AI Cryptocurrency Price Forecaster

An advanced deep learning-based cryptocurrency price forecasting tool that uses LSTM neural networks to predict future price movements with confidence intervals.

Features

• Historical price data fetching from cryptocurrency APIs
• Advanced LSTM model with technical indicators
• Monte Carlo simulation for uncertainty estimation
• Automatic CPU/GPU detection and optimization
• Beautiful visualization of forecasts with confidence intervals
• Automatic plot saving functionality
• Robust error handling and logging

Requirements

• Python 3.7+
• TensorFlow 2.x
• NumPy
• Pandas
• Matplotlib
• Requests
• Scikit-learn

Installation

1. Clone this repository:

git clone <repository-url>
cd <repository-name>

2. Install the required packages:

pip install tensorflow numpy pandas matplotlib requests scikit-learn

Usage

The main functionality is provided through the ai_price_forecast function in ai_forecast.py:

from ai_forecast import ai_price_forecast

# Example usage
forecast_dates, prices, upper_bound, lower_bound = ai_price_forecast(
    api_url="https://api.coingecko.com/api/v3",
    crypto_id="bitcoin",
    historical_days=180,
    forecast_days=30
)

Parameters

• api_url: Base URL for the cryptocurrency API
• crypto_id: Identifier for the cryptocurrency (e.g., "bitcoin")
• historical_days: Number of historical days to use for training (default: 180)
• forecast_days: Number of days to forecast into the future (default: 30)
• seq_length: Sequence length for LSTM input (default: 90)
• epochs: Number of training epochs (default: 500)
• batch_size: Batch size for training (default: 32)
• patience: Early stopping patience (default: 30)
• model_path: Path to save/load the model (optional)
• save_path: Path to save the best model during training (optional)

Currently you are only prompted for forecast length

Features in Detail

Technical Indicators

The model uses several technical indicators for improved forecasting:

• Moving averages (5, 10, and 20-day)
• Volatility measures
• Price momentum
• Rate of change indicators

Hardware Optimization

• Automatic GPU detection and utilization when available
• Graceful fallback to CPU when GPU is unavailable or encounters errors
• Multi-core CPU optimization

Visualization

• Historical price plots
• Forecast line with confidence intervals
• Automatic plot saving with timestamps

Output

The function returns four lists:

1. Forecast dates
2. Forecasted prices
3. Upper confidence bounds
4. Lower confidence bounds

Plots are automatically saved in the forecast_plots directory.

Error Handling

The system includes comprehensive error handling for:

• API connection issues
• Data processing errors
• Hardware configuration problems
• Model training issues

All errors are logged with detailed messages for debugging.

License

[Your chosen license]

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.