AI-powered trading agents enhance the efficiency of crypto trading by continuously learning and refining strategies in real-time. Unlike traditional bots, these agents utilize machine learning techniques such as supervised learning to analyze historical market data and reinforcement learning to improve through simulated experiences. Essential skills for developing an effective AI trading agent include expertise in machine learning, programming, financial markets, and data management. A well-structured approach involves defining a trading strategy, building a robust data pipeline, and training the AI model. Backtesting is crucial to validate performance before deployment, ensuring the trading system adapts to market conditions effectively. Ongoing monitoring is necessary to maintain performance against market changes. Key challenges for AI in trading include market volatility, regulatory hurdles, and data integrity, while advancements like decentralized AI models and federated learning offer promising solutions for future developments in the field.

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