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Overview of QuantPilot’s AI Models

QuantPilot integrates state-of-the-art machine learning (ML) and artificial intelligence (AI) technologies to deliver advanced analytics, predictive insights, and risk mitigation tools. These AI models are built on the latest research in deep learning, natural language processing (NLP), and reinforcement learning, ensuring QuantPilot remains at the forefront of DeFAI innovation.

Forward-looking models leverage Structured State Space sequence model (S4/S6)-based architectures for time-dependant data and Temporal Fusion Transformer (TFT) for multivariate on-chain metrics and macro indicators to anticipate market movements:

  • Price Movement Forecasting: By analyzing historical price data, on-chain metrics, and social sentiment, QuantPilot provides probabilistic forecasts to guide downstream models.

  • Yield Strategies: Reinforcement learning algorithms are deployed to simulate nonconvex yield farming strategies across multiple scenarios for identifying the most profitable rebalancing actions while minimizing risk.

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Token Clustering and Recommendation Systems

Unsupervised learning techniques, such as contrastive learning and graph neural networks (GNNs) are employed to identify patterns and relationships within the DeFAI ecosystem. These models enable QuantPilot to:

  • Personalize Token Recommendations: By analyzing user behavior and token attributes, QuantPilot recommends projects or tokens that align with user’s existing portfolio and preferences mentioned in chats.

  • Dynamic ETF Creation: Using clustering algorithms like DBSCAN, QuantPilot groups tokens with appropriately diversified risk-return profiles, enabling the creation of adaptive ETFs that respond to shifting market conditions.

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Risk Mitigation and Security Models

QuantPilot’s AI-driven security models are designed to protect users from malicious activities and scams. These models incorporate anomaly detection and NLP-based sentiment analysis:

  • Rug Pull Detection: Using datasets of historical scams and rug pulls, QuantPilot trains models to identify red flags such as abnormal token distribution, suspicious wallet activity, and misleading project documentation.

  • Smart Contract Auditing: QuantPilot integrates Slither to analyze smart contract code for vulnerabilities, ensuring users interact with secure protocols.

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Model refinement

QuantPilot’s AI models employ incremental retraining to adapt to evolving market conditions. This dynamic refinement process ensures the models stay accurate, reliable, and aligned with the rapidly changing DeFi landscape.