🚀 Binance AI Trading Bot

A cutting-edge Binance AI Trading Bot that leverages advanced machine learning models to predict cryptocurrency prices and make smarter trading decisions. This project combines state-of-the-art time series forecasting models (LSTM, CNN-LSTM, Transformer, Prophet) with sentiment analysis from news articles, ensuring enhanced prediction accuracy.

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📋 Table of Contents

1. Features
2. Architecture
3. Installation
4. Configuration
5. Usage
6. Models Supported
7. Sentiment Analysis
8. Contributing
9. License

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✨ Features

• Multiple Models: Supports a variety of machine learning models for time series forecasting.
• Real-Time Data: Fetches live market data from Binance API.
• Sentiment Analysis: Integrates news sentiment analysis to adjust predictions dynamically.
• Web Interface: Features an intuitive Flask-based dashboard for user interaction.
• Customization: Fully configurable parameters for model selection, training, and prediction.

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🏗️ Architecture

The bot is modular and well-organized, with the following structure:

binance-ai-trading-bot/
│
├── models/        # Machine learning models for predictions
├── data/          # Data loading and preprocessing scripts
├── utils/         # Utility functions for predictions and management
├── app_routes/    # Flask routes for the web dashboard
├── templates/     # HTML templates for web pages
└── config.py      # Configuration file for API keys and settings

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⚙️ Installation

Prerequisites

• Python (3.7 or higher)
• Git
• Virtualenv (recommended)

Steps

1. Clone the repository:

   git clone https://github.com/yourusername/binance-ai-trading-bot.git
   cd binance-ai-trading-bot

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate   # On Windows: `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

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🔧 Configuration

Binance API Keys

1. Sign up on Binance and obtain your API key and Secret key.
2. Add them to the config.py file or set them as environment variables.

Modify Configurations

Adjust other settings in the config.py file as needed to suit your requirements.

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🚀 Usage

Running the Application

To start the application:

python app.py

Accessing the Dashboard

Open your web browser and navigate to:

http://127.0.0.1:5000/

Training a Model

1. Go to the Train Model page in the dashboard.
2. Select:
   • Model type
   • Coin pair (e.g., BTC/USDT)
   • Data period
   • Epochs and batch size
3. Click Train to start training.

Making a Prediction

1. Navigate to the Prediction page.
2. Select a trained model from the dropdown.
3. (Optional) Enable Sentiment Analysis.
4. Click Predict to view the results.

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🧠 Models Supported

• LSTM: Long Short-Term Memory
• CNN-LSTM: Hybrid Convolutional-LSTM Model
• Transformer: Attention-based Neural Network
• Prophet:High-performance solution for time-series forecasting

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💬 Sentiment Analysis

• Fetches news articles related to the selected cryptocurrency.
• Uses Natural Language Processing (NLP) models to analyze sentiment.
• Dynamically adjusts price predictions based on market sentiment.

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🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix:

   git checkout -b feature-name

3. Make your changes and commit them:

   git commit -m "Description of your changes"

4. Push your branch:

   git push origin feature-name

5. Submit a Pull Request.

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📄 License

This project is licensed under the MIT License. Feel free to use and modify it as per the terms of the license.

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Happy Trading! 🚀