Problem
Crypto markets are fragmented across exchanges with heterogeneous liquidity, latency, fee structures and data quality. A single venue price can be unstable, stale or affected by microstructure noise.
Featured work
Quant Crypto Price Aggregation
Robust aggregation of cryptocurrency prices across fragmented trading venues, with attention to noisy quotes, venue reliability, outlier handling and market microstructure effects.
Approach
This work studies robust estimators, venue weighting, quote filtering and diagnostics for identifying unreliable observations before producing an aggregate reference price.
Technical Focus
- Robust statistics for noisy cross-venue data.
- Outlier detection and stale quote handling.
- Market microstructure-aware reliability metrics.
- Reproducible Python research pipeline.