diff --git a/README.md b/README.md index a0cd0c1..dc49b55 100644 --- a/README.md +++ b/README.md @@ -1,143 +1,242 @@ -# ⚡️ Latent Micro-Regime Early Detection in Limit Order Books +
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- Detection Timeline Visualization -

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+``` +╔══════════════════════════════════════════════════════════════════╗ +║ LATENT MICRO-REGIME EARLY DETECTION IN LIMIT ORDER BOOKS ║ +║ Identifying structural market instability before it surfaces ║ +╚══════════════════════════════════════════════════════════════════╝ +``` + +[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.19697687.svg)](https://doi.org/10.5281/zenodo.19697687) +  +![Method](https://img.shields.io/badge/Method-Trigger%20Based-0078D4?style=flat-square) +![Model](https://img.shields.io/badge/Model-HMM-8A2BE2?style=flat-square) +![Precision](https://img.shields.io/badge/Precision-100%25-D4AF37?style=flat-square) +![Lead‑Time](https://img.shields.io/badge/Lead--Time-Strictly%20Positive-28A745?style=flat-square) +![License](https://img.shields.io/badge/License-MIT-yellow?style=flat-square) + +
+ +**Prakul Sunil Hiremath · Vruksha Arun Hiremath** + +[📄 Paper (coming soon)](#)  ·  [📦 Code DOI](https://doi.org/10.5281/zenodo.19697687)  ·  [💻 Repository](https://github.com/prakulhiremath/LOB-Latent-Regimes) + +
+ +Detection Timeline — Latent regime transitions identified before observable stress + +
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--- -## 🔬 Overview +## The Core Idea -This research investigates whether **latent microstructure dynamics** in Limit Order Books (LOB) can be mathematically identified *before* observable liquidity stress manifests. +> **Market stress does not arrive without warning. It *accumulates*.** -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. +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**. --- -## 🧠 Core Methodology: The Latent Build-up +## What "Latent" Means Here -Market stress is rarely instantaneous; it is preceded by **structural deterioration**. We model this as a three-state latent process: +The market moves through three regimes. The critical one is invisible to standard monitors: -| 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 | +``` +┌─────────────────────────────────────────────────────────────────┐ +│ │ +│ 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 │ +│ │ +└─────────────────────────────────────────────────────────────────┘ +``` -> **Key Discovery:** A delayed transition from **State 1 → State 2** creates a deterministic prediction window, allowing for early detection with strictly positive lead-time. +**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. --- -## 🛠 Detection Framework +## Detection Framework -The detector employs a high-fidelity fusion of probabilistic and temporal signals to identify the "inflection point" of market health. +Three independent signal channels. One fused trigger. -### 📡 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. +### Signal Channels -### 🕹 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. +| 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. --- -## 📊 Results & Performance +## Results -| 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% | +``` +╔════════════════════════════════════════════════════════════════╗ +║ 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% ║ +╚════════════════════════════════════════════════════════════════╝ +``` -### 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. +**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). --- -## 📈 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. +## 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. --- -## 📂 Repository Structure +## Repository Structure -```bash -. -├── experiments/ # Iterative development v1 → v7 -├── notebooks/ # Production-grade experiment analysis +``` +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 summary -├── paper/ # Technical manuscript (PDF) -├── assets/ # Visualizations and GIFs +│ ├── figures/ # High-resolution performance plots +│ └── summary.txt # Quantified results +│ +├── paper/ # Technical manuscript +├── assets/ # Visualizations, GIFs └── README.md ``` --- -## 🚀 Reproducibility +## Reproducibility Guarantees -Validated across high-compute and local environments: -* **Cloud:** Google Colab (NVIDIA T4) -* **Local:** Apple Silicon (M4 Pro/Max) +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) +``` + +--- + +## Quick Start -### Quick Start ```bash -# Clone the repository -git clone https://github.com/your-repo/lob-early-detection.git +# Clone +git clone https://github.com/prakulhiremath/LOB-Latent-Regimes.git +cd LOB-Latent-Regimes -# Install dependencies +# Install pip install -r requirements.txt -# Execute the final pipeline +# Run 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. +## 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 | --- -## 📑 Citation +## 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 + +--- + +## 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}, +@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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