FinQuant AI

Solutions

To overcome the challenges, Hola Tech adhered to the best practices. Key components of the solution included:

– Implement a multi-stage data validation pipeline: Use AWS Lambda functions to create stringent validation gates immediately after data ingestion. This involves cross-referencing on-chain data pulled from The Graph and Infura against traditional off-chain price feeds. Furthermore, apply fairness assessment techniques during the model training phase to mitigate known historical biases in the traditional financial data.

– Adopt a hybrid data polling strategy: Utilize the low-latency capabilities of a high-speed database (Mongo Atlas) for caching critical, frequently-requested data points (like asset prices and liquidity pool depths) with a very short Time-to-Live (TTL). Reserve The Graph for deep, historical, or complex relational queries, while ensuring NodeJS services actively push immediate, confirmed block data directly from Infura into the caching layer to feed time-sensitive AI models.

– Integrate modular and interpretable AI components: Do not rely solely on opaque deep learning models. Design the investment strategy pipeline to use modular components, where risk analysis is handled by more interpretable machine learning (IML) models (like decision trees or linear models). Use specialized XAI libraries in the NodeJS backend to generate audit trails, providing clear data provenance that traces every AI decision back to the specific validated inputs from both The Graph and traditional data sources.