AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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There is a significant disconnect between model prediction accuracy and actual net returns; marginal improvements in prediction brought by complex models cannot offset profit losses caused by transaction frictions in high-frequency trading.
The empirical results verify that the core bottleneck of high-frequency quantitative trading is not model prediction accuracy but the cost adaptation mechanism of signal transformation, and a simple and efficient transaction cost filtering strategy is far more valuable than blindly iterating complex time-series models.
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