+ Latent Micro-Regime Early Detection in Limit Order Books +
++ Identify structural market instability + before it surfaces +
++ A reproducible research framework for detecting latent build-up phases in financial markets. Fires with positive lead-time and 100% precision, exploiting the measurable delay between structural deterioration and observable stress. +
+ + + ++ β οΈ Research pipeline. Not a trading strategy. See Scope & Limitations below. +
++ "Market stress does not arrive without warning. It accumulates." +
++ Classical indicatorsβvolatility, order imbalance, spread wideningβare 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 stressβbefore it becomes visible in price or spread? +
++ Markets cycle through three distinct states. The critical regime is invisible to standard microstructure monitors. +
+ +ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ +β β +β STATE 0 βββββββββββββΊ STATE 1 βββββββββββββΊ STATE 2 β +β β +β Stable Latent Build-up Stress β +β βββββββ βββββββββββββββ ββββββ β +β β’ Balanced liquidity β’ Depth eroding β’ Price shock β +β β’ High resilience β’ Spread drifting β’ Visible β +β β’ Equilibrium β’ β Hidden instability β’ Reactive β +β β +β Observable INVISIBLE Observable β +β (past data) (our signal fires) (too late) β +β β +β βββββ detection window βββββΊ β +β β β β +β we detect stress begins β +β β +ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ+
+ The key insight: The transition from State 1 β State 2 is not instantaneous. There is a measurable delay β and within that delay lives our detection window. We exploit it. +
+ ++ Why this matters: Depth erosion precedes spread widening, which precedes price impact. If we detect the book thinning silently, we have time to respond before the visible stress cascade begins. +
++ Three independent signal channels. One fused trigger. Rising-edge detection instead of thresholds. +
+ ++ Uncertainty in regime classification +
++ Why early: Rises as the latent state becomes ambiguous, before the transition to stress solidifies. +
++ Recursive tracking of LOB depth erosion +
++ Why early: Captures slow structural decay invisible to snapshot metrics. Liquid book β thin book is the first visible sign. +
++ Imbalance between informed and uninformed flow +
++ Why early: Signals adverse selection building in the book before visible spread impact. +
+MAX-Fusion Trigger +βββ Rising-edge detection +β ββ Onset of change, not absolute level +β ββ Bypasses noise floor that kills threshold methods +β +βββ Cross-channel aggregation +β ββ Fire when ANY channel breaches threshold +β +βββ Early-detection constraint + ββ Signal must precede stress (Ο < Ο)+
+ Key design choice: We don't ask "is the spread wide?" We ask "is it getting wider right now?" This rising-edge approach bypasses the noise floor that kills absolute-threshold methods on real microstructure data. +
++ Evaluated under full causal guarantees. No lookahead bias. No global statistics leaked to the predictor. +
+ +| Method | +Lead-Time (steps) | +Precision | +Coverage | +
|---|---|---|---|
| β Adaptive Trigger | ++18.6 | +100% | +52.6% | +
| HMM | ++14.9 | +100% | +43.2% | +
| Multi-Trigger | ++13.1 | +100% | +28.1% | +
| Order Imbalance | +β24.8 | +54.9% | +78.7% | +
| Volatility | +β32.0 | +45.5% | +43.3% | +
+ Reading the table: Positive lead-time means the signal fires before stress begins. Baseline methods are strictly negativeβthey lag. 100% precision means zero false starts during the latent phaseβevery trigger is temporally valid. +
++ Market regimes structurally deteriorate before visible deterioration. Consistent empirical signature across tested sessions. +
++ Depth decay in the LOB precedes spread widening and price impact. Book thinning is the earliest signal. +
++ Posterior entropy is a structural stress barometer. Model uncertainty itself is informative. +
++ Onset of deterioration carries more information than magnitude. No false starts from noise. +
++ This pipeline was engineered to be trusted. Every result is verifiable and repeatable. +
+ +β Causal
+No Lookahead
+β Rolling Norm
+No Data Leakage
+β HMM Re-fit
+Periodic
+β Deterministic
+Fixed Seeds
+β Validated
+Colab + M4
++ Causality verification: Rolling normalization onlyβno global statistics computed on future data. HMM re-fit every evaluation window. All results are exact under deterministic seeds across NVIDIA T4 (Google Colab) and Apple Silicon (M4 Pro/Max). +
+git clone https://github.com/prakulhiremath/LOB-Latent-Regimes.git
+cd LOB-Latent-Regimes
+pip install -r requirements.txtpython experiments/v7_final.pyExpected output:
+ +[INFO] Loading normalized LOB snapshots... +[INFO] Running causal evaluation pipeline... +[INFO] Fitting HMM with rolling windows... +[INFO] Computing signal channels (entropy, depth, toxicity)... +[INFO] Fusing triggers with MAX aggregation... +[INFO] Computing lead-time and precision metrics... + +ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ +β Adaptive Trigger +18.6 steps 100% precision 52.6% β +β HMM +14.9 steps 100% precision 43.2% β +β Multi-Trigger +13.1 steps 100% precision 28.1% β +ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ+
jupyter notebook notebooks/analysis.ipynbContains: Signal decomposition, lead-time distributions, regime transition visualizations, baseline comparisons.
+LOB-Latent-Regimes/ +β +βββ experiments/ +β βββ v1_baseline.py # Initial HMM formulation +β βββ v2_entropy.py # Posterior entropy tracking +β βββ v3_depth_drift.py # Temporal depth signal +β βββ v4_triggers.py # Trigger logic development +β βββ v5_fusion.py # MAX-fusion framework +β βββ v6_rising_edge.py # Rising-edge detection +β βββ v7_final.py # β Production pipeline +β +βββ notebooks/ +β βββ analysis.ipynb # Experiment analysis + figures +β +βββ results/ +β βββ figures/ # High-resolution performance plots +β βββ summary.txt # Quantified results +β +βββ paper/ # Technical manuscript +βββ assets/ # Visualizations, GIFs, detection timeline +βββ requirements.txt # Dependencies +βββ README.md+
The pipeline evolved through 7 versions, each adding precision:
+ +| Version | +Focus | +Key Innovation | +
|---|---|---|
v1 |
+ Baseline HMM | +Three-state Markov model for regime classification | +
v2 |
+ Entropy Signal | +Posterior entropy as stress barometer | +
v3 |
+ Depth Drift | +Temporal tracking of LOB depth erosion | +
v4 |
+ Trigger Design | +Individual signal thresholds | +
v5 |
+ Multi-Signal Fusion | +MAX aggregation across channels | +
v6 |
+ Rising-Edge | +Onset detection instead of absolute levels | +
β
v7 |
+ Final Pipeline | +Production configuration, full causal guarantees | +
+ Be precise about what this is and is not. +
+ ++ Important: This is research code. Backtesting results do not guarantee forward performance. Live trading requires rigorous validation, risk management, and regulatory compliance beyond the scope of this work. +
++ Model latent build-up β stress transition as a three-state latent process with explicit temporal separation. +
++ Subtle depth and spread drift as leading precursor to liquidity voids. Recursive tracking without lookahead. +
++ MAX-fusion + rising-edge trigger for sub-millisecond microstructure data. Positive lead-time guarantee. +
++ Strictly positive lead-time over reactive benchmarks across all evaluated regimes. 100% precision. +
+@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}
+}+ LOB Latent Regimes β Early Detection of Structural Market Instability +
++ MIT License Β· Reproducible Research Pipeline Β· Open Source +
++ "If the signal fires before the storm β it worked." +
+