into the recommendations. Different investment vehicles have different tax consequences, and a comprehensive plan should account for this.

    Goal Setting: Allow users to define specific financial goals (e.g., retirement savings, down payment on a house). This would enable the AI to tailor recommendations more effectively.

    Time Horizon: Incorporate the user's investment time horizon (short-term, long-term) into the decision-making process. Investment strategies vary significantly based on the time frame.