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Difficulty: Hard
Category: machine_learning
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Topics: neural_networks, numpy, anomaly_detection, optimization
Yield curves and volatility surfaces often exhibit low-dimensional structures that allow market makers to detect anomalies representing arbitrage opportunities or data errors. Autoencoders leverage this property by learning a compressed representation of the data manifold, where high reconstruction errors indicate significant deviations from normal market behavior. Task Implement a neural network autoencoder from scratch using numpy to detect market anomalies by training on historical yield cur
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