[P] Comparing Mamba (SSM) vs. LSTM for Signal Recovery in Noisy Market Microstructure

Hi everyone, I’m a 2nd-year CS student. For my latest independent study, I wanted to see how State Space Models (Mamba) compare to LSTMs when dealing with high-entropy time series, specifically, finding hidden ‘Iceberg’ orders in a noisy limit order book.

I built a ‘Frozen Chaos’ simulation engine to bench both architectures on signal efficiency and OOD resilience.

Key Findings from Phase 1:

• ‘Fail-Fast’ Logic: In a ‘Pure Drain’ stress test (zero signal), the LSTM suffered from state-locking, staying ‘certain’ of a false signal for an average of 928 ticks.
• Mamba’s Selective Scan: Mamba was highly sensitive but correctly ‘flushed’ its memory 28x faster than the LSTM baseline once the data didn’t confirm the signal.
• Risk Exposure: Mamba reduced total risk exposure by 94% compared to the RNN.

I’ve documented the simulation logic, convergence charts, and the forensic P&L results in the README here: jackdoesjava/mamba-ssm-microstructure-dynamics: Investigating the Information Bottleneck in Stochastic Microstructure: A Comparative Study of Selective State Space Models (Mamba) vs. Gated RNNs.

I’m currently moving into Phase 2 (Monte Carlo significance testing). I’d love some feedback from the community on my implementation of the selective scan mechanism or how you would handle the ‘jitter’ in high-frequency signal detection!