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StockSignalAI
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# 📈 StockSignalAI

AI-powered stock analysis and signal generation tool using Streamlit.

---

## 🚀 Features

- Live stock data via Yahoo Finance
- Technical indicators (e.g., RSI, MACD, EMA)
- ML-based trading signal generator
- Model training and evaluation
- Interactive UI with Streamlit
- SQLite-based data storage

---

## ⚙️ Setup Instructions

### 1. Clone the Repository

```bash
git clone https://github.com/yourusername/StockSignalAI.git
cd StockSignalAI


## 2. Create Virtual Environment (optional but recommended)

python3 -m venv venv
source venv/bin/activate   # For macOS/Linux
venv\Scripts\activate      # For Windows
3. Install Dependencies

pip install -r requirements.txt
If you don’t have a requirements.txt, you can create one:

txt
Copy
Edit
yfinance
pandas
numpy
matplotlib
scikit-learn
streamlit
ta
Save the above as requirements.txt, then run:


pip install -r requirements.txt
🖥️ Run the App
Use Streamlit, not Python directly:


streamlit run app.py
💡 If Port 8501 is Busy

streamlit run app.py --server.port 8888
🧠 Notes
Session State Initialization
Before using any Streamlit session state variables (like model), initialize them at the start of the app:


if "model" not in st.session_state:
    st.session_state.model = None
Add similar blocks for other keys like data, stock_symbol, etc.

🗃️ Database Tables Used
Your app uses the following tables:

stock_data

technical_indicators

trading_signals

model_metrics

Ensure your SQLite DB or ORM setup creates them before use.

🔧 Project Structure (Example)

StockSignalAI/
├── app.py
├── requirements.txt
├── data/
│   └── stocks.db
├── models/
│   └── trained_models.pkl
└── README.md
📌 

TODO / Future Enhancements
Add backtesting module

Save models per stock  - this is done 

Upload and analyze user CSV data

Add performance metrics dashboard

Email/SMS alerts on new signals

🙌 Acknowledgments
yfinance

ta-lib

Streamlit

scikit-learn