This research investigates whether latent microstructure dynamics in Limit Order Books (LOB) can be mathematically identified before observable liquidity stress manifests.

Traditional signals like volatility and order imbalance are reactive—they trigger only after a dislocation has occurred. This project introduces a predictive framework focusing on the Latent Build-up Phase, identifying structural instability before it translates into price or spread shocks.

🧠 Core Methodology: The Latent Build-up

Market stress is rarely instantaneous; it is preceded by structural deterioration. We model this as a three-state latent process:

State	Regime	Market Description	Signal Characteristic
0	Stable	Balanced liquidity, high resilience	Equilibrium
1	Latent Build-up	Depth erosion, subtle spread drift	Hidden Instability
2	Stress	Observable dislocation, price shocks	Reactive

Key Discovery: A delayed transition from State 1 → State 2 creates a deterministic prediction window, allowing for early detection with strictly positive lead-time.

🛠 Detection Framework

The detector employs a high-fidelity fusion of probabilistic and temporal signals to identify the "inflection point" of market health.

📡 Signal Integration

Probabilistic Instability: HMM posterior entropy monitoring.
Temporal Drift: Recursive analysis of spread and depth dynamics.
Structural Decay: Real-time tracking of depth erosion and order flow toxicity.

🕹 Detection Logic

MAX-Trigger Fusion: Cross-channel integration to capture the first sign of decay.
Rising-Edge Detection: Focusing on the onset of change rather than absolute thresholds.
Early-Detection Constraint: Optimization of \tau < \sigma, ensuring the signal precedes the event.

📊 Results & Performance

Method	Mean `\Delta` (Lead-Time)	Precision	Coverage
Adaptive Trigger	+18.62	100%	52.6%
Model HMM	+14.95	100%	43.2%
Multi-Trigger	+13.15	100%	28.1%
Order Imbalance	-24.84	54.9%	78.7%
Volatility	-32.02	45.5%	43.3%

Critical Interpretations:

Positive Lead-Time: Our methods detect stress before it happens; baselines are strictly negative (lagging).
Temporal Validity: 100% precision indicates zero "false starts" before the latent phase begins.
Trade-off: Coverage levels reflect the conservative nature of high-precision early signals.

📈 Key Findings

Latent Instability exists: Market regimes degrade structurally before they degrade visually.
Primary Indicators: Depth erosion and HMM entropy are the most robust early-warning metrics.
Signal Edge: Rising-edge detection is essential to bypass the noise inherent in absolute thresholding.
Performance: Trigger-based detection consistently outperforms classical econometric baselines.

📂 Repository Structure

.
├── experiments/          # Iterative development v1 → v7
├── notebooks/            # Production-grade experiment analysis
├── results/
│   ├── figures/          # High-resolution performance plots
│   └── summary.txt       # Quantified results summary
├── paper/                # Technical manuscript (PDF)
├── assets/               # Visualizations and GIFs
└── README.md

🚀 Reproducibility

Validated across high-compute and local environments:

Cloud: Google Colab (NVIDIA T4)
Local: Apple Silicon (M4 Pro/Max)

Quick Start

# Clone the repository
git clone https://github.com/your-repo/lob-early-detection.git

# Install dependencies
pip install -r requirements.txt

# Execute the final pipeline
python experiments/v7_final.py

📝 Contributions

Causal Formulation: Formalizing the Latent Build-up \rightarrow Stress transition.
Temporal Drift: Identifying subtle drift as a precursor to liquidity voids.
MAX Fusion & Rising-Edge: Novel trigger logic for sub-millisecond microstructure data.
Empirical Proof: Demonstrating strictly positive lead-time over reactive benchmarks.

📑 Citation

@article{lob_micro_regime_detection_2026,
  title={Early Detection of Latent Micro-Regimes in Limit Order Books},
  author={Hiremath, Prakul. & Hiremath, Vruksha},
  year={2026},
}

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⚡️ Latent Micro-Regime Early Detection in Limit Order Books

🔬 Overview