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Stock Recommendation Engine V1

Description

This Stock Recommendation Engine is a Python-based application that provides data-driven investment recommendations for both short-term and long-term horizons. By leveraging machine learning techniques and incorporating various financial and non-financial factors, it offers a comprehensive analysis of stock performance and potential.

Before Using

  • Make sure to replace ALPHA_VANTAGE_API_KEY, and NEWS_API_KEY with your api keys to ensure the program works---> analysis.py
  • In analysis.py, for more GPI countries you will need to update it manually

Key Features

  • Short-term recommendations (1 day and 1 month) based on technical analysis and machine learning predictions
  • Long-term recommendations (1 year and 10 years) considering fundamental analysis, historical performance, and additional factors
  • Integration of multiple data sources:
    • Stock price and volume data
    • Company fundamentals
    • News sentiment analysis
    • ESG (Environmental, Social, and Governance) scores
    • Economic indicators
    • Geopolitical risk assessment
  • Custom scoring system that balances various aspects of stock performance and market conditions

Technologies Used

  • Python
  • Pandas for data manipulation
  • Scikit-learn for machine learning models
  • yfinance for stock data retrieval
  • NewsAPI for sentiment analysis
  • Alpha Vantage API for additional financial data
  • World Bank API for economic indicators

How It Works

  1. Fetches historical stock data and calculates technical indicators
  2. Trains a machine learning model for short-term predictions
  3. Retrieves fundamental company data and calculates financial ratios
  4. Incorporates news sentiment, ESG scores, and macroeconomic factors
  5. Applies a custom scoring algorithm to rank stocks
  6. Generates top 5 recommendations for different time horizons

Disclaimer

This tool is for educational and research purposes only. It should not be considered as financial advice. Always conduct your own research and consult with a qualified financial advisor before making investment decisions.

Future Improvements

  • Incorporate more advanced machine learning models
  • Add backtesting functionality to validate strategy performance
  • Implement real-time data updates
  • Expand the range of analyzed stocks and markets
  • Better UI
  • Make it Less Pricy To Use