FinSight2 📈

Sentiment-Enhanced Deep Learning Stock Prediction

FinSight2 is a deep learning–based system that predicts Indian stock market trends using historical price data and technical indicators. The focus is on LSTM and RNN models, with ensemble logic and an interactive Streamlit dashboard for visualization.

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✨ Features

• Fetches real-time historical stock data using Yahoo Finance (yfinance)
• Computes popular technical indicators:
  • RSI (Relative Strength Index)
  • EMA, SMA, WMA (Moving Averages)
  • MACD (Moving Average Convergence Divergence)
  • Bollinger Bands
  • MFI (Money Flow Index)
• Deep learning prediction with:
  • LSTM (Long Short-Term Memory)
  • RNN (Recurrent Neural Network)
• Ensemble forecasting combining outputs from DL models
• Interactive Streamlit dashboard:
  • Input any NSE stock ticker
  • Generate 7-day OHLC forecasts
  • Toggle light/dark mode
  • Export results as CSV

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⚙️ Installation

Clone the repository:

git clone https://github.com/iamarchitshah/FinSight2.git
cd FinSight2

Create a virtual environment (recommended):

python -m venv venv
source venv/bin/activate   # macOS/Linux
venv\Scripts\activate      # Windows

Install requirements:

pip install -r requirements.txt

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🚀 Usage

Run the Streamlit app:

streamlit run app.py

Then open the local server link (e.g., http://localhost:8501) in your browser.

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📊 Example

1. Enter stock ticker (e.g., RELIANCE.NS).
2. Select analysis options (indicators, forecast period).
3. View interactive plots:
   • Historical vs Predicted Prices
   • RSI, MACD, Bollinger Bands, etc.
4. Export predictions to CSV.

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📂 Project Structure

FinSight2/
│── app.py              # Streamlit dashboard
│── models.py           # LSTM and RNN model definitions
│── utils.py            # Data fetching & technical indicators
│── requirements.txt    # Dependencies
│── README.md           # Project documentation

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✅ Roadmap

• Add sentiment analysis from news & social media
• Enhance ensemble strategy
• Cloud deployment (Streamlit Cloud / AWS / GCP)
• Add hyperparameter tuning module

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🗓️ 7-Day Intern Work Plan

Day	D24IT166 Focus	D24IT168 Focus
1	Setup & repo exploration	Environment setup & yfinance testing
2	Data fetching framework	Technical indicator implementation
3	LSTM exploration & training	RNN exploration & training
4	Ensemble logic (LSTM+RNN)	Forecast generation (Open/High/Low/Close)
5	Dashboard visual improvements	CSV export & UI polish
6	Unit testing for core modules	Edge case testing and error handling
7	README & documentation updates	Deployment prep & verification

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🤝 Contributing

Pull requests are welcome. For significant changes, open an issue to discuss what you’d like to modify.

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📜 License

This project is licensed under the MIT License.