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RLTraderNet integrates deep reinforcement learning with behavioral imitation modeling techniques, drawing on the same approach that AI uses to learn games like Go. By studying the trading behaviors of human traders, it develops a fundamental understanding of trading. This is followed by iterative feedback within a deep reinforcement learning environment. After multiple iterations, it has evolved to effectively mimic the trading strategies of experienced traders across various market scenarios. Initially developed for academic research, this strategy aims to explore and evaluate the potential and limitations of AI in financial trading. It’s important to note that, while the strategy leverages cutting-edge technologies and comprehensive risk management frameworks, it does not guarantee profitability. Given the inherent volatility and unpredictability of financial markets, the promise of positive returns cannot be assured, even with advanced methodologies in place.