SAMBA: A Graph-Mamba Approach for Stock Price Prediction
Paper | Dataset

📖 About

This repository contains the modular implementation of SAMBA, a novel architecture that combines State-space Mamba models with Graph Neural Networks for stock price prediction. This work is based on the paper "Mamba Meets Financial Markets: A Graph-Mamba Approach for Stock Price Prediction" accepted for publication in IEEE ICASSP 2025.

🔗 Original Paper Repository: https://github.com/Ali-Meh619/SAMBA

🎯 Overview

SAMBA (State-space Mamba with Graph Neural Networks) is designed for stock price prediction using real-world financial market data. The model leverages:

• 🧠 Mamba blocks for efficient sequence modeling with selective state spaces
• 🕸️ Graph Neural Networks with Chebyshev polynomials for spatial relationships
• 📊 Gaussian kernel-based adjacency matrices for adaptive graph learning
• ⚡ Bidirectional processing for enhanced temporal understanding

🏗️ Architecture

The model consists of several key components:

1. 🧠 Mamba Backbone: Processes temporal sequences using selective state space models
2. 🕸️ Graph Convolution Layers: Capture spatial dependencies using Chebyshev polynomials
3. 📊 Adaptive Adjacency Matrix: Learns graph structure using Gaussian kernels
4. 🔗 Residual Connections: Enable deep network training with skip connections

🚀 Installation

1. Clone the repository:

git clone <repository-url>
cd samba-stock-prediction

2. Install dependencies:

pip install -r requirements.txt

3. Ensure you have a CUDA-compatible GPU for optimal performance. 🎮

💻 Usage

⚡ Quick Start

1. 📦 Install dependencies:

   pip install -r requirements.txt

2. 📁 Create Dataset folder and add your CSV files:

   mkdir Dataset
   # Keep the SAMBA feature builder and copy your CSV files to the Dataset folder:
   # - samba_feature_builder.py
   # - combined_dataframe_IXIC.csv
   # - combined_dataframe_NYSE.csv  
   # - combined_dataframe_DJI.csv

3. 🚀 Run the model:

   python main.py

🎯 Basic Training

Run the model:

from main import main

# Run training with paper configuration
main()

⚙️ Custom Configuration

You can modify the training configuration in main.py or paper_config.py:

# In main.py, the configuration is loaded from paper_config.py
model_args, config = get_paper_config()

# You can modify the config before training
config.epochs = 500  # Reduce epochs for faster training
config.batch_size = 64  # Increase batch size

📊 Data Format

⚠️ Important: Create a Dataset folder and place your CSV files in it.

The model expects SAMBA combined CSV data with the following format:

• 📅 Date column as index
• 🏷️ Name column (will be removed during preprocessing)
• 💰 Price column for target values
• 📈 82 feature columns containing target-market technical features and shared market variables

Example:

Date,Price,Vol.,weekday,mom,mom1,...,DE6
2010-01-04,2308.42,-0.14709,0,-0.000126,...,1.23
2010-01-05,2308.71,0.034934,1,0.003311,...,1.12

💡 Note: The num_nodes parameter is automatically determined from the input data shape (number of features), so you don't need to specify it manually.

SAMBA Feature Builder

Dataset/samba_feature_builder.py builds SAMBA-compatible combined feature files when the required raw source series are available. It computes the target-market technical features, merges shared external variables, and derives the SAMBA term-spread and default-spread features.

Use legacy mode to match the published SAMBA dataset convention:

python Dataset/samba_feature_builder.py \
  --target-csv raw_ixic.csv \
  --target-name IXIC \
  --external-csv raw_external_features.csv \
  --external-mode raw \
  --external-alignment reverse-position \
  --mode legacy \
  --output Dataset/combined_dataframe_IXIC.csv

Use causal mode for chronological feature engineering on new datasets:

python Dataset/samba_feature_builder.py \
  --target-csv raw_ixic.csv \
  --target-name IXIC \
  --external-csv raw_external_features.csv \
  --external-mode raw \
  --external-alignment date \
  --mode causal \
  --output Dataset/combined_dataframe_IXIC.csv

Exact reproduction of the published combined CSVs requires the same raw source series, vendor calendars, missing values, historical snapshots, and rounding used to create those files.

📈 Available Datasets

This repository is configured to work with three real-world datasets from the US stock market with 82 daily stock features:

📁 Folder Structure:

Dataset/
├── samba_feature_builder.py       # SAMBA feature construction utility
├── combined_dataframe_IXIC.csv    # 📊 NASDAQ Composite Index
├── combined_dataframe_NYSE.csv    # 🏛️ New York Stock Exchange
└── combined_dataframe_DJI.csv     # 📈 Dow Jones Industrial Average

📅 Dataset Period: January 2010 to November 2023
🔢 Features: 82 daily stock features including technical indicators, market data, and financial metrics

Each dataset contains comprehensive historical price data with multiple technical indicators as features, providing rich information for the Graph-Mamba model to learn complex market patterns.

🧩 Model Components

🔧 Core Modules

• 📋 config/: Configuration classes for model and training parameters
• 🧠 models/: Model implementations (SAMBA, Mamba, Graph layers)
• 🛠️ utils/: Utility functions (data loading, metrics, logging)
• 🏃 trainer/: Training loop and evaluation

🎯 Key Classes

• 🚀 SAMBA: Main model combining Mamba and GNN
• 🧠 Mamba: State-space sequence model
• 🔗 MambaBlock: Individual Mamba block with selective scanning
• 🕸️ gconv: Graph convolution with Chebyshev polynomials
• 🏃 Trainer: Training and evaluation pipeline

📊 Metrics

The model evaluates performance using:

• 📏 MAE: Mean Absolute Error
• 📐 RMSE: Root Mean Squared Error
• 🔗 IC: Information Coefficient (Pearson correlation)
• 📈 RIC: Rank Information Coefficient (Spearman correlation)

⚙️ Configuration

🧠 Model Parameters

• 🔢 d_model: Model dimension
• 📚 n_layer: Number of Mamba layers
• 🎯 vocab_size: Number of features (automatically determined from input data)
• 📥 seq_in: Input sequence length
• 📤 seq_out: Output sequence length
• 🏠 d_state: State dimension
• 📈 expand: Expansion factor
• 🧮 cheb_k: Chebyshev polynomial order

🏃 Training Parameters

• 🔄 epochs: Number of training epochs
• 📚 lr_init: Initial learning rate
• 📦 batch_size: Training batch size
• ⏹️ early_stop: Enable early stopping
• ⏰ early_stop_patience: Early stopping patience

📈 Results

The model outputs results to:

• 📄 samba_results.txt: Performance metrics
• 💾 ./best_model.pth: Best model checkpoint
• 📺 Console logs: Training progress and final metrics

📁 File Structure

├── 📂 Dataset/                # SAMBA datasets and feature builder
│   ├── samba_feature_builder.py
│   ├── 📊 combined_dataframe_IXIC.csv
│   ├── 🏛️ combined_dataframe_NYSE.csv
│   └── 📈 combined_dataframe_DJI.csv
├── 📂 config/
│   ├── __init__.py
│   └── model_config.py
├── 📂 models/
│   ├── __init__.py
│   ├── 🚀 samba.py
│   ├── 🧠 mamba.py
│   ├── 🔗 mamba_block.py
│   ├── 🕸️ graph_layers.py
│   └── 📏 normalization.py
├── 📂 utils/
│   ├── __init__.py
│   ├── 📊 data_utils.py
│   ├── 📈 metrics.py
│   ├── 📝 logger.py
│   └── 🛠️ model_utils.py
├── 📂 trainer/
│   ├── __init__.py
│   └── 🏃 trainer.py
├── 🚀 main.py                 # Main execution file
├── 📋 paper_config.py         # Paper-specific configuration
├── 🧪 test_system.py          # System test
├── 📦 requirements.txt
└── 📖 README.md

📦 Dependencies

• 🔥 PyTorch >= 1.9.0
• 🔢 NumPy >= 1.21.0
• 🐼 Pandas >= 1.3.0
• 📊 Matplotlib >= 3.4.0
• 🧮 einops >= 0.4.0
• 💾 h5py >= 3.1.0

📚 Citation

If you find our paper and code useful, please kindly cite our paper as follows:

@inproceedings{SAMBA,
  title={Mamba Meets Financial Markets: {A} {G}raph-{M}amba Approach for Stock Price Prediction},
  author={Mehrabian, Ali and Hoseinzade, Ehsan and Mazloum, Mahdi and Chen, Xiaohong},
  booktitle={Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP)},
address={Hyderabad, India},
  month={Apr.},
  year={2025}
}

📄 Paper: "Mamba Meets Financial Markets: A Graph-Mamba Approach for Stock Price Prediction"
🏛️ Conference: IEEE ICASSP 2025
👥 Authors: Ali Mehrabian, Ehsan Hoseinzade, Mahdi Mazloum, Xiaohong Chen

📞 Contact

Please feel free to contact us if you have any questions:

• 👨‍💻 Ali Mehrabian: alimehrabian619@yahoo.com, ali.mehrabian@vectorinstitute.ai
• 🔗 Original Repository: https://github.com/Ali-Meh619/SAMBA

🤝 Contributing

1. 🍴 Fork the repository
2. 🌿 Create a feature branch
3. ✏️ Make your changes
4. 🧪 Add tests if applicable
5. 📤 Submit a pull request

🐛 Issues

If you encounter any issues, please:

1. 🔍 Check the existing issues
2. 📝 Create a new issue with detailed description
3. 💻 Include system information and error logs

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