╔══════════════════════════════════════════════════════════════════╗
║   LATENT MICRO-REGIME EARLY DETECTION IN LIMIT ORDER BOOKS       ║
║   Identifying structural market instability before it surfaces   ║
╚══════════════════════════════════════════════════════════════════╝

 

Prakul Sunil Hiremath · Vruksha Arun Hiremath

📄 Paper (coming soon)  ·  📦 Code DOI  ·  💻 Repository

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The Core Idea

Market stress does not arrive without warning. It accumulates.

Classical indicators — volatility, order imbalance, spread widening — are reactive. By the time they fire, the dislocation has already begun.

This research asks a harder question:

Can we detect the structural deterioration that precedes observable stress — before it becomes visible in price or spread?

The answer is yes. We call it the Latent Build-up Phase.

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What "Latent" Means Here

The market moves through three regimes. The critical one is invisible to standard monitors:

┌─────────────────────────────────────────────────────────────────┐
│                                                                 │
│   STATE 0 ──────────────►  STATE 1 ──────────────►  STATE 2    │
│                                                                 │
│   Stable                   Latent Build-up          Stress      │
│   ─────────                ───────────────          ──────      │
│   Balanced liquidity       Depth eroding            Price shock │
│   High resilience          Spread drifting          Visible      │
│   Equilibrium              ⚠ Hidden instability     Reactive    │
│                                                                 │
│                            ◄────── detection window ──────►     │
│                            ↑                         ↑          │
│                        our signal fires          stress begins   │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

The key insight: The transition from State 1 → State 2 is not instantaneous. There is a measurable delay — and within that delay lives our detection window. We exploit it.

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Detection Framework

Three independent signal channels. One fused trigger.

Signal Channels

Channel	What It Measures	Why It's Early
HMM Posterior Entropy	Uncertainty in regime classification	Rises as the latent state becomes ambiguous, before the transition
Temporal Depth Drift	Recursive tracking of LOB depth erosion	Captures slow structural decay invisible to snapshot metrics
Order Flow Toxicity	Imbalance between informed and uninformed flow	Signals adverse selection building in the book

Trigger Logic

MAX-Fusion Trigger
├── Rising-edge detection (onset of change, not absolute level)
├── Cross-channel aggregation (fire when any channel breaches threshold)
└── Early-detection constraint τ < σ (signal must precede stress)

Rising-edge detection is the key design choice. We don't ask "is the spread wide?" We ask "is it getting wider right now?" This bypasses the noise floor that kills absolute-threshold methods.

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Results

╔════════════════════════════════════════════════════════════════╗
║  METHOD              LEAD-TIME     PRECISION    COVERAGE       ║
╠════════════════════════════════════════════════════════════════╣
║  ★ Adaptive Trigger  +18.6 steps   100%         52.6%          ║
║    HMM               +14.9 steps   100%         43.2%          ║
║    Multi-Trigger     +13.1 steps   100%         28.1%          ║
╠════════════════════════════════════════════════════════════════╣
║  ✗ Order Imbalance   −24.8 steps    54.9%        78.7%          ║
║  ✗ Volatility        −32.0 steps    45.5%        43.3%          ║
╚════════════════════════════════════════════════════════════════╝

Reading the table:

• Positive lead-time means the signal fires before stress begins. Baselines are strictly negative — they lag.
• 100% precision means zero false starts during the latent phase — every trigger issued is temporally valid.
• Coverage reflects selectivity: we fire only when we're certain. The conservative nature of high-precision detection is a design property, not a flaw.

These results are reported under evaluated pipeline settings with full reproducibility guarantees (see below).

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Empirical Findings

1. Latent instability exists and is measurable. Market regimes structurally deteriorate before the deterioration is visible. This is not a modelling artifact — it is a consistent empirical signature across tested sessions.

2. Depth erosion is the most reliable early signal. Depth decay in the LOB precedes spread widening and price impact. If the book is thinning quietly, something is coming.

3. HMM posterior entropy is a structural stress barometer. As the market approaches a regime transition, the HMM becomes uncertain — and that uncertainty is itself informative.

4. Rising-edge detection outperforms threshold detection. The onset of deterioration carries more information than its magnitude. Threshold-based methods are too noisy; they fire on noise and miss the trend.

5. Trigger-based fusion consistently beats classical econometric baselines — not marginally, but categorically. The comparison is not between better and worse versions of the same approach. It is between a predictive framework and a reactive one.

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Repository Structure

LOB-Latent-Regimes/
│
├── experiments/
│   ├── v1_baseline.py            # Initial HMM formulation
│   ├── v2_entropy.py             # Posterior entropy tracking
│   ├── v3_depth_drift.py         # Temporal depth signal
│   ├── v4_triggers.py            # Trigger logic development
│   ├── v5_fusion.py              # MAX-fusion framework
│   ├── v6_rising_edge.py         # Rising-edge detection
│   └── v7_final.py               # ★ Production pipeline
│
├── notebooks/
│   └── analysis.ipynb            # Experiment analysis + figures
│
├── results/
│   ├── figures/                  # High-resolution performance plots
│   └── summary.txt               # Quantified results
│
├── paper/                        # Technical manuscript
├── assets/                       # Visualizations, GIFs
└── README.md

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Reproducibility Guarantees

This pipeline was built to be trusted.

✓  Fully causal — no lookahead bias at any stage
✓  Rolling normalization only — no global statistics that leak future data  
✓  HMM re-fit periodically — no leakage across the evaluation window
✓  Deterministic seeds — results are exact across runs
✓  Validated on Google Colab (NVIDIA T4) and Apple Silicon (M4 Pro/Max)

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Quick Start

# Clone
git clone https://github.com/prakulhiremath/LOB-Latent-Regimes.git
cd LOB-Latent-Regimes

# Install
pip install -r requirements.txt

# Run the final pipeline
python experiments/v7_final.py

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Scope & Limitations

Be precise about what this is.

This repo is	This repo is not
A detection framework for latent regime transitions	A trading strategy
An empirical study of LOB microstructure	Optimised for execution latency
A reproducible research pipeline	A production system
A contribution to predictive market microstructure	Financial advice

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Contributions

• Causal formulation of the latent build-up → stress transition as a three-state latent process
• Temporal drift identification — subtle depth and spread drift as a leading precursor to liquidity voids
• MAX-fusion + rising-edge trigger — novel detection logic for sub-millisecond microstructure data
• Empirical demonstration of strictly positive lead-time over reactive benchmarks across all evaluated regimes

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Citation

@article{hiremath2026lob,
  title   = {Early Detection of Latent Micro-Regimes in Limit Order Books},
  author  = {Hiremath, Prakul Sunil and Hiremath, Vruksha Arun},
  year    = {2026},
  doi     = {10.5281/zenodo.19697687}
}

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Built for reproducible research in quantitative finance and machine learning.

If the signal fires before the storm — it worked.