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This document summarizes the findings and methodology of the LOB Micro-Regime Early Detection project, detailing the model, evaluation metrics, results, key findings, and reproducibility aspects.
73 lines
1.9 KiB
Plaintext
73 lines
1.9 KiB
Plaintext
LOB Micro-Regime Early Detection — Summary
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Overview
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--------
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This project studies early detection of latent instability in limit order books (LOB) before observable liquidity stress.
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A causal three-regime model is used:
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- Regime 0: Stable
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- Regime 1: Latent build-up (hidden deterioration)
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- Regime 2: Stress (observable disruption)
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The transition from build-up to stress occurs with a delay, enabling evaluation of early detection.
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---
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Method
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------
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The proposed detector combines:
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- Probabilistic instability (HMM posterior entropy)
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- Temporal drift (spread, depth dynamics)
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- Structural signals (depth erosion, order flow)
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Final detection uses:
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- MAX-trigger fusion across channels
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- Rising-edge detection (onset of instability)
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- Early-detection constraint (ensuring signals precede stress)
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---
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Evaluation Metrics
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------------------
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- Lead-time: Δ = σ − τ (positive = early detection)
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- Precision: fraction of detections occurring before stress
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- Coverage: fraction of stress events detected early
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---
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Results
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-------
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Adaptive Trigger (final method):
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- Mean Δ (lead-time): +18.62 steps
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- Precision: 100%
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- Coverage: 52.6%
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Other variants:
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- Model: Δ = +14.95 | Precision = 100% | Coverage = 43.2%
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- Multi-Trigger: Δ = +13.15 | Precision = 100% | Coverage = 28.1%
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Baselines:
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- Imbalance: Δ = -24.84 | Precision = 54.9% | Coverage = 78.7%
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- Volatility: Δ = -32.02 | Precision = 45.5% | Coverage = 43.3%
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---
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Key Findings
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------------
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- Latent instability signals exist prior to observable stress.
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- Standard baselines are reactive (negative lead-time).
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- Trigger-based detection enables strictly positive lead-time.
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- A trade-off exists between early detection and coverage.
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- Depth erosion and entropy are the dominant early indicators.
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---
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Reproducibility
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---------------
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- Implemented in Python (NumPy, scikit-learn, hmmlearn)
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- Tested on:
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- Google Colab (NVIDIA T4)
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- Apple MacBook (M4)
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All figures and results are generated from the provided pipeline.
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