MarkPhamm/Alcon-Financial-Assistant

Alcon Financial Analysis: An interactive dashboard showcasing dynamic financial metrics, KPIs, and competitor comparisons. Powered by a RAG-based chatbot for real-time insights from financial reports and industry data, enabling data-driven decisions and strategic market positioning for Alcon.

Alcon Financial Competitors Analysis App

Access our Streamlit app here

Overview

This Streamlit application provides a comprehensive financial analysis tool for Alcon and its competitors. It offers various features including financial data visualization, a chatbot for queries, and configuration management.

Features

1. Financial Data Visualization

   • Income Statement Analysis (Annual and Quarterly)
   • Balance Sheet Analysis (Annual and Quarterly)
   • Cash Flow Analysis (Annual and Quarterly)
   • Interactive charts using Plotly and Pygwalker

2. RAG-powered Chatbot

   • AI-powered assistant for financial queries

   • Utilizes OpenAI's GPT model and Chroma vector database for retrieval-augmented generation

   • Provides context-aware responses based on up-to-date financial data

   • RAG Architecture

3. Configuration Management

   • Add or remove ticker symbols for analysis

     

   • Run ETL (Extract, Transform, Load) pipeline

     

   • Populate Vector Database for enhanced chatbot performance

     

Installation

1. Clone the repository:

   git clone [repository-url]
   

2. Navigate to the project directory:

   cd [project-directory]
   

3. Create a virtual environment:

   python -m venv .venv
   

4. Activate the virtual environment:

   • On Windows:

     .venv\Scripts\activate
     

   • On macOS and Linux:

     source .venv/bin/activate
     

5. Install required dependencies:

   pip install -r requirements.txt
   

6. Set up environment variables:

   • Create a .env file in the root directory
   • Add your OpenAI API key:

     OPENAI_API_KEY=your_api_key_here
     

Usage

1. Run the Streamlit app:

   streamlit run app.py
   

2. Navigate through different sections using the sidebar:

   • Income Statement
   • Balance Sheet
   • Cash Flow
   • Chatbot
   • Configs

3. In the visualization sections, you can:

   • Select specific companies for comparison
   • Choose between annual and quarterly data
   • Create custom charts using Plotly or Pygwalker

4. Use the Chatbot for financial queries related to the loaded data

5. In the Configs section, you can:

   • Add or remove ticker symbols
   • Run the ETL pipeline to update data
   • Populate the vector database for improved chatbot responses

Data Sources

The application uses financial data stored in CSV files:

• annually_income_statement.csv
• annually_balance_sheet.csv
• annually_cash_flow.csv
• quarterly_income_statement.csv
• quarterly_balance_sheet.csv
• quarterly_cash_flow.csv

Dependencies

• Streamlit
• OpenAI
• LangChain
• Pandas
• Plotly
• Pygwalker
• Chroma

Customization

You can modify the config.py file to change the list of tracked tickers and update the color theme for visualizations.

Note

Ensure that you have the necessary permissions and comply with the terms of service for all data sources and APIs used in this application.