Long Short-Term Memory Networks

Long Short-Term Memory networks are a specialized type of recurrent neural network capable of learning long-term dependencies in sequential data. Unlike standard neural networks, they possess memory cells that can maintain information over long periods, making them ideal for analyzing time-series data like price charts and funding rates.

In derivatives trading, this allows the model to remember historical volatility patterns or trend reversals that occurred weeks ago, which may still influence current price action. By mitigating the vanishing gradient problem, LSTMs can process long sequences of market data to identify complex, time-dependent correlations.

They are a staple in quantitative finance for building predictive models that require deep context about past market behavior.