Flask Financial Analysis API

📌 Project Overview

This is a Flask-based Financial Analysis API that allows users to:

• Extract stock ticker symbols from natural language queries.
• Fetch financial data for a stock using Yahoo Finance.
• Analyze stock data using AI-driven financial analysis.

🏗 Project Structure

/flask_financial_api
│── /app
│   │── /api
│   │   │── __init__.py
│   │   │── routes.py
│   │── /models
│   │   │── __init__.py
│   │   │── stock.py
│   │── /services
│   │   │── __init__.py
│   │   │── symbol_extractor.py
│   │   │── data_fetcher.py
│   │   │── stock_analysis.py
│   │── /utils
│   │   │── __init__.py
│   │   │── logger.py
│   │── /config
│   │   │── __init__.py
│   │   │── settings.py
│   │── /middleware
│   │   │── __init__.py
│   │   │── error_handler.py
│   │── __init__.py
│   │── extensions.py
│── /migrations
│── /tests
│   │── __init__.py
│   │── test_routes.py
│── /instance
│── .env
│── requirements.txt
│── gunicorn_config.py
│── wsgi.py
│── README.md

🚀 Getting Started

🔹 Prerequisites

• Python 3.12+
• Anaconda (if using Conda environments)

🔹 Setup

1. Clone the Repository

git clone https://github.com/yourusername/flask_financial_api.git
cd flask_financial_api

2. Create a Virtual Environment

conda create -n financial_api_env python=3.12
conda activate financial_api_env

Or using venv:

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

3. Install Dependencies

pip install -r requirements.txt

4. Set Up Database

flask db init
flask db migrate -m "Initial migration"
flask db upgrade

5. Run the Server

flask run

By default, the API runs at: http://127.0.0.1:5000

For public access:

flask run --host=0.0.0.0 --port=5000

📡 API Endpoints

1️⃣ Extract Stock Symbol

POST /api/extract-symbol

Request:

{
    "query": "symbol for microsoft"
}

Response:

{
  "symbol":"The stock ticker symbol for Microsoft is:\n\nMSFT"
}

2️⃣ Fetch Financial Data

POST /api/fetch-financials

Request:

{
    "ticker": "AAPL"
}

Response:

{
 "file":"MSFT.json",
  "message":"Financial data successfully saved at MSFT.json"   
}

3️⃣ Analyze Stock Data

POST /api/analyze-stock

Request:

{
    "file_path": "AAPL.json",
    "query": "Analyze this stock"
}

Response:

{
    "analysis": "The stock shows strong profitability....... I would recommend that the trader hold the stock..."

}

🧪 Running Tests

pytest tests/

Expected output:

================================================= 3 passed in 13.74s ==================================================

🚀 Deployment

1️⃣ Deploy with Waitress (Windows Production Server)

! you can use gunicorn for linux

pip install waitress
waitress-serve --listen=0.0.0.0:5000 wsgi:app

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📌 License

This project is open-source and available under the MIT License.

❤️ Contributing

• Fork the repository
• Create a new branch (git checkout -b feature-branch)
• Commit your changes (git commit -m 'Add new feature')
• Push to the branch (git push origin feature-branch)
• Create a pull request 🚀