📈 Agent #3: Advanced Prediction Agent

A Financial Forecasting AI driven by OpenAI & LangChain.

[Image of financial forecasting chart]

📌 Overview

The Advanced Prediction Agent is a specialized tool for analyzing time-series financial data. Unlike traditional statistical models that only look at numbers, this agent uses Large Language Models (LLMs) to interpret trends, identify seasonality, and generate qualitative strategic advice alongside quantitative forecasts.

In this lab, we simulate 18 months of revenue data and use OpenAI (GPT-3.5/4) to predict future performance and visualize the trajectory.

🚀 Features

• Time-Series Generation: Creates realistic mock revenue data with growth trends and noise.
• AI Forecasting: Uses GPT to analyze 18-month historical data and predict the next quarter.
• Trend Analysis: Identifies growth patterns and potential seasonality automatically.
• Visual Dashboard: Renders interactive matplotlib line charts within a Streamlit interface.

🛠️ Tech Stack

• Frontend: Streamlit
• AI Logic: LangChain (Prompt Templates & Chains)
• Model: OpenAI GPT-3.5 Turbo (or GPT-4)
• Data Science: Pandas & NumPy
• Visualization: Matplotlib
• Language: Python 3.10+

📂 Project Structure

prediction_agent/
├── app.py              # Main Streamlit Dashboard
├── forecast_agent.py   # AI Logic (LangChain integration)
├── forecast_prompt.py  # Prompt Engineering for Financial Analyst Persona
├── data_loader.py      # Data Generator (Mock Revenue & Seasonality)
├── requirements.txt    # Project Dependencies
└── README.md           # Documentation

⚙️ Setup & Installation
1. Clone or Create the Repository
Bash
mkdir prediction_agent
cd prediction_agent

2. Set Up Virtual Environment
It is recommended to use a virtual environment to manage dependencies.

Bash
python -m venv venv
# Windows
venv\Scripts\activate
# Mac/Linux
source venv/bin/activate

3. Install Dependencies
Create a requirements.txt file (or run the command below directly):

Bash
pip install openai langchain pandas matplotlib streamlit

4. Configure API Keys
You need an OpenAI API key to run the analysis.

Option A (Environment Variable):

Bash
export OPENAI_API_KEY="sk-proj-..."
Option B (.env file):
Create a .env file and add: OPENAI_API_KEY=sk-proj-... (requires python-dotenv).

5. Run the Application
Bash
streamlit run app.py