AI-powered trading agents are advanced systems that learn and adapt in real-time, unlike traditional bots confined to pre-set rules. They leverage various strategies, including arbitrage, trend following, market-making, and sentiment analysis, although each comes with unique challenges. Creating an effective AI trading agent requires a blend of technical, financial, and analytical skills, including proficiency in machine learning, programming, and risk management. Key steps in development involve defining a trading strategy, building a robust data pipeline, choosing the right AI model, and emphasizing execution and risk management. The process includes data collection and preparation, model training, backtesting, deployment, and continuous monitoring to adapt to market changes. Despite their advantages, AI trading strategies can falter under market anomalies, regulatory hurdles, and data integrity issues. The future of AI in crypto trading hinges on its ability to adapt, comply with regulations, and maintain security while continuously refining models.

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