Financial-news-Stock-Analysis

Project Overview

This project is focused on analyzing a large corpus of financial news data to discover correlations between news sentiment and stock market movements.

Main Objectives

1. Perform sentiment analysis on financial news headlines.
2. Establish statistical correlations between news sentiment and stock price movements.
3. Provide actionable insights and investment strategies based on your analysis.

Folder Structure

├── .vscode/
│   └── settings.json  
├── app/
│   └── app.py     # streamlit app         
├── .github/
│   └── workflows/
│       └── unittests.yml      # GitHub Actions
├── .gitignore                 # directories to be ignored by git
├── requirements.txt           # contains dependencies for the project
├── README.md                  
├── src/
│   ├── __init__.py
│   
├── notebooks/
│   ├── __init__.py
│   ├── Sentiment_Analysis_EDA.ipynb  # Jupyter notebook for stock news EDA analysis
│   └── AAPL_EDA.ipynb,AMZN_EDA.ipynb,GOOG_EDA.ipynb,META_EDA.ipynb,MSFT_EDA.ipynb,NVDA_EDA.ipynb, TSLA_EDA ipynb,  correlation_analysis_notebook.ipynb        #  notebook files for financial analysis of each stock data 
├── tests/
└── scripts/
    ├── __init__.py
    ├── utils.py # Script for financial news analysis 
    ├── financial_analyzer.py # script for the stock data analysis    
    ├── sentiment_correlation_analysis.py # script for financial news and stock price integration analysis
    └── README.md             # Documentation for the scripts directory

Setup

1. Clone the repository:

   git clone https://github.com/OL-YAD/Financial-news-Stock-Analysis.git
   cd Financial-news-Stock-Analysis
   

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
   

3. Install the required packages:

   pip install -r requirements.txt
   

Running the Streamlit App

To run the Streamlit app locally:

1. Ensure you're in the project directory and your virtual environment is activated (if you're using one).

2. Run the following command:

   streamlit run app.py
   

Data Sources

• Historical stock data: Retrieved from Yahoo Finance
• News sentiment data: Collected from financial news sources

Contact

For any questions or feedback, please open an issue on this GitHub repository.