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Risk Tolerance Reanalysis Using AI Agentic Workflow (AI-Agents-For-Reanalysis-of-Risk-Tolerance)

Overview

This project aims to reevaluate user risk tolerance based on provided JSON data. It involves analyzing relevant financial information and generating a new JSON file with updated risk tolerance values. The project employs Microsoft Autogen and Llama3.1 8b from the Groq API for a comprehensive analysis.

Table of Contents

Objective

The primary goal is to develop a system that:

  1. Reads user data from a JSON file.
  2. Analyzes financial information, investment goals, and demographic data.
  3. Generates updated risk tolerance values based on the analysis.

Technologies Used

  • Programming Language: Python
  • Language Model: Llama3.1 8b from Groq API
  • Agentic Framework: Microsoft Autogen
  • Data Format: JSON

Steps

Step 1: Read the JSON File

  • Create an agent to read and extract data from userForm.json, which includes user demographics, financial information, risk tolerance, and investment preferences.

Step 2: Create a Group of Agents for Reanalysis

  • Develop a group of AI agents to:
    • Reanalyze specific data points such as financial goals, investment strategy, and portfolio structure.
    • Use the analyzed information to reassess user risk tolerance based on:
      • Current income, investments, and debt levels.
      • Investment goals and preferences.
      • Existing tolerance levels and target values.

Step 3: Write a New JSON File

  • Each agent provides an updated analysis of factors affecting risk tolerance.
  • A final agent compiles the findings and writes a new JSON file containing updated risk tolerance values, reflecting changes in the user's financial situation or risk parameters.