CryptoMamba is a novel Mamba-based architecture designed for accurate and efficient time-series forecasting, with a focus on cryptocurrency price prediction. Leveraging the capabilities of SSMs, CryptoMamba excels at capturing long-range dependencies and adapting to highly volatile market conditions.
Repository Includes:
- Implementation of CryptoMamba and baseline models (LSTM, Bi-LSTM, GRU, and S-Mamba).
- Two Trading Algorithms: Vanilla and Smart, for evaluating real-world performance.
- Code for data preprocessing, model training, evaluation metrics, and trading simulations.
CryptoMamba’s robust performance and generalizability make it a promising solution for various sequential forecasting tasks, including financial markets, commodities, and other time-series applications.
To install the requirements you can use:
pip install -r requirements.txt
If you have difficulties installing `mamba_ssm`, please follow the instructions in its GitHub repository.
You can find the processed data that we use in here. If you want to use another data configuration, you should change the configuration in the data config file. Note that the data_path should point to the raw data file with similar format to this.
To train a model, use the following:
python3 scripts/training.py --config CONFIG_NAME
Here, `CONFIG_NAME` is the name of a config file in the training config folder without its extension. For example, to train CryptoMamba with volume you can run the following command:
python3 scripts/training.py --config cmamba_v
To evaluate a model, run the this command:
python scripts/evaluation.py --config CONFIG_NAME --ckpt_path PATH_TO_CHECKPOINT
To run the trading simulation, you can use the following command:
python scripts/simulate_trade.py --config CONFIG_NAME --ckpt_path PATH_TO_CHECKPOINT --split SPLIT_NAME --trade_mode TRADE_ALGORITHM
Where SPLIT_NAME is train, val, or test and TRADE_ALGORITHM is smart or vanilla.
We also have a script to predict the next day price and trade suggestions by providing the prices of its previous days. You have to create a `csv` file similar to [this](data/one_day_trade.csv) and use this command:
python scripts/one_day_pred.py --config CONFIG_NAME --ckpt_path PATH_TO_CHECKPOINT --date DATE
If you don't provide a value for DATE, the code automatically predicts one day after the last day that is in the file.
| Model | RMSE | MAPE | MAE | Parameters |
|---|---|---|---|---|
| LSTM | 2672.7 | 3.609 | 2094.3 | 204K |
| Bi-LSTM | 2325.6 | 3.072 | 1778.8 | 569k |
| GRU | 1892.4 | 2.385 | 1371.2 | 153k |
| S-Mamba | 1717.4 | 2.248 | 1239.9 | 330k |
| CryptoMamba | 1713.0 | 2.171 | 1200.9 | 136k |
| LSTM-v | 2202.1 | 2.896 | 1668.9 | 204K |
| Bi-LSTM-v | 2080.2 | 2.738 | 1562.5 | 569k |
| GRU-v | 1978.0 | 2.526 | 1454.3 | 153k |
| S-Mamba-v | 1651.6 | 2.215 | 1209.7 | 330k |
| CryptoMamba-v | 1598.1 | 2.034 | 1120.7 | 136k |
If you use CryptoMamba in a research paper, please cite our paper:
@article{Sepehri2025CryptoMamba,
title={CryptoMamba: Leveraging State Space Models for Accurate Bitcoin Price Prediction},
author={Mohammad Shahab Sepehri and Asal Mehradfar and Mahdi Soltanolkotabi and Salman Avestimehr},
year={2025},
url={https://arxiv.org/abs/2501.01010}
}If you have any questions, feel free to open a Discussion and ask your question. You can also email [email protected] (Mohammad Shahab Sepehri) or [email protected] (Asal Mehradfar).