Decentralized HTLC Token Swapping Enhanced by Predictive Analytics and Generative AI

Abstract

The increasing use of decentralized finance (DeFi) accelerates the demand for trustless, secure mechanisms for cross-chain token exchange. This paper outlines a complete model for atomic token swaps based on the Hashed Timelock Contract (HTLC) scheme, allowing for intermediary-free token exchanges across disparate blockchain systems. The system makes use of the local blockchain simulation framework, Ganache, to design and test cross-chain interactions in a sandbox environment. To improve the decision-making capabilities for users, a real-time cryptocurrency price forecasting subsystem is added which utilizes machine learning models to analyze and predict the market and its volatility. Additionally, the system harnesses Generative AI capabilities through prompt engineering to tailor investment advice for individual users by analyzing the market, their preferred risk level, expected returns, and provide investment strategies aligned with users\' preferences. Apart from sophisticated trading algorithms, the solution also offers a simple dashboard for market price monitoring and performs rapid token swaps at the user\'s command. Smart contracts are implemented using Solidity, token and price feeds are ports to Web3.js, predictive analytics is done in Python, while the frontend and backend are structured in Next.js alongside Node.js. System testing validates hypotheses on the provision of secure cross-chain swaps within one transaction without compromising.

Introduction

Decentralized Finance (DeFi) enables peer-to-peer asset exchanges without intermediaries, but interoperability between heterogeneous blockchains remains a challenge. Centralized exchanges (CEXs) undermine DeFi principles due to custodial control, regulatory constraints, and vulnerability to hacks. This work proposes a trustless cross-chain token swapping mechanism using Hashed Timelock Contracts (HTLCs), ensuring atomic swaps where transactions either execute fully or not at all, eliminating counterparty risk. The system is tested in a safe simulated environment (Ganache) with Solidity smart contracts interfacing via Web3.js.

To enhance usability and decision-making, the platform integrates AI-powered predictive analytics and Generative AI investment advisory, using LSTM networks and Prophet models to forecast cryptocurrency prices and provide personalized, risk-aware guidance. The architecture includes smart contracts, blockchain simulation, frontend (React.js/Next.js), backend (Node.js/Python), and REST APIs for seamless interaction. By combining cross-chain interoperability, atomic swap security, and AI-driven insights, the system aims to make decentralized multi-chain token exchange safer, more efficient, and user-friendly.

Conclusion

The paper presented a method to transfer tokens between blockchains with the help of HTLCs, and it also offered some tools for making predictions and getting investment guidance. The system was built so that people could exchange tokens safely and without relying on any middlemen, even if the blockchains they use are different. Validation through simulation on both the Ethereum and Binance test networks showed that the HTLC system works well by keeping transactions safe, making sure refunds are possible, and protecting funds during different types of swaps. In addition to the main feature that lets you swap cores, adding machine learning to predict prices and using Generative AI for advice made it much easier for people to make smart choices. The LSTM model was 7.8% accurate in its predictions, and the AI-generated investment suggestions were easy to understand and clear, especially helping people who aren’t very tech-savvy. These results show that using decentralized swapping in DeFi along with smart data analysis can actually work well and be useful in real-world applications. Looking ahead, future work will look into connecting more blockchain networks and trying out ways to make transactions faster and easier. Further enhancements will mean making the predictions more accurate by adding other types of data and real-time changes in the market, and also making the AI tool smarter by teaching it to learn from new information. These changes are trying to make the system work better, faster, and more reliably, so it can handle more transactions in real-world situations with decentralized finance.

References

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Copyright

Copyright © 2025 Medha Wyawahare, Milind Rane, Atharva Awate, Amey Bhagwatkar, Akhilesh Mane. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.