Extending Your Financial Analyst Agent Module

Welcome to our extensions module!

We'll explore how to enhance our Financial Analyst Agent with additional capabilities that make it even more powerful and versatile. Think of this as giving your financial analyst new tools to provide deeper, more insightful analysis.

1. Accessing Different Types of CrewAI Outputs

CrewAI provides multiple ways to access the outputs from your agent crew. This extension shows how to access different output formats and what they're best used for.

Current Implementation

Our Financial Analyst currently uses:

Expanded Output Options

You can also access the output of your agent in a few different ways:

• Raw Output

• JSON Output

For structured data:

• Pydantic Models

For type-safe structured data:

• Individual Task Outputs

Access results from specific tasks:

• Complete Token Usage

Detailed token metrics:

When to Use Each Type

• Raw Output: Best for simple text responses
• JSON: When you need structured data for further processing
• Pydantic: When you need type safety and validation
• Task Outputs: When you need to track individual agent contributions
• Token Usage: For monitoring API usage and optimization

Note

Remember that not all output types may be available depending on how your crew and tasks are configured. Always check for existence before accessing optional outputs.

2. Implementing Additional Data Sources

You can expand your analysis by integrating additional financial data sources from Alpha Vantage, and SEC Edgar.

Both data sources give you access to a wide range of financial data, including historical price data, fundamental financial metrics, and SEC (Securities and Exchange Commission) filings.

To integrate these data sources, you'll need to update your get_financial_data method in the run.py file to include the new sources in an appropriate way.

3. Financial Data Visualization

You can also extend the Financial Analyst Agent by adding visualization capabilities. This extension allows for rich visual analysis of financial data while keeping the core functionality focused on analysis and reporting.

Implementation Steps

i. Add visualization field to InputSchema:

ii. Update the input parameters in the run.py file:

iii. You can then create a visualization function using packages like Plotly or Matplotlib. and then integrate it within the analyze method.

Troubleshooting Guide: Common Issues and Solutions

Directory Structure Errors

Error:

Cause: Incorrect directory structure after cloning template Solution: Ensure correct directory structure:

Module Import Errors

Error:

Cause: Python path not recognizing local module Solution: Update import statement to use full module path:

Config File Loading Errors

Error:

Cause: Incorrect path to config files Solution: Use absolute path or correct relative path:

CrewAI Output Handling

Error:

Cause: Incorrect access of CrewAI output object Solution: Access raw output directly:

Pydantic Warnings

Warning:

Cause: Using deprecated Pydantic V1 features Solution: Update to Pydantic V2 syntax or ignore warning (non-critical)

YFinance Deprecation Warnings

Warning:

Cause: Using deprecated yfinance methods Solution: Use income_stmt instead of earnings:

Next Steps and Resources

• Explore the Plotly Documentation for more visualization options
• Check out Alpha Vantage API for additional financial data
• Review CrewAI Advanced Features for more agent capabilities

Remember: The best extensions are those that add real value to your analysis. Focus on implementing features that enhance your agent's ability to provide meaningful insights.