# β‘οΈ Latent Micro-Regime Early Detection in Limit Order Books
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## π¬ Overview
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**βthey 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.
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## π§ 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 β State 2** creates a deterministic prediction window, allowing for early detection with strictly positive lead-time.
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## π Detection Framework
The detector employs a high-fidelity fusion of probabilistic and temporal signals to identify the "inflection point" of market health.
### π‘ 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.
### πΉ 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.
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## π 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.
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## π Key Findings
1. **Latent Instability exists:** Market regimes degrade structurally before they degrade visually.
2. **Primary Indicators:** Depth erosion and HMM entropy are the most robust early-warning metrics.
3. **Signal Edge:** Rising-edge detection is essential to bypass the noise inherent in absolute thresholding.
4. **Performance:** Trigger-based detection consistently outperforms classical econometric baselines.
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## π Repository Structure
```bash
.
βββ experiments/ # Iterative development v1 β v7
βββ notebooks/ # Production-grade experiment analysis
βββ results/
β βββ figures/ # High-resolution performance plots
β βββ summary.txt # Quantified results summary
βββ paper/ # Technical manuscript (PDF)
βββ assets/ # Visualizations and GIFs
βββ README.md
```
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## π Reproducibility
Validated across high-compute and local environments:
* **Cloud:** Google Colab (NVIDIA T4)
* **Local:** Apple Silicon (M4 Pro/Max)
### Quick Start
```bash
# 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
```
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## π 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.
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## π Citation
```bibtex
@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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