CTA - Crypto Trading Agent

The Crypto Trading Agent (CTA) is an advanced automated trading system designed to operate within the cryptocurrency markets. Utilizing state-of-the-art machine learning techniques and real-time data analytics, CTA aims to optimize trading strategies to maximize profitability and minimize risk.

Technical Architecture:

Machine Learning Models:

Predictive Analytics: Employs neural networks and time-series forecasting models (using libraries like TensorFlow and Scikit-Learn) to predict market movements and identify trading opportunities. Reinforcement Learning: Incorporates reinforcement learning algorithms to dynamically adjust trading strategies based on market performance feedback. Data Acquisition and Processing:

API Integration: Connects with cryptocurrency exchange APIs (such as Binance and Coinbase) to pull real-time and historical market data. Data Pipeline: Uses Pandas for data manipulation and preprocessing, ensuring that the data fed into the machine learning models is clean and structured. Backtesting Environment:

Strategy Validation: Leverages Backtrader, a Python-based backtesting library that allows for strategy testing against historical data to assess the efficacy and robustness of trading strategies. Operational Infrastructure:

Microservices Architecture: The system is designed as a series of microservices, facilitating easier scaling and maintenance. Docker and Kubernetes: Utilizes Docker for containerization and Kubernetes for orchestration, enabling the deployment of the trading bot across various environments seamlessly. User Interface:

Dashboard: Features a user-friendly web dashboard built with React.js, allowing users to monitor trades, view performance statistics, and adjust trading parameters in real-time. Deployment and Scalability:

The CTA is cloud-ready, with configurations available for deployment on major cloud platforms such as AWS, Google Cloud, and Azure. This ensures high availability and scalability to handle varying loads and multiple markets simultaneously.

Installation Guide

To get started with the installation, please refer to the Installation Guide.

Features

• Real-time trading
• Multiple exchange support (Binance, Coinbase)
• Backtesting capabilities
• Risk management
• Performance metrics

Usage

After installing the bot, you can start it by running:

python -m src.main

To-do:

Running on it's own blockchain

.
├── README.md               # Project overview and instructions
├── config
│   ├── default.json        # Default configuration settings
│   └── production.json     # Production-specific configuration settings
├── docs
│   ├── config-examples.md  # Example configuration files and explanations
│   ├── setup.md            # Setup instructions
│   └── usage.md            # Usage instructions
├── logs
│   └── trading.log         # Log file for trading activity
├── scripts
│   ├── deploy.sh           # Deployment script
│   └── setup.sh            # Setup script
├── setup.py                # Package setup and installation
├── requirements.txt        # List of dependencies for the project
└── src
    ├── alerts.py           # Handles alerts and notifications
    ├── api
    │   ├── api_manager.py  # Manages API interactions
    │   ├── binance
    │   │   ├── client.py   # Binance API client implementation
    │   │   └── models.py   # Binance API models and data structures
    │   ├── coinbase
    │   │   └── client.py   # Coinbase API client implementation
    │   └── common.py       # Common API functionalities shared across exchanges
    ├── backtesting.py      # Backtesting trading strategies
    ├── bot
    │   ├── config.py       # Configuration handling for the bot
    │   ├── logging.py      # Logging setup and management
    │   ├── scheduler.py    # Scheduling trading actions
    │   ├── strategy.py     # Trading strategies implementation
    │   └── trader.py       # Core trading operations
    ├── dashboard_interface.py # Interface for the trading dashboard
    ├── data_storage.py     # Data storage and retrieval
    ├── db
    │   ├── database.py     # Database connections and setup
    │   └── models.py       # ORM models for database interaction
    ├── event_handler.py    # Event handling and management
    ├── historical_data_fetcher.py # Fetches historical market data
    ├── logging_manager.py  # Manages logging for the application
    ├── main.py             # Main entry point for running the bot
    ├── market_analysis.py  # Analyzes market data for trading decisions
    ├── notification_service.py # Service for sending notifications
    ├── order_manager.py    # Manages order placements and executions
    ├── performance_metrics.py # Tracks and reports performance metrics
    ├── risk_management.py  # Implements risk management strategies
    ├── session_manager.py  # Manages trading sessions
    ├── strategy_optimizer.py # Optimizes trading strategies
    ├── tests
    │   ├── conftest.py     # Configuration for tests
    │   ├── test_api.py     # Tests for API interactions
    │   ├── test_bot.py     # Tests for bot functionalities
    │   ├── test_db.py      # Tests for database interactions
    │   ├── test_integration.py # Integration tests
    │   └── test_trader.py  # Tests for trading operations
    ├── trade_execution.py  # Executes trades based on strategy decisions
    ├── ui
    │   └── dashboard.py    # Dashboard UI for monitoring and control
    ├── user_settings.py    # Manages user settings and preferences
    └── utils
        ├── error_handling.py # Error handling utilities
        └── notifications.py # Notification utilities