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# Stock and Cryptocurrency Price Prediction Web App

A comprehensive web application that predicts stock and cryptocurrency prices using LSTM neural networks, featuring real-time market data, historical charts, and sentiment analysis.

## Features

- **📈 Price Prediction**: Predict next day's closing price using LSTM neural networks
- **📊 Real-time Data**: Live price and daily percentage change display
- **📉 Historical Charts**: Interactive charts showing last 100 days of price data
- **📰 Sentiment Analysis**: Market sentiment analysis based on news headlines (Bullish 🐂, Bearish 🐻, or Neutral)
- **🏢 Company Information**: Detailed asset information including market cap and volume
- **💹 Multi-Asset Support**: Both stocks and cryptocurrencies supported

## Technologies Used

### Backend
- **Flask** - Web framework
- **TensorFlow/Keras** - LSTM model implementation
- **Scikit-learn** - Data preprocessing and scaling

### Data APIs
- **yfinance** - Stock market data
- **CoinGecko API** - Cryptocurrency data
- **NewsApiClient** - News headlines for sentiment analysis

### Frontend
- **HTML/CSS/JavaScript** - Frontend structure and styling
- **Chart.js** - Interactive data visualizations
- **Bootstrap** - Responsive UI components (optional)


## Installation & Setup

### Prerequisites

- Python 3.8 or higher
- News API key (free from [newsapi.org](https://newsapi.org/))

### Step 1: Clone the Repository

```bash
git clone <repository-url>
cd stock-crypto-predictor

Step 2: Create Virtual Environment

# Create virtual environment
python -m venv venv

# Activate on macOS/Linux
source venv/bin/activate

# Activate on Windows
venv\Scripts\activate

Step 3: Install Dependencies

pip install -r requirements.txt

Step 4: Configure API Keys

Create a config.py file in the root directory:

# config.py
NEWS_API_KEY = 'your_news_api_key_here'

Or update in main.py:

newsapi = NewsApiClient(api_key='your_actual_api_key')

Step 5: Train the Model

python rnn.py

This will:

1. Load training data from data/Google_Stock_Price_Train.csv
2. Preprocess and scale the data
3. Train the LSTM model
4. Save model to models/google_stock_lstm_model.keras
5. Save scaler to models/min_max_scaler.pkl

Step 6: Run the Application

python main.py

Visit http://127.0.0.1:5000 in your browser.

Usage

1. Home Page: Enter a stock ticker (e.g., AAPL, GOOGL) or cryptocurrency symbol (e.g., BTC-USD, ETH-USD)
2. Click Predict: The app will fetch real-time data and generate predictions
3. View Results:
   • Predicted price for next trading day
   • Current market data
   • Historical price chart
   • News sentiment analysis
   • Company/asset information

API Reference

Supported Stock Symbols

• AAPL (Apple)
• GOOGL (Google)
• MSFT (Microsoft)
• TSLA (Tesla)
• And many more...

Supported Crypto Symbols

• BTC-USD (Bitcoin)
• ETH-USD (Ethereum)
• ADA-USD (Cardano)
• And other major cryptocurrencies

Model Details

Architecture

• Type: Recurrent Neural Network (RNN)
• Layers: Long Short-Term Memory (LSTM) layers
• Input: Historical closing prices (90-day window)
• Output: Next day's predicted closing price

Training Data

• Google stock price data from specific period
• Data normalized using MinMaxScaler
• 60-time step sliding window for sequences

Troubleshooting

Common Issues

1. News API Errors: Ensure your API key is valid and has not exceeded free tier limits
2. Model Not Found: Run python rnn.py to train and save the model first
3. Data Fetching Issues: Check internet connection and API availability

Dependencies Issues

# If you encounter dependency conflicts
pip install --upgrade pip
pip install -r requirements.txt --no-cache-dir

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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