Stock Market Analysis

Overview

An Open source python server application for distributing public financial data and compute results on a local network, minimizing external requests, and conserving external API usage.

Features

• Stock price scraping and storage using SQLite database
• Stock News gathering and sentiment analysis
• Local Flask server with CORS support for multi-process communications
• RESTful API endpoints for stock predictions and data access
• In-memory caching system for optimal performance
• LSTM-based price prediction using historical data
• Technical indicator calculations
• Support for all S&P 500 companies

Directory Structure

Data

• database/ - Stores SQLite databases for:
  • Historical price data
  • Model predictions
  • Stock metadata
• raw data/ - Contains raw data files before processing

Documentation

• Contains team breakdowns, tasks and other relevant documentation
• Current Project Scope

Src

• notebooks/ - Jupyter notebooks for analysis and model development
• scripts/ - Core Python scripts including:
  • app.py - Main Flask application
  • init_all_data.py - Data initialization
  • price_prediction.py - LSTM model implementation
  • get_stock_data.py - Data retrieval utilities

Setup

Prerequisites

• Python 3.10+
• pip (Python package manager)
• Docker (optional, for containerized deployment)

Installation

Local Development

1. Create and enable a Python virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install requirements:

pip install -r requirements.txt

3. Create a .env file in the root directory with the following variables:

NEWS_API_ORG_KEY = "API_KEY"    # Your News API key from newsapi.org
TESTING_MODE = "True"           # Enable testing mode with sample data and limited model training
                               # Set to "False" for production mode with live data

4. Initialize the data and train models:

cd Src/scripts
python init_all_data.py
python app.py

Docker Deployment

1. Build and run using Docker Compose:

docker compose up --build

The application will be available at http://localhost:5000

REST API Usage

Endpoints

Get Stock Information

GET /get_data/{ticker}

Response includes:

• Today's predicted close price
• Historical price data
• Predicted price movement score
• Sentiment-adjusted close price

Example Response:

{
    "ticker": "AAPL",
    "todays_predicted_close_price": 178.45,
    "historical_price_data": [...],
    "predicted_price_movement_score": 0,
    "adjusted_close_price_based_on_sentiment": 180.25
}

Get Available Tickers

GET /get_available_tickers

Returns a list of all available S&P 500 tickers in the database.

Implementation Details

Data Storage

• Uses SQLite databases for storing historical prices and predictions
• Implements an in-memory cache for frequently accessed data
• Thread-safe data access using multiprocessing locks

Model Training

• Automated model training pipeline
• Stores predictions in a dedicated SQLite database
• Updates predictions periodically with new market data

Security

• Uses Flask's secret key for session management
• Implements CORS for controlled API access
• Runs as non-privileged user in Docker container

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

Disclaimer

This application is for educational and research purposes only. Trading stocks carries risk, and predictions should not be used as the sole basis for investment decisions.

License

MIT License