Coin Quest: A Time-Series Database Architecture for Modular Risk Quantification in Cryptocurrency Portfolio Tracking

The pervasive volatility and structural complexity of decentralized assets present significant challenges for modern portfolio management. This paper introduces Coin Quest, a novel, high-fidelity cryptocurrency tracking and risk management platform designed to address critical shortcomings in existing market solutions, notably high data latency and the deficiency of robust quantitative risk tools. Our technical proposal mandates a resilient microservices architecture centered on Apache Kafka for high-throughput, low-latency data stream ingestion, ensuring real-time portfolio valuation across disparate exchanges and blockchains. The analytical core of Coin Quest implements the Monte Carlo Simulation (MCS) framework to compute Value at Risk (VaR) and the superior measure, Conditional Value at Risk (CVaR), recognizing the non-normal return distributions inherent to crypto assets. Furthermore, we detail specialized algorithms necessary for comprehensive tracking and valuation of complex Decentralized Finance (DeFi) positions, including the calculation of Impermanent Loss, and quantitative monitoring of NonFungible Tokens (NFTs) using floor price metrics. We conclude by outlining empirical validation requirements demonstrating the system’s capacity to maintain sub-100ms data latency and confirming the superior predictive accuracy of the MCS-based risk model against traditional historical simulations in highly volatile market environments.