Create Reproducibility Note.md

Notebook/Reproducibility Note.md

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