A Python-based application to analyze, visualize, and predict stock price trends from the NSE (National Stock Exchange of India) using real-time financial data. It leverages time-series analysis, technical indicators, and basic predictive models to generate insights such as expected returns, confidence intervals, and buy/sell signals.
🚀 Features
🔗 Real-Time Data: Fetches live stock price data from NSE via APIs.
📊 Technical Indicators: EMA, RSI, MACD, Bollinger Bands, Stochastic Oscillator.
🔮 Prediction Engine: Forecasts short-term price movement with confidence scoring.
🏆 Top Stock Filtering: Ranks stocks by highest expected return or lowest error (MAE).
📉 Visualization: Interactive charts for price and indicators.
📝 Export: Saves results to Excel (Sheet2 with predictions & confidence).
🛠️ Tech Stack
Programming Language: Python 🐍
Libraries:
pandas, numpy → Data preprocessing & analysis
matplotlib → Visualization
ta → Technical indicators
yfinance / nsetools → Data fetching
scikit-learn → Predictive modeling
⚙️ Installation & Setup
git clone https://github.com/your-username/NSE_Stock_Tracker.git cd NSE_Stock_Tracker
python -m venv venv source venv/bin/activate # on macOS/Linux venv\Scripts\activate # on Windows
pip install -r requirements.txt
Example Output 🧠 Signal: CALL (UP) ✅ Confidence: 2/2 📈 Expected Return (5 days): +3.7% (95% CI: 2.1% – 5.4%)
Results
Predictions saved in Excel (Sheet2)
Interactive charts for analysis
📂 Project Structure ├── main.py # Main script ├── indicators.py # Technical indicators calculation ├── predictor.py # Prediction & signal generation ├── requirements.txt # Dependencies ├── README.md # Project documentation └── outputs/ # Excel reports & charts
🧠 How It Works
Fetches the last 75 one-minute candles (configurable).
Computes EMA crossover, RSI, MACD, Bollinger Bands, Stochastic Oscillator.
Generates a buy/sell/hold signal with confidence score.
Uses historical data to predict expected return over the next 5 days with a confidence interval.
Exports results and ranks top 5 opportunities.
📸 Screenshots
(Add sample chart images, Excel output screenshots here)
🔒 Security Note
Your API keys / credentials.json are ignored via .gitignore.
Never push credentials to GitHub (GitHub Push Protection is enabled).
📌 Future Improvements
✅ Improve ML model accuracy with LSTM / Prophet
✅ Add portfolio simulation
✅ Deploy via Streamlit for interactive web dashboard
🤝 Contributing
Contributions, issues, and feature requests are welcome! Feel free to fork this repo and submit a PR.
👨💻 Author:
Aayush Singh
💼 www.linkedin.com/in/aayush1908
⭐ If you like this project, give it a star on GitHub! ⭐