33 lines
1.3 KiB
Markdown
33 lines
1.3 KiB
Markdown
# Reproducibility Guide
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This repository contains the complete pipeline for generating the synthetic Limit Order Book (LOB) data, detection signals, and evaluation metrics presented in the paper.
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## 💻 Environment & Hardware
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Experiments were conducted across the following environments:
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* **Primary Runtime:** Google Colab (NVIDIA T4 GPU)
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* **Local Testing:** Apple MacBook (M4 Architecture)
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> [!NOTE]
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> All experiments are fully deterministic using specified random seeds. Minor variations may occur due to differences in hardware floating-point precision or library versions.
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## 🛠 Prerequisites
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Ensure you have **Python 3.x** installed. The core logic relies on the following stack:
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* `NumPy` & `SciPy` (Numerical processing)
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* `scikit-learn` (Evaluation metrics)
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* `hmmlearn` (Hidden Markov Modeling)
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## 🚀 Quick Start
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To reproduce the paper's results, figures, and tables:
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1. **Install Dependencies:**
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```bash
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pip install -r requirements.txt
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```
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2. **Execute Pipeline:**
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Run the notebook cells sequentially. The pipeline is designed to be executed end-to-end to generate:
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* **Synthetic LOB Data:** Simulated market depth and flow.
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* **Detection Signals:** Primary output of the proposed model.
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* **Metrics & Figures:** All visualizations and tables used in the final publication.
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---
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