Trading Agent System

This directory contains the implementation of a modular trading agent system that integrates with the neural network models and can execute trades on various cryptocurrency exchanges.

Overview

The trading agent system is designed to:

Connect to different cryptocurrency exchanges using a common interface
Execute trades based on signals from neural network models
Manage risk through position sizing, trade limits, and cooldown periods
Monitor and report on trading activity

Components

Exchange Interfaces

ExchangeInterface: Abstract base class defining the common interface for all exchange implementations
BinanceInterface: Implementation for the Binance exchange, with support for both mainnet and testnet
MEXCInterface: Implementation for the MEXC exchange

Trading Agent

The TradingAgent class (trading_agent.py) manages trading activities:

Connects to the configured exchange
Processes trading signals from neural network models
Applies trading rules and risk management
Tracks and reports trading performance

Neural Network Orchestrator

The NeuralNetworkOrchestrator class (neural_network_orchestrator.py) coordinates between models and trading:

Manages the neural network inference process
Routes model signals to the trading agent
Provides integration with the RealTimeChart for visualization

Usage

Basic Usage

from NN.exchanges import BinanceInterface, MEXCInterface
from NN.trading_agent import TradingAgent

# Initialize an exchange interface
exchange = BinanceInterface(
    api_key="your_api_key",
    api_secret="your_api_secret",
    test_mode=True  # Use testnet
)

# Connect to the exchange
exchange.connect()

# Create a trading agent
agent = TradingAgent(
    exchange_name="binance",
    api_key="your_api_key",
    api_secret="your_api_secret",
    test_mode=True,
    trade_symbols=["BTC/USDT", "ETH/USDT"],
    position_size=0.1,
    max_trades_per_day=5,
    trade_cooldown_minutes=60
)

# Start the trading agent
agent.start()

# Process a trading signal
agent.process_signal(
    symbol="BTC/USDT",
    action="BUY",
    confidence=0.85,
    timestamp=int(time.time())
)

# Stop the trading agent when done
agent.stop()

Integration with Neural Network Models

The system is designed to be integrated with neural network models through the NeuralNetworkOrchestrator:

from NN.neural_network_orchestrator import NeuralNetworkOrchestrator

# Configure exchange
exchange_config = {
    "exchange": "binance",
    "api_key": "your_api_key",
    "api_secret": "your_api_secret",
    "test_mode": True,
    "trade_symbols": ["BTC/USDT", "ETH/USDT"],
    "position_size": 0.1,
    "max_trades_per_day": 5,
    "trade_cooldown_minutes": 60
}

# Initialize orchestrator
orchestrator = NeuralNetworkOrchestrator(
    model=model,
    data_interface=data_interface,
    chart=chart,
    symbols=["BTC/USDT", "ETH/USDT"],
    timeframes=["1m", "5m", "1h", "4h", "1d"],
    exchange_config=exchange_config
)

# Start inference and trading
orchestrator.start_inference()

Configuration

Exchange-Specific Configuration

Binance: Supports both mainnet and testnet environments
MEXC: Supports mainnet only (no test environment available)

Trading Agent Configuration

exchange_name: Name of exchange ('binance', 'mexc')
api_key: API key for the exchange
api_secret: API secret for the exchange
test_mode: Whether to use test/sandbox environment
trade_symbols: List of trading symbols to monitor
position_size: Size of each position as a fraction of balance (0.0-1.0)
max_trades_per_day: Maximum number of trades to execute per day
trade_cooldown_minutes: Minimum time between trades in minutes

Adding New Exchanges

To add support for a new exchange:

Create a new class that inherits from ExchangeInterface
Implement all required methods (see exchange_interface.py)
Add the new exchange to the imports in __init__.py
Update the _create_exchange method in TradingAgent to support the new exchange

Example:

class KrakenInterface(ExchangeInterface):
    """Kraken Exchange API Interface"""
    
    def __init__(self, api_key=None, api_secret=None, test_mode=True):
        super().__init__(api_key, api_secret, test_mode)
        # Initialize Kraken-specific attributes
        
    # Implement all required methods...

Security Considerations

API keys should have trade permissions but not withdrawal permissions
Use environment variables or secure storage for API credentials
Always test with small position sizes before deploying with larger amounts
Consider using test mode/testnet for initial testing

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