The paper discusses the development of an Automated Bitcoin Trading decentralized application (dApp) that utilizes price predictions generated from advanced deep learning models, specifically Random Forest (RF), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM).
The model achieved an impressive 488.74% return on investment (ROI), significantly outperforming buy and hold strategy while ensuring transparency and automation throughout the trading process.
You can view the publication here.
The model is deployed to Replicate using the Cog framework, allowing for efficient replication and execution.
Firebase functions are utilized to trigger the model, facilitate on-chain trading, and store results in Firestore.
The RF, LSTM, and Bi-LSTM models are trained using the data provided in the data folder. The trained models are saved in the model folder for easy access and deployment.
This repository includes the landing page associated with the paper.