PRAKUL HIREMATHandGitHub
aea1332fef
Updated README to enhance formatting, improve clarity, and add new sections.
243 lines
10 KiB
Markdown
243 lines
10 KiB
Markdown
<div align="center">
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<br/>
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```
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╔══════════════════════════════════════════════════════════════════╗
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║ LATENT MICRO-REGIME EARLY DETECTION IN LIMIT ORDER BOOKS ║
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║ Identifying structural market instability before it surfaces ║
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╚══════════════════════════════════════════════════════════════════╝
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```
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[](https://doi.org/10.5281/zenodo.19697687)
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<br/>
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**Prakul Sunil Hiremath · Vruksha Arun Hiremath**
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[📄 Paper (coming soon)](#) · [📦 Code DOI](https://doi.org/10.5281/zenodo.19697687) · [💻 Repository](https://github.com/prakulhiremath/LOB-Latent-Regimes)
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<br/>
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<img src="assets/detection.gif" width="860" alt="Detection Timeline — Latent regime transitions identified before observable stress"/>
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<br/>
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</div>
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---
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## The Core Idea
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> **Market stress does not arrive without warning. It *accumulates*.**
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Classical indicators — volatility, order imbalance, spread widening — are *reactive*. By the time they fire, the dislocation has already begun.
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This research asks a harder question:
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> *Can we detect the structural deterioration that **precedes** observable stress — before it becomes visible in price or spread?*
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The answer is yes. We call it the **Latent Build-up Phase**.
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---
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## What "Latent" Means Here
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The market moves through three regimes. The critical one is invisible to standard monitors:
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ │
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│ STATE 0 ──────────────► STATE 1 ──────────────► STATE 2 │
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│ │
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│ Stable Latent Build-up Stress │
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│ ───────── ─────────────── ────── │
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│ Balanced liquidity Depth eroding Price shock │
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│ High resilience Spread drifting Visible │
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│ Equilibrium ⚠ Hidden instability Reactive │
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│ │
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│ ◄────── detection window ──────► │
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│ ↑ ↑ │
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│ our signal fires stress begins │
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│ │
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└─────────────────────────────────────────────────────────────────┘
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```
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**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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---
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## Detection Framework
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Three independent signal channels. One fused trigger.
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### Signal Channels
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| Channel | What It Measures | Why It's Early |
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|---|---|---|
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| **HMM Posterior Entropy** | Uncertainty in regime classification | Rises as the latent state becomes ambiguous, before the transition |
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| **Temporal Depth Drift** | Recursive tracking of LOB depth erosion | Captures slow structural decay invisible to snapshot metrics |
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| **Order Flow Toxicity** | Imbalance between informed and uninformed flow | Signals adverse selection building in the book |
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### Trigger Logic
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```
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MAX-Fusion Trigger
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├── Rising-edge detection (onset of change, not absolute level)
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├── Cross-channel aggregation (fire when any channel breaches threshold)
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└── Early-detection constraint τ < σ (signal must precede stress)
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```
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**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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---
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## Results
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```
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╔════════════════════════════════════════════════════════════════╗
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║ METHOD LEAD-TIME PRECISION COVERAGE ║
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╠════════════════════════════════════════════════════════════════╣
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║ ★ Adaptive Trigger +18.6 steps 100% 52.6% ║
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║ HMM +14.9 steps 100% 43.2% ║
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║ Multi-Trigger +13.1 steps 100% 28.1% ║
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╠════════════════════════════════════════════════════════════════╣
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║ ✗ Order Imbalance −24.8 steps 54.9% 78.7% ║
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║ ✗ Volatility −32.0 steps 45.5% 43.3% ║
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╚════════════════════════════════════════════════════════════════╝
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```
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**Reading the table:**
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- **Positive lead-time** means the signal fires *before* stress begins. Baselines are strictly negative — they lag.
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- **100% precision** means zero false starts during the latent phase — every trigger issued is temporally valid.
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- **Coverage** reflects selectivity: we fire only when we're certain. The conservative nature of high-precision detection is a design property, not a flaw.
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> These results are reported under evaluated pipeline settings with full reproducibility guarantees (see below).
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---
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## Empirical Findings
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**1. Latent instability exists and is measurable.**
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Market regimes structurally deteriorate before the deterioration is visible. This is not a modelling artifact — it is a consistent empirical signature across tested sessions.
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**2. Depth erosion is the most reliable early signal.**
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Depth decay in the LOB precedes spread widening and price impact. If the book is thinning quietly, something is coming.
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**3. HMM posterior entropy is a structural stress barometer.**
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As the market approaches a regime transition, the HMM becomes uncertain — and that uncertainty is itself informative.
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**4. Rising-edge detection outperforms threshold detection.**
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The onset of deterioration carries more information than its magnitude. Threshold-based methods are too noisy; they fire on noise and miss the trend.
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**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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---
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## Repository Structure
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```
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LOB-Latent-Regimes/
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│
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├── experiments/
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│ ├── v1_baseline.py # Initial HMM formulation
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│ ├── v2_entropy.py # Posterior entropy tracking
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│ ├── v3_depth_drift.py # Temporal depth signal
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│ ├── v4_triggers.py # Trigger logic development
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│ ├── v5_fusion.py # MAX-fusion framework
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│ ├── v6_rising_edge.py # Rising-edge detection
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│ └── v7_final.py # ★ Production pipeline
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│
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├── notebooks/
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│ └── analysis.ipynb # Experiment analysis + figures
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│
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├── results/
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│ ├── figures/ # High-resolution performance plots
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│ └── summary.txt # Quantified results
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│
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├── paper/ # Technical manuscript
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├── assets/ # Visualizations, GIFs
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└── README.md
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```
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---
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## Reproducibility Guarantees
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This pipeline was built to be trusted.
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```
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✓ Fully causal — no lookahead bias at any stage
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✓ Rolling normalization only — no global statistics that leak future data
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✓ HMM re-fit periodically — no leakage across the evaluation window
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✓ Deterministic seeds — results are exact across runs
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✓ Validated on Google Colab (NVIDIA T4) and Apple Silicon (M4 Pro/Max)
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```
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---
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## Quick Start
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```bash
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# Clone
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git clone https://github.com/prakulhiremath/LOB-Latent-Regimes.git
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cd LOB-Latent-Regimes
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# Install
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pip install -r requirements.txt
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# Run the final pipeline
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python experiments/v7_final.py
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```
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---
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## Scope & Limitations
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Be precise about what this is.
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| This repo **is** | This repo **is not** |
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| A detection framework for latent regime transitions | A trading strategy |
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| An empirical study of LOB microstructure | Optimised for execution latency |
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| A reproducible research pipeline | A production system |
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| A contribution to predictive market microstructure | Financial advice |
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---
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## Contributions
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- **Causal formulation** of the latent build-up → stress transition as a three-state latent process
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- **Temporal drift identification** — subtle depth and spread drift as a leading precursor to liquidity voids
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- **MAX-fusion + rising-edge trigger** — novel detection logic for sub-millisecond microstructure data
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- **Empirical demonstration** of strictly positive lead-time over reactive benchmarks across all evaluated regimes
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---
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## Citation
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```bibtex
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@article{hiremath2026lob,
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title = {Early Detection of Latent Micro-Regimes in Limit Order Books},
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author = {Hiremath, Prakul Sunil and Hiremath, Vruksha Arun},
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year = {2026},
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doi = {10.5281/zenodo.19697687}
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}
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```
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---
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<div align="center">
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Built for **reproducible research** in quantitative finance and machine learning.
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*If the signal fires before the storm — it worked.*
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</div>
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