📊 StockScout

StockScout is a stock analysis dashboard that combines technical indicators, momentum metrics, fundamental data, and news sentiment into a single composite score.

The project fetches market data, evaluates multiple signals, ranks stocks, and presents the results through an interactive Streamlit dashboard.

Features

• Technical analysis using RSI and moving averages
• Momentum scoring based on historical returns
• Fundamental scoring from company metrics
• News sentiment analysis
• Composite scoring system
• Buy / Hold / Sell recommendations
• Interactive Streamlit dashboard
• Candlestick charts with RSI visualization
• Filterable and sortable stock rankings

Tech Stack

• Python
• Streamlit
• Pandas
• Plotly
• yfinance
• Python-dotenv

Project Structure

stockscout/
├── app.py
├── run_scoring.py
├── config.py
├── requirements.txt
├── agent/
│   ├── scoring_agent.py
│   └── prompts.py
├── tools/
│   ├── market_data.py
│   ├── signal_calculator.py
│   └── news_sentiment.py
└── data/

Installation

Clone the repository:

git clone https://github.com/<your-username>/stockscout.git
cd stockscout

Create a virtual environment:

python -m venv venv

Activate it:

Windows

venv\Scripts\activate

macOS / Linux

source venv/bin/activate

Install dependencies:

pip install -r requirements.txt

Create an environment file:

cp .env.example .env

Configure the required API keys if needed.

Running the Scoring Pipeline

python run_scoring.py

The generated scores are stored in the data/ directory.

Running the Dashboard

streamlit run app.py

The application will be available at:

http://localhost:8501

Scoring Methodology

The final score is calculated using weighted components:

Component	Weight
Technical	35%
Momentum	25%
Fundamental	20%
Sentiment	20%

The resulting score is converted into a recommendation category:

• STRONG BUY
• BUY
• HOLD
• SELL
• STRONG SELL

Future Improvements

• Portfolio tracking
• Historical backtesting
• Alert notifications
• Additional technical indicators
• Sector-level analysis
• Automated scheduled runs

Disclaimer

This project is intended for educational and research purposes only and should not be considered financial advice.