Financial Data Analysis and AI (Google Gemini API) Advisory Project

Overview

This project combines financial data processing and AI-powered investment advisory to assist users in making informed investment decisions. By leveraging APIs, data analysis, and machine learning prompts, the system offers both data aggregation and investment strategies based on company financial information.

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Key Features

1. Financial Data Processing

• Retrieves financial data from public APIs for listed companies.
• Merges balance sheets and income statement data for comprehensive analysis.
• Filters and organizes data for user-selected companies based on critical financial metrics.

2. AI-Powered Investment Analysis

• Utilizes Google Generative AI to generate insightful investment advice.
• Provides strategies for:
  • Value Investing: Identifying undervalued stocks with potential for long-term gains.
  • Momentum Trading: Highlighting companies with continuous revenue or profit growth.
  • Low Volatility Stock Selection: Recommending stable stocks for low-risk investors.

3. Prompt Optimization Tool

• Enhances prompt engineering for better AI responses.
• Incorporates user feedback to refine and improve prompts.

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Installation and Setup

1. Clone the Repository

$ git clone <repo-link>
$ cd <project-directory>

2. Install Dependencies

Ensure you have the required packages installed:

$ pip install pandas requests python-dotenv google-generativeai

3. Environment Configuration

Create a .env file in the project directory and add your API key:

API_KEY=<Your_Google_GenerativeAI_API_Key>

4. Run the Application

$ python main.py

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Usage Instructions

1. Data Input:

   • The system will prompt you to input the number of companies and their IDs to look up.
   • It fetches financial data and merges them for analysis.

2. Generating AI Reports:

   • The AI advisor analyzes the financial data and provides actionable investment strategies.

3. Prompt Optimization:

   • Provides feedback-based improvements for AI prompts.

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Code Structure

• dataProcessing.py: Handles data fetching, filtering, and merging.
• promptNgenAI.py: Manages AI interactions and prompt engineering.
• main.py: Integrates the workflow and coordinates execution.

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Example Usage

enter the number you'd like to look up: 2
enter the company id you'd like to look up: 1234
enter the company id you'd like to look up: 5678

The system fetches the data and generates an AI investment report:

[Investment Recommendations and Strategies Generated by AI]

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Future Improvements

• Expand visualization capabilities.
• Integrate additional financial data sources.
• Improve AI models for better financial insights.
• Adding backtesting module for more precising financial analysis and insights.

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Contributors

• [Eva Liu] - Project Developer

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Acknowledgments

• Public APIs for financial data
• Google Generative AI (Google Gemini) for content generation